Why do housing and parking cost more in urban than in rural areas, but road access doesn't? In city centres, land is more expensive than in suburban or rural areas, as land is scarce.  Consequentially, housing and parking in cities cost more.  However, the same is not true for using the road (such as driving).  Indeed, if two people are driving into a city (in one car) to run an errand, it will usually be cheaper for one person to drive around in circles while the other runs the errand, than it is to park the car and run the errand together, even in cities with a congestion charge.

What is the origin of this discrepancy in pricing?  Is it merely because we haven't implemented road pricing for technical and political reasons, or are there economic reasons that there is no pricing on roads?

Land values tend to increase with population density, because it is typically possible to put the land to more productive use.  A store operating in a dense urban core will have more potential customers, more access to public infrastructure (e.g. electricity, communications networks, those roads you mention, public transportation that makes it easier for more customers to visit the store, etc.) and any number of other external factors that contribute to such land being more desirable.  Since more high-wage jobs also tend to be available in such places, there is more access to desirable public and private places (e.g. museums, theaters, upscale businesses) people are also generally willing to pay more to live in such places.

Roads, on the other hand, are generally built and paid for by the government.  The government almost never collects the full use value for the things they make, and so roads are drastically underpriced, in terms of the charge consumers must pay (if there is any such use fee at all.)


Economic Reasoning for Structural change vs Cyclical change Often when there is an episode of low output growth, a debate takes place: Whether the downturn/recession is due to structural factors or is it cyclical.

I understand that the analysis is largely based on statistical estimation wherein it is checked whether there is decline in trend growth or it's the beginning of trough in business cycles.

How do I understand this in terms of pure economics? Broadly I think answer lies in understanding causes of business cycles. But without statistical estimation, how can one argue that a recession is cyclical or due to structural changes?

It is not possible ex-ante, it's all about:

economic interpretations, i.e., economic schools of thought
expectations on central banks' next moves,
political/ideological views,
the (hidden and/or unconscious) emerging willingnesses to induce self-fulfilling prophecies

... hence the debates that take place here and there.

Whether a recession is cyclical or due to structural changes can only be assessed ex-post, i.e. with data and (well driven) studies.


Is wealth created when money is transferred from a wealthy country to a less wealthy country? Imagine a person based in India is working for a client in the USA. The client pays the person 100 USD. The currency is converted into INR, which turns out to be 100 * 74 = 7400 INR.
But if we convert the currency according to Purchasing Power Parity rate, then it should have been 100 * 21.21 = 2121 INR.
So, the USA man is spending money which could have bought him 2121 INR worth of goods and services, but the Indian man is receiving money which can buy him 7400 INR worth of goods and services. So does that mean that 7400 - 2121 = 5279 INR worth of purchasing power is created out of thin air?

No purchasing power is not created from thin air. When you convert money through PPP (expressed usually in terms of local currency to USD) you are getting information about what is the purchasing power of US dollar that would get you the same basket of goods under the law of one price. This is not the same as making currency conversion with exchange rate which generally tells you not much about what would be the actual purchasing power of different currency.
Moreover, it is generally agreed by international trade economists that one of the major reasons why you will find discrepancies between exchange rate and purchasing power parity is the Balassa-Samuelson effect (See Krugman et al. International Trade Theory and Policy). This is because according to Balassa-Samuelson theory the price level in a country depends on productivity mainly in the tradable sector (mostly consisting of goods) because tradable and non-tradable sector (mostly consisting of services) compete for the same workers. This in turn means that if productivity in tradable sector increases it will increase wages not just in tradable sector but also in non-tradable sector.
This is the reason why hairdresser with exactly the same level of skill will get much higher wage in US than in India. However, this in turn leads to violation of law of one price as then also prices in non-tradable sector will be generally higher in US (e.g. haircut in US will be much more expensive than haircut in India).
Beside Balassa-Samuelsson effect, divergence between PPP and market exchange rate can be also due to transaction costs (which lead to failure of one price), differentiated goods (beef produced under strict EU health regulations is not the same beef as beef produced in some developing economy), fixed investment costs  that can also lead to deviations of law of one price and various composition issues when constructing price indecies (see Marrewijk International Economics: theory application and policy 2nd ed pp 471).
As a consequence of this there you will observe divergence between PPP and market exchange rate, but that is not the same as saying that some purchasing power was created out of nothing as they measure different things.


A feasible rational payoff that is not an equilibrium payoff in the repeated game The textbook I am currently reading claims that, in an infinitely-repeated game with discount, there might be a payoff vector which is feasible and individually-rational, but it is not an equilibrium payoff vector in the repeated game. The example is the following basic game for three players:

     L       R
T  0,2,5    0,0,0
M  0,1,0    2,0,5
B  1,1,0    1,1,0

The third player is a dummy player with only one possible action.

In this game:

The minimax values of the players are 1,1,0.
The payoff vector 1,1,5 is both individually-rational and feasible (e.g. by mixing TL and MR with equal frequencies).

The book claims that the only payoff vector in equilibrium is 1,1,0! Why?

Let $E$ be some equilibrium in the repeated game. The payoffs of the row and column players in $E$ must be 1, because:

They must be at least 1 because these are the minimax values;
They must be at most 1 because the sum of utilities of these players in every outcome is at most 2.

In the TR and ML cells, the sum of utilities of the row and column players is less than 2; hence, these cells are not played in equilibrium at all.

So in equilibrium, the only cells that may be played with positive frequency are: TL, MR, BL, BR.

Now, the authors claim that TL and MR are also not played at all in equilibrium. From this they conclude that the payoff of the dummy player is 0. I didn't understand this part. Is it true that TL and MR are never played in equilibrium? Why?

It is true because of the discounting. If the discount parameter were $\delta = 1$ then players 1 and 2 could alternate playing TL and MR in equilibrium and then reach the average payoff vector $1,1,5$. But if $\delta < 1$ someone will not play along. Suppose the players are supposed to start with TL. Then MR would follow in the next round then, then TL again, and so on. In this case
$$
U_1 = 0 + 2 \cdot \delta + 0 \cdot \delta^2 + 2 \cdot \delta^3 + ...
$$
Yet by always playing B player 1 would achieve the payoff
$$
U_1' = 1 + 1 \cdot \delta + 1 \cdot \delta^2 + 1 \cdot \delta^3 + ...
$$
which is larger then $U_1$. Similarly player 2 would not want to start with MR. So these are not equilibrium playoffs. (Not even Aumann's correlated-equilibrium would do the trick.)


Currency Devaluation - As a Means of Paying Off Debt From https://www.sapling.com/8117368/debt-handled-currency-devaluation:

  Currency devaluation will not only affect consumer debts, but it will affect how a country pays back its national debt. If a loan is denominated in the devalued currency, then the debt will be easier to pay off, as the country will have to spend less money paying back foreign investors.

The above quote doesn't make sense to me. Why would the country have to spend less money paying back foreign investors?

Say that at time $0$, $1$£ is worth $1$€. Indeed, like any good or service, a unit of £ can be seen as having a price in € (and vice versa).

At time $0$, Alice lends $10$£ to Peter. Assume that Peter can actually influence the exchange rate between £ and €.

At time $1$, Peter influences the value of $1$£ expressed in €, such that $1$£ is now worth $0.5$€. Peter then decides to reimburse Alice the amount of $10$£. The point is that this amount of $10$£ is only worth $5$ once converted back into €.

Alice, the lender, has lost $5$€ between time $0$ and time $1$. And for her, it was a bad operation given that she lives in a country wherein everything is priced in €.

For Peter, it may have been a good operation over the short term, especially if he does not need to import goods and services from €-based countries, indeed €-priced goods cost henceforth twice as much than at time $0$, since he would now need $2$£ to get $1$€, etc...

That being explained, I do not think that Alice is willing to lend Peter money anymore, at least not in a currency that Peter can influence... She has learnt the lesson and, in the future, she will always write debt contracts which amount will be explictly expressed in the currency that matters most to her, i.e. in €.


Why is the Argentine Peso still so expensive? In the last twelve months, the argentine peso (ARS) lost about 11% of its value in comparison to USD.

In the same time range the prices rouse about 29% (spanish).

What's the reason for this effect? I am living in Argentina for about 2 years, and the prices even feel worse to the published statistics. The government is acting very "special": it's starting social programs one after another and is building lots of (unneeded, or at least oversized) infrastructure using money with unclear origin (well, a lot of this money is draining in similar circumstances, too).

So, who is willing to pay this price for Pesos, since they do not have any value outside of argentina? In my expectation the demand for Pesos is really low, and since Argentina maintains a trade deficit, it is forced to import lots of goods, payed with Dollars. They should have a pressing demand for Dollars. And economics say: low demand + great offer = low price.

Buying and selling of currencies in Argentina is currently strongly controlled by the government:
http://www.reuters.com/article/2015/02/09/argentina-imports-idUSL5N0VJ4I520150209

This means that you cannot buy as many Dollars as you want, but the ones you buy you get at a government controlled exchange rate, which is actually cheaper. The Argentine government might have to pay 10 Pesos for a Dollar, but they sell it to the importer for 8 Pesos. They do this so that consumer prices do not spiral out of control. Hence the current exchange rate is not what it would be in an uncontrolled market. Further devaluation (controlled by the government) is expected next year, because this subsidized exchange is too costly for the government:
http://www.bloomberg.com/news/articles/2015-03-18/argentine-peso-forwards-at-record-show-devaluation-bet-for-2016

As for your second question: while the Peso may not have any value outside of Argentina, individuals and firms that buy products in Argentina will still have a use for it. For example a US firm importing and selling Argentine beef will trade Dollars for Pesos.


Can Quasi-Linear functions be monotonic? I want to ask that Can Quasi-Linear utility functions satisfy the more is better (monotonicity) assumption?

Yes, a trivial example is the following utility:
$$u(x,y) = x+ \sqrt{y}$$
which is both quasi linear and monotonic.


A utility function that gives a regressive tax rate I see some discussion that the reasoning for the construction of a tax rate function is the utility function. Whould $u=\ln\ln w$ function where $u$ is the utility and $w$ the wealth give a regressive tax rate?

I don't see how the log-log utility function you specify would justify a regressive tax rate. In a simple setup, where government engaged in lump sum spending that had to be financed by a consumption or labor income tax, the log-log utility function would have all the same properties as more typical linear, quadratic, CRRA, and CARA utility functions. quasi-concave, increasing, weakly declining marginal utility, etc.

Usually the motivation for a regressive tax rate isn't the utility function over wealth. It is some other feature of the model, like taxes on wealth discouraging capital formation and therefore harming the productivity of poor households by making them less productive (Judd (1985)). Or some feature of the production function is tied to the income of the rich or high productivity specifically, consider this new paper Jones (2018):

  This paper considers the taxation of top incomes when the following
  conditions apply: (i) new ideas drive economic growth, (ii) the reward
  for creating a successful innovation is a top income, and (iii)
  innovation cannot be perfectly targeted by a separate research subsidy
  — think about the business methods of Walmart, the creation of Uber,
  or the “idea” of Amazon.com. These conditions lead to a new term in
  the Saez (2001) formula for the optimal top tax rate: by slowing the
  creation of the new ideas that drive aggregate GDP, top income
  taxation reduces everyone’s income, not just the income at the top.
  When the creation of ideas is the ultimate source of economic growth,
  this force sharply constrains both revenue-maximizing and
  welfare-maximizing top tax rates. For example, for extreme parameter
  values, maximizing the welfare of the middle class requires a
  negative top tax rate: the higher income that results from the subsidy
  to innovation more than makes up for the lost redistribution. More
  generally, the calibrated model suggests that incorporating ideas and
  economic growth cuts the optimal top marginal tax rate substantially
  relative to the basic Saez calculation

Source: Taxing Top Incomes in a World of Ideas


What happens to undated government stocks when interest rates dip below the coupon rate? What happens to undated government stocks (debt— known as "bonds" in the US) which pay a coupon and have an embedded call option, when interest rates dip below the coupon rate? Will the market value of the bond exceed its par value, or is the risk that the bond will be redeemed at any time liable to depress the price?

This is a pretty standard bond pricing issue. The short answer is yes, the market value of the bond can and often will exceed its par value if interest rates are below the coupon rate, just so long as the call option is structured such that the government must pay any interest that has accrued between the last coupon date and the time that the call option is exercised. However, the extent to which the bond will pay more than its face is a question of how the call option is structured: whether it can be called at any time, or whether it can only be called on coupon dates.

Consider an example where the government has issued a \$100 bond paying 4% per period and (for whatever reason) chooses not to exercise the option immediately, the prevailing interest rate is 3% per period, and the coupon rate is 4% per period:

Period 1: \$4 payment, discounted present value is \$3.88
Period 2: \$4 payment, DPV is \$3.77
Period 3: \$4 payment, DPV is \$3.66
Period 4: \$4 payment, call option exercised, \$100 principal returned, DPV is \$92.40

Total value: \$103.72 (difference due to rounding).

Now consider the case where the bond is outstanding, rates drop to 3%, and the bond is immediately called— the bond pays out \$100, plus whatever interest has accrued.

US government bonds are discretely callable— they can only be called on the coupon date, as in the first example. So in the case you give they'll generally trade above par at any date between coupon payments. UK bonds can be called at any time, so there's a lot more uncertainty— it's possible that you could buy the bond today and have it called immediately. However, given that there's little reinvestment risk and that in the event that they're called they will pay out the face value plus accrued interest, the lower bound of their value should be right around par. Thus, to the extent that there's uncertainty as to whether and when the call option will be exercised, it's reasonable to expect that they will trade strictly above par.

Interestingly, as the interest rate drops and the spread between a callable bond and market rates becomes larger, the likelihood that the bond will be called increases, so the value of the bond continues to increase, but at a decreasing rate. This is known as negative convexity. It's a common feature in US mortgage markets, because most borrowers in US mortgage markets (unlike mortgage markets in many other countries) have an implicit option to prepay at any time without penalty. As a result, mortgages and mortgage-backed securities display significant negative convexity, making hedging interest rate risk a big deal for mortgage lenders. For this reason, your question is actually closely related to the question, "Why do lenders dislike early loan repayments?".


Derive the market demand function and market supply function In the second page of this paper Gjerstad et al derive the market demand and the supply for assets using the data shown in Table 1.
The table is
The demand function they find is $Q = 94 – 0.4 P$ and the supply function is $Q = P/15 – 1$
I cannot understand how the functional form they find relates to the data in the table: can someone help me understand how do they derived it?

Hidden on the third page (left column, middle) you can find that the markets they set up experimentally comprised 8 buyers and 4 sellers. If you multiply the individual demand and supply values from Table 1 by the corresponding number of individuals, you get the linearly approximated demand and supply functions stated on the second page.


What are the contents of "Recursive Macroeconomic Theory" I am trying to self-study macroeconomics. I just read parts of "Advanced Macroeconomics" by Romer, and found it a good, intuitive introduction to more advanced models (I have also read a more basic book on ISLM/Solow models and such).

Now I am being told the next step is Recursive Macroeconomic Theory. What are the contents of this book, i.e. what useful topics and methods does one learn that are not present in the Romer book?

Also, I am considering reading either RMT, or maybe something on Business cycles and the short run, which I haven't really read much of, the focus so far in the books has mainly been long-run economic growth... What is recommended to read first?

I guess you mean some edition of Ljungqvist, L., & Sargent, T. J. Recursive macroeconomic theory. MIT press.

This is a very valuable book, but not for acquiring a concrete theoretical knowledge. Copying from its marketing-speak,

  Only experience in solving practical problems fully conveys the power
  of the recursive approach, and the book provides many applications.

Now really, it provides only applications. I do not mean empirical-econometric applications, but presentation of dozens of theoretical models from very different sub-fields and their workings under the Recursive Approach. Mind you, the models themselves are summarily presented, with the implicit understanding that the reader already is familiar with, or will go and study the original papers first. Moreover, a lot of theoretical/mathematical underlying knowledge is taken for granted.


Reference Request on consumer theory I am looking for a recent literature review of consumer demand theory and its empirical applications. I have tried common places (google scholar, jel and jep papers) but no success. Any help is really appreciated.

There is a very nice survey by Aviv Nevo on Empirical Models of Consumer Behavior. He surveys some of the models used in the literature. In particular, he discusses two commonly used demand systems: multi-stage budgeting approaches and discrete choice models.

We may also find interesting Recent Developments in Empirical IO: Dynamic Demand and Dynamic Games by Victor Aguirregabiria and Aviv Nevo.


Uniform price vs discriminatory price double auction Uniform price and discriminatory price double auctions are the most commonly used auction mechanisms in the trading markets especially in peer-to-peer energy trading markets. However, I could not find which one is the better option in this particular domain. Can someone provide a comparative analysis of both auctions mechanisms?

I guess the main reason why you could not find an answer is that economists do not know, see, for instance this chapter of "Electricity Markets."
The auction theorist Peter Cramton has a few papers on this.


Elasticity Cobb-Douglas production function I am given the production function
$y=x_1^\alpha x_2^{1-\alpha}$, where $0< \alpha <1$ I found the demand functions for minimum production cost to be
$ x_1^{*}(w_1,w_2,y)=\left ( \frac{w_2}{w_1}\frac{\alpha}{\beta} \right )^{\frac{\beta}{\alpha +\beta}}y^\frac{1}{{\alpha +\beta}} \; \wedge \; x_2^{*}(w_1,w_2,y)=\left ( \frac{w_1}{w_2}\frac{\beta}{\alpha} \right )^{\frac{\alpha}{\alpha +\beta}}y^\frac{1}{{\alpha +\beta}}$
Problem
Now, I have to find the elasticity of $(x_2^*/x_1^*)$ wrt. $w_2/w_1$. I found that $(x_2^*/x_1^*)=\frac{\beta w_1}{\alpha w_2}$ and thus
$\epsilon = \frac{\partial \frac{\beta w_1}{\alpha w_2}}{\partial (w_2/w_1)} \cdot \frac{w_2/w_1}{\frac{\beta w_1}{\alpha w_2}}$

Here I end up stuck and do not know how to reduce/evaluate this expression
What does the elasticity mean for the total production cost on input 1?

For elasticity calculation why don't you try this:
\begin{align}
\frac{x_2^*}{x_1^*}&=\frac{\beta w_1}{\alpha w_2}  \\
\ln\bigg(\frac{x_2^*}{x_1^*}\bigg) &= c - \ln\bigg(\frac{w_2}{w_1}\bigg) \tag{$c=\ln(\beta/\alpha)$}
\end{align}
It's easy to see from above that elasticity is $-1$
For second part, cost of production:
\begin{align}
C&=w_1x_1^* + w_2x_2^* \\
&=w_1x_1^*(1+\beta/\alpha) \\
&=\frac{w_1x_1^*}{\alpha} \tag{$\beta=1-\alpha$}
\end{align}
So the production cost per unit of input 1 is just the function of $w_1$ (given $\alpha$).
EDIT: As requested for clarification in comments:
Note that elasticity of $y$ w.r.t $x$ is defined as:
\begin{align}
\epsilon &= \frac{\partial y/y}{\partial x/x} \\
&= \frac{\partial \ln y}{\partial \ln x}
\end{align}
So elasticity is simply the slope on the log-scale for $y$ and $x$.
Now substitute $y=x_2^*/x_1^*$ and $x=w_2/w_1$ in your case.


Preference: Convexity and Monotonicity I need an example of a

Convex, non-monotonic preference
Non-convex, monotonic preference

I figured that an example of non-convex, monotonic utility preference could be $U(x,y)=x^2+y^2$.
For convex, non-monotonic preference, I cannot think of a standard example. As far as my understanding goes, lexicographic preferences are convex, but I don't think these are non monotonic.
Please help me figure out the examples so that I can conceptually understand monotonicity and convexity.

$u_1(x, y) = -(x-1)^2 - (y-1)^2$

$u_2(x, y) = -|x-1| - |y-1|$

are examples of utility functions that are concave, and therefore represent the preferences that are convex. Also, these preferences are not monotonic.


Is an increase in the money supply (by buying government bonds) recorded in bank deposits or bank reserves? When the central bank pays 100 shekels for a bond held by the public, it is assumed that the 100 shekels are deposited. In this case, shouldn't it cause a 100 shekels increase in the bank deposit rather than in the bank reserve?

No.

Think of it as the central bank is buying the bond from the commercial bank itself, so that the bank was holding one type of asset (a bond) and now it's holding another type of asset(cash or its equivalent, reserves at the central bank). At that point deposits have not increased, it's just, that the bank now is holding more cash and less bonds than before. Because cash holdings or reserves at the central bank are called 'reserves', the commercial bank's reserves have increased.

The next step is that this bank sees no benefit in holding so much cash, more than the regulation forces it to, so ti goes and lends this cash out. At that point deposits start increasing, but still, the whole of the banking system is going to have more reserves than before because somebody is going to be holding that cash.


Properties of orders and preference relations Suppose I have alternatives $A$, $B$, and $C$. If I have strict preferences, that means that for any $x,y \in \{A,B,C\}$ such that $x \ne y$, either $x \succ y$ or $y \succ x$. Assume transitivity, non-reflexivity, completeness.

Things get a lot less clear to me when I'm trying to talk about it as an order. How would I say that an agent has an order over the alternatives that would allow some ties? And how would I say an agent has an order over the alternatives that would not allow ties? Is it just the intuitive weak total order and strict total order, over alternatives where the weak and strict carry the same meaning as for preference relations and total is a synonym for complete?

You are right, total is a synonym for complete, it simply means that two elements can always be compared.

Regarding your other point, I don't think that your definitions of strict order and weak order are common but they make perfect sense. Notice, however, that they differ from some definitions used by mathematicians and therefore you have to specify what you are talking about to avoid possible confusions (you can check for instance "strict weak ordering").


Flooding the real estate market with luxury homes: will average prices rise or fall? If every middle class single-family detached home in a sprawling metropolitan area like Los Angeles were each replaced with two luxury condos over a short period, would average real estate prices rise or fall? Would they go in one direction first and then the other direction later?

Or the opposite, if every two luxury homes were replaced by a single middle-class home occupying both parcels, would prices rise or fall?

I'm asking this because I'm wondering whether density or luxuriousness has the greatest effect on real estate prices. Knowing the answer to this would help to answer the question, "To solve the housing crisis, is it better to increase supply (even if it's luxury housing) or mandate affordable housing?"

After 2 weeks and no answers, I think it's fair to say that the odds of a average prices rising as a result of replacing each lower priced dwelling unit with 2 higher priced ones is roughly equal to the odds of prices falling (equal enough that nobody is willing to stake their reputation on a prediction).


Higher interest rates and default probabilities for longer loan term I am analyzing LendingClub data with two available loan terms: 36 and 60 months. In the course of my analysis I have spotted that both the interest rates associated with each loan and the default probabilities, obtained from my logistic regression, are higher for the longer term.

I am thinking about a way of explaining why both interest rates and PDs are higher for the longer term. For the former the only thing I can come up with is that considering the assets are frozen for longer time lender expects to obtain higher premium.

Is there any formal way to explain these relations?

There are two reasons why the interest rate is higher for the 60 months loans:

1- Liquidity premium
Like you said, since the money is immobilized for a longer period of time, it's normal to expect a higher return on the investment. It's also a way of protecting itself in case the interest rate increases, because the longer the term of your loan, the more you are exposed to changes in the interest rate.

2- Risk
Like you said yourself, the risk of default is significantly higher when the term of the loan is longer. Following the basic rule that more risk means higher returns, it's clear that the rates will be higher for 60 months than for 36 months loans.

As for why the default rate is higher for 60 months loans than for 36 months, I'm not very knowledgeable on the subject, but if we imagine that there is a fixed chance that the borrower defaults each month, we can easily imagine why more months means more chances of default.


What econometrics book or textbook should I use? I'm doing my Master thesis in economics. I have only a very basic understanding of econometrics, and honestly, I hate it, the same things go for stats and probability etc.

Now, at my school my supervisor allowed me to get help to analyze the data using VAR or GARCH so the analysis itself will be done by an assistant at the school. However, my supervisor told me I should be able to answer questions regarding the model once I defend.

I have been watching tons of YouTube videos about econometrics, but I think I should get a book to understand econometrics better. I don't want to master it, I just want to pass the defense. Once I'm done I'm not interested to learn anything about econometrics ever again. So, any Econometrics book you suggest? I found many online and not sure which one is the easiest to use.

I'm not an econometrician but here is my guide on some books I found helpful (and still do).

My opinion is that a nice place to start is with an advanced undergraduate textbook. These are often helpful even for postgraduate students to build intuition and brush up on mathematical techniques. For books at this level I would recommend Introductory Econometrics: A Modern Approach by Wooldridge, Introduction to Econometrics by Stock and Watson and Introduction to Econometrics by Dougherty.

You mention a dislike of maths and stats. Do you have much in the way of familiarity with matrix algebra? If not, I recommend A Guide to Modern Econometrics Verbeek and the older Econometric Methods by Johnston and DiNardo. These two books are pitched at the more basic end of the graduate level and have excellent appendices on matrix algebra. Either of these would be an excellent starting point for you.

More advanced books include Greene's Econometric Analysis and my favourite; Econometrics by Fumio Hayashi. The latter textbook centers on the Generalised Method of Moments as an organizing principle which really helps illuminate the material. Econometric Analysis of Cross Sectional and Pannel Data Wooldridge is a classic at the graduate level. It may also be worth considering the online textbook by Bruce Hansen. It's available on his website https://www.ssc.wisc.edu/~bhansen/econometrics/ These textbooks could be considered rather mathematical on the first reading. For example, the large sample properties of estimators require some sophisticated mathematical machinery. All the textbooks mentioned above give a fairly digestible but somewhat superficial treatment of these topics. To get a good understanding of these topics a course in measure theory and integration would be a great help!

After these come the more specialized books in Panel Data / Microeconometerics and Time Series. The books mentioned in the previous paragraph generally do have an introduction to these topics but it's rather limited. In terms of Time Series a great textbook is Applied Econometric Time Series by Walter Enders. This book is much more focused on Time Series in real world applications and is lighter on the technical details. I would recommend this to you if you're still interested in VAR, ARCH, GARCH and error correction models etc. In stark contrast is Time Series Analysis by James Hamilton. A very thorough textbook but quite a hard read. In terms of Microeconometrics, I like Microeconometrics by Cameron and Trivedi and Baltagi's Econometric Analysis of Panel Data. There are many, many more excellent textbooks in these fields but they're too numerous to mention. Remember, these books are advanced and would only be used once you've taken the standard grad econometrics courses.

Lastly, every economist should have a copy of Angrist and Pischke's Mostly Harmless Econometrics A great guide to econometrics as a discipline.


Papers on time it takes to reach market equilibrium? Are there any models which calculate the time (i.e. minutes, hours, days ect.) it takes for equilibrium to occur? I.e how long it takes for market forces to settle.

There is the famous Cobweb model. Starting from a situation of equilibrium, it analyses the effect of a shock, and how long it takes to reach the equilibrium, which depends entirely on the elasticities (or slopes) of demand and supply. Thus, by estimating empirically such elasticities for a given period (e.g. year), you can predict/forecast how many periods (e.g. years) it will take the market to settle after a shock. (Strictly speaking, it is infinity, but you just need to select a threshold or margin of error to consider the market in equilibrium)

$\hskip4cm$

(Source of image here)

A huge literature exploded after the original paper, so if you google Cobweb models you get tons of hits. A very good treatment of this model can be found in Waugh (1964). He extends the simple model to multiple markets.


Are Tariffs Refundable? When a company decides to import goods, are the tariffs paid on those goods collected by customs? Or do companies treat tariffs as tax expense? In any case, can a company write off these tariffs and eventually receive a tax refund on the import taxes they paid?

can a company write off these tariffs and eventually receive a tax
  refund on the import taxes they paid?

A country might enact provisions to exempt (possibly via a refund) from tariffs if the importer company or the intended use of the import meets certain conditions. But generalizing a reimbursement in the form of tax refund (or tax credit) would defeat the purpose of imposing tariffs.

Keep in mind that tariffs are one mechanism through which policymakers implement barriers to entry in the domestic market. Therefore, it would make no sense for the regulator to implement such barriers only to essentially reverse them thereafter.


Extended game mixed SPNE I have doubts about whether my solution is right.

Let $x>z>y>1$.
Given the extended game above, we have two subgames and will find the Nash equilibria of these.

Observe that player 2 will play $b$ in the right subgame.
In the left subgame player 2 will be indifferent between playing $a$ and $b$.
Let $0 \le r \le 1$ such that $(r,1-r)$ is a mixed strategy for player 2 and such that the player plays this strategy in the left subgame.

Player 1 has expected payoffs
$$
E u(s) =
(rx+(1-r)y) \cdot 1_{s=A} + z \cdot 1_{s=B}
$$
Observe that player 1's best response is $A$ if $r \ge (z-1)/(x-y)$ and $B$ if $r \le (z-1)/(x-y)$, hence SPNE will be
$$
\{(A,(r,1-r))|r \ge (z-1)/(x-y) \} \cup \{(B,(r,1-r))|r \le (z-1)/(x-y)\}
$$
Is this correct?

There are a couple of imprecisions. First, there are 3 subgames: two that start with player 2 moving, and the complete game is also a subgame.

Second I think that A is a best response for player 1 iff $r\geq \frac{z-y}{x-y}$ otherwise, B is the best response (perhaps this was just a typo).

Third, note that the fraction is guaranteed to be positive and smaller than 1 given the restrictions on the parameters,  but it is not necessarily equal to $0.5$, For example, let $x=5$, $z=3$ and $y=2$, then the fraction is equal to 1/3.

Fourth, you are missing other equilibria, when $r=\frac{z-y}{x-y}$, then player 1 is indifferent between A and B, and any randomization of A and B also constitutes an equilibrium.

Lastly, when writing the equilibria, you must specify what player 2 does in each information set (or node in this game)

Thus: $$NE=\{(A, (ra+(1-r)b)b)|r\geq \frac{z-y}{x-y}\}\cup\{(B, (ra+(1-r)b)b)|r\leq \frac{z-y}{x-y}\}$$
$$\cup \{((pA+(1-p)B), (ra+(1-r)b)b)|r=\frac{z-y}{x-y}, 0\leq p\leq 1\}$$

Where $(ra+(1-r)b)b$ specifies that player 2 randomizes in the first node and chooses b in the second node.


What's the combined wealth of the US banks? How much is the amount of money of the US banks?

In terms of:

assets
their own money (the banks make profits and they can keep the profits for further investments)

The question is only about the banks. No retirement funds, no venture capitals, no other kind of financial institutions.

The total assets of all US commercial banks are about \$17.2 trillion.

You'll have to be clearer about what you mean by "their own money". The total equity capital of US commercial banks is about \$1.9 trillion.


What is the No Free Lunch Axiom? I have tried to know what exactly is the no free lunch axiom.

I got multiple links but none of them explain (a bit rigorously, with examples and mathematical formulation) what is this axiom and how I can formulate it in a problem.

Any explanation/formulation is very much welcome. Thanks.

First, to give a little more background to 123's answer, a production set $Y$ is the set of all feasible production values. With $y \in Y$, $y$ is a vector in $\mathbb{R}^L$, where positive elements indicate outputs, negative values indicate inputs. The inputs you put in that get you outputs can be expressed with either a transformation or production function.

123 is right when stating the formal microeconomic axiom:

  If that $y \in Y$ and $y \geq 0$, then $y = 0$.

However, no free lunch axiom needs more than merely "passing through the origin."

The first image shows a production set (two dimensions) that satisfies no free lunch. The second image shows a production set that does not, even though it passes through the origin.

(yes this image is a bad rendition of the one from Mas-Colell)

Interestingly though, the author has not tagged this with a microeconomics tag, but with a macro and financial econ tag. The closest thing I can thing of to a no free lunch axiom here is some sort of transversality condition. For example, in simple money market models with borrowing with households, you might have a condition that you have to pay off all debt eventually (or just pay the interest for the rest of your life). So that would look something like,

$$\lim_{t \rightarrow \infty} \frac{b_t}{(1+r)^t} = 0$$

where $b$ is the amount of borrowing in a period. I do not hear conditions like these called "no free lunch" however. Something more like "no ponzi scheme condition" or "borrowing constraints" (snoree) are more apt.


Banking Regulatory Frameworks, between the 1973 collapse of Bretton Woods and the introduction of the Basel Accords in 1988? Does anyone know of any work on banking system behaviour, and in particular the governing regulatory frameworks that applied between the 1973 collapse of the gold standard based Bretton Woods system and the introduction of the Basel Accords and capital regulation beginning in 1988?

I found two of two papers discussing this period, the second at length.

  Throughout the 1970s, the capital position of many banking
  institutions declined significantly. To address this decline, in
  December 1981, the bank regulators issued explicit minimum capital
  standards for banks and bank holding companies. These standards
  required banks to hold capital at least equal in amount to a fixed
  percentage of their assets. While these standards have been given
  credit for increasing bank capital ratios, the 1980s saw an increase
  in both the number and cost of bank failures. A weakness of the
  minimum capital standards is that they failed to take into account the
  risk in a bank's portfolio of assets; high-risk assets required the
  same amount of capital as low-risk assets.

Risk-based capital, portfolio risk, and bank capital: A simultaneous equations approach by K Jacques and P Nigro (1997)

  In 1972 the Fed capital standard was revised again. Asset risk was
  separated into “credit risk” and “market risk” components. In
  addition, banks were required to maintain a higher capital ratio to
  meet the test of capital sufficiency. Further, the Fed reintroduced
  both the capital to total asset and capital to total deposit ratios.
  This time, however, the former ratio was based on total assets less
  cash plus U.S. government securities, a rough “risk asset” adjustment.
  In practice, bankers and analysts used the FDIC and Fed standards more
  than those of the OCC.

  None of the agencies established a firm minimum capital ratio.
  Instead, the capital positions of banking institutions were evaluated
  on an individual bank basis. Particular attention was directed toward
  smaller banks whose loan portfolios were not as diversified and whose
  shareholders were fewer in number than those of larger institutions.
  It was reasoned that small or “community banks” might have a hard time
  raising capital in times of difficulty and therefore should be more
  highly capitalized at the start than larger institutions. Table 1
  shows the banking industry’s capital-asset ratios from 1960 to 1980.
  The table shows that there was a steady downward drift in the ratio,
  which can be explained by a number of factors. Chief among these would
  be the attractiveness of increased leverage in banking and reliance on
  other techniques to manage balance sheets, e.g., liability management.

  In late 1981 the three Federal bank regulatory agencies announced a
  new coordinated policy related to bank capital. The policy established
  a new definition of bank capital and set guidelines to be used in
  evaluating capital adequacy. The new definition of bank capital
  included two components: primary and secondary capital.

  Primary capital consisted of common stock, perpetual preferred stock,
  surplus, undivided profits, mandatory convertible instruments (debt
  that must be convertible into stock or repaid with proceeds from the
  sale of equity), reserves for loan losses, and other capital reserves.
  These items were treated as permanent forms of capital because they
  were not subject to redemption or retirement. Secondary capital
  consisted of nonpermanent forms of equity such as limited-life or
  redeemable preferred stock and bank subordinated debt. These items
  were deemed nonpermanent since they were subject to redemption or
  retirement.

  In addition to the new definition of capital, the agencies also set a
  minimum acceptable level for primary capital and established three
  zones for classifying institutions according to the adequacy of their
  total capital.

International risk-based capital standard: History and explanation by MC Alfriend (1988)

In the United States it isn't just Basel that is changing. FDICIA act of 1991 is also an important regulatory change for regulatory capital standards. My understanding is that Basel I only worried about credit risk but FDICIA introduced capital requirements for interest rate risk as well, perhaps in response to the contemporaneous savings and loan crisis where many thrifts failed as a result of losses from interest rate exposures.


What is the result of the Bellman Equation I'm just starting with dynamic optimization and although I understant the proof's of the theorem I'm not able to fully understand whether the bellman equation is a function , a function valuated at some number (therefore a number) or both. I'm using Stokey Lucas and Prescot Recursive methods for economics dynamics.
And they state in chapter 4.1 and 4.2:
Given some assumptions about the feasibility set and the $F(x_t,x_{t+1})$ This problems are equivalent:

1) Sequential problem (SP) $sup_{x_{t+1}}(\Sigma_{t=0}^\infty\beta^tF(x_t,x_{t+1}))$ st $x_{t+1}\in\Gamma(x_{t+1})$ for all t

2) Functional Equation (FE)$\ v(x)= sup_{y\in\Gamma(x)}[F(x,y)+\beta v(y)]$

What is not clear to me is that:
In 1 the result of applying sup operator is a NUMBER (Value function valuated at $x_0$
While in 2 as there is a functional equation, the result is a function. The authors seem to talk about a number (chapter 4.1) but then (in chapter 4.2) they state that applying the contraction mapping theorem to 2 we get the solution which is the unique fixed point in the set of continous bounded function, therefore the result is a function

So the solution is a number or a function?

Thanks in advance

...the result of applying sup operator is a NUMBER...

Read it carefully. The equation is

$$
v(x_0) = \sup_{ \{x_t \}_{t \geq 1}} \cdots \quad (1)
$$
This defines a function $v$, called the value function, which is a type of indirect utility function. For given $x = x_0$, the value of $v(x)$ is defined to be the sup on the RHS, taken over feasible sequences $\{ x_t \}$.

  ...there is a functional equation, the result is a function...

Yes, the function $v(\cdot)$ defined by $(1)$ satisfies the function (Bellman) equation. Equality is in the sense of functions. The LHS is $v(\cdot)$ defined by $(1)$. The RHS is another function
$$
\nu(x) = \sup_{y\in\Gamma(x)}[F(x,y)+\beta v(y)].
$$
The claim---the dynamic programming principle---is that they are equal, $v(\cdot) = \nu(\cdot)$.


What is a suitable exponential to model a currency or cryptocurrency price increase? I would like to model a supply and demand cryptocurrency market, not necessarily for Bitcoin, but for any coin.

My difficulty is that I don't know what exponential factor might be realistic to do so.

For example, I was thinking to pick the exponential factor of e to the power of -10 increase per coin, this happens in case you exchange fiat money for crypto.  Same exponential factor applicable for decrease, in case that the reverse transaction is performed.

I searched online, and I can't seem to find a value for this factor, or additionally if it is static or dynamic.

For example, with the above data, if the starting value of a coin is \$300 and someone invests \$300,000 to buy 1,000 coins, the value per coin goes to \$300.0457.

But if someones invests \$3,000,000 the value goes to \$345.7, I am wondering if it is realistic given that the total coin supply is 8 Million for example?

Can someone help and reason about the choices?

If I understand correctly you are asking for a model of the price of bitcoin based on the number of transactions.

This is impossible for a number of reasons.
1) Theres not necessarily a relationship between those two variables

2) Even if there is a relationship between the two variables then there may be other variables which have an equal or stronger effect (very likely) making the variable a bad predictor.

3) If there is a relationship, and that relationship is not causal then knowing the relationship would cause it to cease to exist as people begin to trade on arbitrage with that model which would cause it to stop being effective.

4) If I could model the price of bitcoin I certainly wouldn't be telling you, instead I'd be living it up on some private island.

The question makes no sense and is by far too complicated of a question. This is the type of topic that requires a research paper and will not necessarily provide results.

This is why you are getting downvoted. Because there is no logical answer to what you are asking.


Comparing two groups of individuals I am trying to show that 2 groups of individuals are intrinsically the same. I have various characteristics to measure for both group A and group B, such as height, weight, age, etc. I have taken the averages of both groups, and by inference I can see that they are similar - is there a more robust way of doing this?

It sounds like you've basically eyeballed the groups to decide they're similar.  A more advanced way of eyeballing would be to plot the distribution of each characteristic to see how similar they look.

In order to move beyond eyeballing, though, you'd need recourse to statistics.  One common method--that comes up frequently in two-group studies--is to do a statistical test to verify that the groups are similar.

Broadly speaking, you'd look at each characteristic, say height, and assert your hypothesis: average height is the same in both groups.  Then you could do a hypothesis test to see if the average height is actually the same or if it's different.  The specific statistical test you'd use depends on the data in your possession.

For example, see the t-test or the chi-squared test for a difference in means (or just search for "difference in means test").

You'd run the test (statistical software generally has a feature like this) to determine if it shows a significant difference.  If so, you might be comfortable saying the groups are not similar inasmuch as that characteristic (height) is concerned.

Then you'd repeat the procedure for weight and each of the other characteristics.

At the end, many researchers make a little table that shows the $p$-values (which you get from running the test) for each of the characteristics.  This gives your readers the ability to see and decide for themselves whether the groups are similar.


Are many of the traditionally oil dependent Gulf states really successfully diversifying their economies? I've often heard the claim that many Persian Gulf states are successfully diversifying their economies away from oil (e.g. non-oil share of UAE's GDP is 71% and in Saudi Arabia, non-petroleum sectors account for 55% of GDP).

The IMF has the 2015 GDPs per capita of the UAE and Saudi Arabia at \$35392 and \$20139, respectively. If we naively multiply by 0.71 and 0.55, we get that their non-oil GDPs per capita are \$25128 and \$11076. \$25128 is greater than the GDPs per capita of countries such as Portugal and Poland, and \$11076 is greater than those of Mexico and Turkey.

I'm rather surprised by this, as I've always assumed that countries such as Portugal and Mexico would be well ahead of the UAE/Saudi Arabia in terms of development/education/technology etc.

Are the 71% and 55% figures (and similar claims for other Gulf states) misleading in some way?

i.e. Are most of the non-oil sectors in these countries still somehow mostly based on oil (and would they substantially shrink/collapse if the oil "ran out")?

Several points need to be considered.
1) Oil dependance
First of all, it is not correct to assert that 30%-50% of the GDP being concentrated within a single industry is "diversified". This is actually a high focus. And I can only guess that a lot of other activities are strongly linked to the oil and gas sector: for example, banks might have a lot of lending and trading activity in the sector, even if they aren't directly counted as being part of it.
Moreover, an interesting thing to look at is how much the government relies on this sector. For Saudi Arabia, which budget was discussed recently, it is obvious that oil plays a major role, as it makes up 90% of the revenue. FOr the UAE the answer is more tricky, as the different emirates have significantly different profiles: Abu Dhabi relies strongly on the Oil industry, whereas the other provinces such as Dubai rely only slightely on oil - though they receive high subsidies from Abu Dhabi.
As for the progress made, there are indeed some. These countries are aware that they will not always be able to pump up oil, and that they will need, in the long run, to have other strong industries. That is why, pretty much the same as in Norway, huge sovereign funds (check nb 4 & 6) are setted up in these countries.
2) Development

I'm rather surprised by this, as I've always assumed that countries
such as Portugal and Mexico would be well ahead of the UAE/Saudi
Arabia in terms of development/education/technology etc.

GDP per capita is not a index of development. If you are looking for this, the HDI (Human development indicator) is a much more appropriate index. This index is composed by several measures of education, wealth (GDP), life expectancy and other indicators which relate to development and not only to economics.
The UN compiles this indicator on an annual basis, here is for 2015. It shows the following for the countries you mentioned. The HDI ranges from 0 (worse) to 1 (perfect score):

#6 Ireland: 0.916
#36 Poland: 0.843
#39 Saudi Arabia: 0.837
#41 United Arab Emirates: 0.835
#43 Portugal: 0.830
#72 Turkey: 0.751
#74 Mexico: 0.756

You can see from this numbers that the gap is thin between Portugal and the two Oil countries. You can also guess that the contribution of their GDP to the index is much higher than for any other countries mentionned above. Thus, the pure non-economic indicators are probably better for the other countries than Saudi Arabia and the UAE.
Furthermore, the Gini index can also be used to have some idea of the level of development of a country. It shows how big inequality are (regarding income or net wealth for example). Sadly this index is not computed for Saudi Arabia nor for the UAE. This is probably intentional, as these countries are well known to have very high inequality, in particular between immigrants (the huge majority of UAE's inhabitants) and natives.
Finaly, your intuition is correct: it is indeed quite 'naive' to multiply the GDP per capita by the proportion of GDP of a sector. Indeed, different sectors display significantly different productivity and efficiency of the labor factor. Therefore, it can be that the productivity of the Oil industry in those countries largely outweigh the productivity of the other sectors. Therefore it is hard to compare this clumsy calculation with 'full' GDPs of other countries.


Consequentialist View of Risk In MWG, the authors introduce the consequentialist view of risk by assuming for any risky alternative, only the reduced lottery over final outcome matters to decision maker.

From philosophical view, how reasonable and unreasonable is this assumption? Can you provide examples where this assumption is in fact reasonable while it isn't?

This assumption is usually considered as reasonable from a normative perspective. For instance, consider the following situations:

in situation A, you face an urn with 5 blue balls and 5 red balls. A ball is selected randomly and you can bet on its color. You receive a monetary prize if your bet is correct.
in situation B, you face an urn which contains 10 balls that are either blue or red. The number of blue balls has been selected randomly between 0 and 10 according to a uniform distribution. Once the urn is formed (you don't know its content), a ball is selected randomly and you can bet on its color. You receive a monetary prize if your bet is correct.

In the expected utility theory we usually consider these two situations as equivalent since the probability distribution over the final outcomes (the color of the ball) is the same: there is a 50% chance that a blue ball is selected, and a 50% chance that a red ball is selected. Therefore from a theoretical perspective it is hard to think of any reason why people would strictly prefer one situation to another.

However, empirically a substantial fraction of people exhibits a strict preference for the situation A rather than the situation B, for instance in
 this paper. According to my experience, most researchers are inclined to see this behavior as a mistake that reflects some difficulties with the computations of probabilities. Interestingly, this behavior is associated with ambiguity aversion, which is also sometimes considered as an anomaly. However, this is largely a matter of interpretation and other researchers prefer not to make any normative judgment on this behavior, as a matter of principle, and prefer instead to try to find rational explanations for it. It is for instance sometimes argued that the additional layer of uncertainty which is added in situation B would be a legitimate reason for risk-averse individuals to strictly prefer situation A.


Auction models for tickets Consider a typical event for which tickets are sold. The event only occurs once, and for simplicity we can assume that only one kind of ticket is provided, i.e, no VIP seats etc. $N$ tickets are available for purchase, and the $N$ highest bids win the tickets.

Does it exist any clever method for auctioning this type of good?

In a classical auction, the highest bid at any time is shown and participants can increase their bid to be the winning participant. But when auctioning this type of good that does not make any sense, since there will be multiple winning bids.

Should we perhaps display the currently lowest bid that is needed to win a ticket?

What if we think that it is desirable that all winners pay the same amount, how does this effect things?

I have tried to find existing research about this, but to no avail. Maybe there exist some other type of good with similar characteristics to tickets (oil/electricity?) that is auctioned in a similar way? Pointers to existing literature would be greatly appreciated.

First, you should note that there is a Revenue Equivalence Theorem that says under a set of conditions, the seller's revenue from using different forms of auctions will be the same. This same result holds also in the multi-object case.

Thus, if you have $n$ identical objects, you can sell them using a ($n+1$)th price sealed bid auction, where

each bidder submits a bid,
the highest $n$ bidders each gets $1$ object, and
they all pay a price equal to the ($n+1$)th highest bid.

In such an auction, it is a weakly dominant strategy for every bidder to bid their true valuation of the object. The reasoning is similar to the second price auction.


Effect of policies on Aggregate Demand Expenditure dampening policies may be used to correct a current account deficit. This involves contractionary monetary and fiscal policy. I understand that such measures induce a reduction in consumption, investment, and government spending and may lead to a fall in aggregate demand. But how can we be so sure that the rise in net exports won't counteract the fall and make AD rise? Is this not a common case? In short, what is the usual effect of dampening policies on AD?

The effect of trade would normally only partly counter-act the dampening policy.

It is of course possible that a change in net exports is greater than the effect of the dampening policy if multiple things are changing at once (like in the real world). I will just discuss what happens if all else is assumed to be unchanged.

My discussion is based on model OPEN, from “Monetary Economics” by Wynne Godley and Marc Lavoie (Link to code at sfc-models.net). I will just do some back-of-the-envelope approximations.

We assume that imports are a fixed percentage of demand (20%), and we have two identical economies (which start out with balanced trade). We then assume that Country A enacts a policy that reduces domestic demand by 5%.

Exports would be (roughly) unchanged, since Country B kept policies unchanged. (This is an approximation, since the drop in growth in A would also reduce growth in B via its export industry.)
Imports drop by 5% relative to their previous level, which is an amount that is 1% of total demand (since imports are 20% of domestic demand).

The net result is that there is only a 1% offset from trade against the 5% demand contraction from policy.

In order for there to be no effect, we would need the entire drop in domestic demand to come from imports. There might be some special cases where this happens (e.g., the government stops importing foreign equipment), but such selectivity would not be possible with something like interest rate policy.


What was Milton Friedman's opinion of Supply-side economics? I realize Friedman was a part of the Reagan Administration, so even if he disapproved of supply-side, he may have moderated his remarks, but what kind of things did he say about it, and what can be determined about his real opinion?

Friedman's involvement with the Reagan administration was only as an advisor, he was not part of the administration as a cabinet member or on the budget committee (roles he was offered), specifically because he did not want to moderate his writing and public opinion.
On the Reagan administration Friedman supported the Reagan Era tax cuts, but noted that tax cuts must be coupled with government cuts as well. In fact he even said that cutting the size of government must come before tax cuts, and predicted correctly the increase in he deficit. It should also be noted that Reagan himself was strongly in favor of government cuts in areas of education, agriculture, and environment, but he had to pick his battles with the congress controlled by the democrats (this is outlined very well in Steven Hayward's book "The Age of Reagan: The Conservative Counterrevolution").
Friedman also once famously said on Uncommon Knowledge with Peter Robinson "There is no such thing as supply side economics, nor demand side economics, just economics. You do not increase production by taking from one and giving it to another, whether that be from supplier to demander or vise versa". It should be noted this was after the Reagan Administration.
Generally however, Friedman was a strong supporter of the Reagan administration, because he thought that this was the first successful political movement in over a century to embrace the founding principles of America and liberty (he had the same view of Goldwater's 64' campaign which was unsuccessful).


integrating CDF functions by parts This is a derivation from "Exercises in Recursive Macroeconomic Theory
preliminary and incomplete by
Stijn Van Nieuwerburgh
Pierre-Olivier Weill
Lars Ljungqvist
Thomas J. Sargent"

which uses integration by parts. Can someone explain how this derivation works? I have tried to replicate the result but to no avail. The text says that they use integration by parts on the second integral on the second line [the integral from w to B of w'dF(w') ]

The integral of interest is $\int_{w^*}^Bw'dF(w')$, which we get after bringing $\frac{1}{1-\beta}$ out from the last integral on the second line. I assume that $F(B)=1$. First, we have:

$$
\int_{w^*}^Bw'dF(w')=w'F(w')\vert_{w^*}^B - \int^B_{w^*}
F(w')dw'
$$
from integration by parts with $u=w'$ and $dv=dF(w')$. We can evaluate the first term on the right-hand side and both add and subtract $B-w^*=\int^B_{w^*}dw'$ to get:

$$
\int_{w^*}^Bw'dF(w')= [B - w^*F(w^*)]-[B-w^*]+\int^B_{w^*}(1-F(w')dw'.
$$

Note that I used my initial assumption here, and that I brought $B-w^*=\int^B_{w^*}dw'$ under the pre-existing integral.It should be clear after a bit of algebra that:

$$
\int_{w^*}^Bw'dF(w')= w^*(1 - F(w^*))+\int^B_{w^*}(1-F(w')dw'.
$$

The rest should be easy after multipling both sides by the $\frac{\beta}{1-\beta}$ factor.


Short run Aggregate supply curve is horizontal or positively sloped? I want to understand the adjustment process from Very short run production decision to Short run production decision to Long run production decision. As far as I've understood: consider an increase in demand due increase in money supply. This shifts AD curve to the right.
 Now in the very short run firms are willing to supply whatever demanded at the same price (I want to know why. They'll ask workers to work overtime so they'll have to pay extra wages so why would they not charge higher price instantly?)
After the very short run, prices rise(why?) and demand falls so output goes down after the initial increment.
In the long run, prices keep rising (why?) and output finally comes back to initial full employment level.
I couldn't find proper coherent explanation of this process in mankiw or in dornbusch or in Able and Bernanke.

Explaining the shape of the horizontal range

In the very short run, the AS curve is perfectly price-elastic (i.e. on the diagram, it is a horizontal line). It is also referred to as the Keynesian range. In this time period, firms respond to a rise in demand for their product without considering the effects of the rising demand, such as higher prices. This can be generalised for most firms, but not all.

We can look at the example of a book shop or a food stall. It is highly unlikely for these business owners to revise their prices on daily basis based on their past sales records. Prices are more likely reviewed on a yearly basis, or at some longer time intervals. Regular price revision could complicate simple business accounting and may also dispel customers, especially if your price revisions keep increasing the price.

There are some firms that are able to change prices based on instantaneous demand. These would include the ride-hailing service providers, such as Uber and Grab. Their computer algorithms are programmed to respond their market conditions of the moment and price according to the level of demand at that instant. Firms that are able to do this are more of an exception rather than the norm.

Another way to view it would be from the resource employment viewpoint. In this time period, the real national output/income is much lower than the full employment level. Thus, there is an abundance of un-utilised or under-utilised resources. Should there be an increase in aggregate demand, the spare capacity will allow producers to increase output production easily without incurring high costs. Hence, there is no pressure on the general price level to increase.

Explaining the shape of the vertical range

In the long run, prices do not keep rising. The AS curve is simply perfectly price-inelastic (i.e. on the diagram, it is a vertical line). This is also called the classical range of the AS curve. In the LR, all firms in the economy reach their full employment level (i.e. resources have been fully-utilised to their maximum capacity) and there is no demand-deficient unemployment.

  Price levels are fully flexible as all firms will make adjustments to their prices in response to changes in demand.

This is what the vertical line indicates, not what you have mentioned.

The model I have learnt for the AS curve is a hybrid model, which is also depicted by the Wiki page for AS. The "short run" here essentially corresponds to your so-called "very short run". The "long run" here is the same as yours.


Is there a name for this type of CES look-alike utility function? I do not know whether the following utility functions are used a lot but I was wondering whether there was a common name for this type CES like utility function
$$u(z) = \left[  \left( \sum_{j \in J_1} z_{j}^{\alpha_1}\right)^{\rho/\alpha_1} + \left(\sum_{j \in J_2} z_{j}^{\alpha_2}\right)^{\rho/\alpha_2} \right]^{1/\rho},$$
perhaps it is simply an instance of the CES?
The utility function is defined over two non-overlapping groups $J_1$ and $J_2$ of goods $z_j$. The goods from the two groups are weighted diffrently in the inner CES part by $\alpha_1$ and $\alpha_2$ respectively.

I believe it can be found as "nested CES"
See for example:
Nested CES


Just how much calculus is involved in the study of economics? I'm a sophomore at a community college that is set to transfer to the University of California as an economics major. It's required that I took Calculus 1 and Calculus 2, but not Probability and Statistics – which I think is strange. In Principles of Microeconomics and Principles of Macroeconomics, I never really used any calculus, but then again, those are introductory classes.
Tldr
I am terrible at calculus, but I love economics. When I get into the more advanced classes, is calculus going to be more present? If so, then I think I might have to rethink my major.

Calculus is one of the most widely-used branches of mathematics in economics. Here are two (non-exhaustive) examples of important ways in which economists use calculus:

to optimize functions. As a simple example, suppose we are thinking about a firm that must choose its price in order to maximize profit. Provided the profit function satisfies a few regularity properties (i.e. is continuous, concave, and smooth), locating the profit maximizing price amounts to identifying the price for which the profit function has zero slope (see figure below). Since we can calculate the slope by differentiating, calculus gives us the means to identify the optimal price.

to perform 'comparative statics' analysis. In the simple problem above, the firm must choose its price to maximize profit. But often the result of this exercise will depend on a number of other factors. For example, the optimal price will probably depend upon competitive the market is, or how closely substitutable rival products are. A relevant question for economics is therefore "if I make the market more competitive, what will happen to the price?" The typical way of answering this question would be to calculate the optimal price, $p$, as a function of market competitiveness, $h$, and then compute the derivative: $p'(h)$.* Indeed, $p'(h)$ is the answer to the question "how much would $p$ change if I change $h$ a little bit?" Most types of policy involve going into a market and changing something, so the ability to predict what the effects of such a change are likely to be is very useful.

In your principles courses, you have probably seen problems that could be solved in this way, but were instead solved through some other method. almost certainly, the courses were organized like this in order to avoid making calculus a requirement. But, as the problems you tackle become more sophisticated, there comes a point at which the effort needed to learn calculus is much less than the difficulty of trying to figure out a way to do economics without calculus.

To answer your explicit question, there is lots of calculus in economics. But, to answer the implicit question in the background, I don't think you should be too worried by this because:

Economics involves a lot of fairly easy calculus rather than a little very hard calculus. Primarily, this means calculation of simple derivatives and the occasional bit of integration.
Doing economics is a great way to become good at calculus! You will get lots of exposure to simple calculus problems. That will give you lots of practice. Moreover, the problems will usually have a fairly concrete application and hopefully make calculus more interesting than trying to learn it from an abstract math book.
From long personal experience as a student and teacher: it is easier to become good at things you love than it is to learn to love things you are good at.

* If you are wondering why I use $h$ for competitiveness, take a look at the Herfindahl index.


Martingale, random walk and rational expectations What is the link between these concepts?

For example let's take a process $Z_n$ which follows a random walk, I would say that:

This is a martingale, because my expectations of tomorrow, i.e. n+1 depends only on today
If we assume an individual with rational expectation, he will assume that the forecast of tomorrow is today

So in other words, the forecast of a rational individual follows a martingale?

I am not sure of the difference since one concept is used in finance while the other in economics, but they seem basically the same to me.

Last, I have seen in the example that a random walk implies a martingale, is this always true? Does a martingale implies a random walk?

All concepts are used in Economics. Definitions (not stated in a fully rigorous manner):

Martingale : A stochastic process $\{X_t\}$ is called "martingale" if and only if it holds that

$$E(X_{t+1} \mid X_t,X_{t-1},...) = X_t \tag{1}$$

There are extensions like "sub-martingale", "super-martingale" but the basic definition is the above

Random walk : A stochastic process $\{X_t\}$ is called "random walk" if and only if

$$X_{t+1} = X_t + u_{t+1}, \;\;\;u_t \sim \text{White Noise} \tag{2}$$

and you can look up the definition of "White Noise".

Here too there are spin-offs ("random walk with drift" etc).

Comment: it follows that a random walk is (one example of) a martingale, but a martingale does not imply a random walk, the former being a much broader concept.

Rational Expectations : Originally, the Rational Expectations Hypothesis stated that the aggregate expectation on some unknown (usually future) value of an aggregate random variable (see also this post on the matter), equals the conditional expectation (in the rigorous mathematical sense) of this variable "given all relevant information at the time of forming the expectation

$$X^e_{t+k|t} = E(X_{t+k}\mid I_t) \tag{3}$$

Comment: note that the conditioning set in the case of a martingale contains only past values of the process. The conditioning set in the case of the REH contains "whatever is available and is deemed relevant".

In a representative agent model, we are forced, for internal methodological consistency, to apply the Rational Expectations Hypothesis at the individual level (something that has raised all sorts of valid objections as regards the availability of information and the information processing constraints of an individual).

So do the forecasts of an individual follow a martingale? Let's examine it for one-step-ahead forecasts only: our stochastic process is

$$\{X^e_{t+1|t},\,X^e_{t+2|t+1},...\} = \{E(X_{t+1}\mid I_t),\,E(X_{t+2}\mid I_{t+1}),...\} \tag{4}$$

To obtain the martingale property it must hold that

$$E[X^e_{t+1|t} \mid X^e_{t|t-1}, X^e_{t-1|t-2},...] = ?\; X^e_{t|t-1} \tag{5}$$
(I will name in the end what expectations does eq. $(5)$ describes)

$$E[X^e_{t+1|t} \mid X^e_{t|t-1}, X^e_{t-1|t-2},...] = E[E(X_{t+1}\mid I_t) \mid \{E(X_{t}\mid I_{t-1}), E(X_{t-1}\mid I_{t-2}),...\}]$$

As already commented, the outer conditioning set is smaller than the inner conditioning set. By the Law of Iterated Expectations (the Tower property) of Conditional Expectation, we then obtain

$$E[E(X_{t+1}\mid I_t) \mid \{E(X_{t}\mid I_{t-1}), E(X_{t-1}\mid I_{t-2}),...\}] = E[X_{t+1} \mid \{E(X_{t}\mid I_{t-1}), E(X_{t-1}\mid I_{t-2}),...\}] \tag{6}$$

The right-hand-side of $(6)$ is not necessarily equal to the right-hand-side of $(5)$, so we cannot say that the stochastic process of one-step-ahead forecasts under the Rational Expectations Hypothesis is a martingale.

Equation $(5)$ describes the following situation: standing at period $t-1$ we form the expectation $X^e_{t|t-1}$, and "the best we can say" (best in the mean-squared-error sense) about our subsequent expectation $X^e_{t+1|t}$ is that it will be equal to $X^e_{t|t-1}$. This is (sometimes) called "static expectations" but beware because the term has two totally different meanings in the literature: for some authors, eq. $(5)$ represents "static" expectations in the sense that the expectation itself remains (or is expected to remain) unchanged in value. But you will often find authors who write the term "static expectations" and mean something totally different, namely $E(X_{t+1}\mid I_t) = X_t$ ("what is, will be"). This in turn looks like the martingale property but it is at best an extended concept of it (because the conditioning set is bigger), and in any case, it is a martingale-like property as regards the actual process $\{X_t\}$, and not the forecasts-process $\{X^e_{t+1|t}\}$.


What is the term for workers who outputs more than other workers at the same wage thus depressing wage for others? What is the specific term for this worker:
A worker who outputs more than other workers at the same wage, therefore now the owner doesn't have to pay workers as much since he gets the same total output for less

There are two aspects to your question. One is indeed (marginal) productivity of workers. The other is the "depressing wages" [for others] angle. (In more neutral terms, the latter would be "wage adjustment".)
Wages are generally downward sticky, so lowering everyone else's wages when one more productive worker shows up is a bit mythical (or at least uncommon), I think, in a salaried environment anyway. For wages to get lowered due to worker competition, it would have to be a more sustained effect, like finding cheaper labor "in bulk" due to some systemic reasons (e.g. finding labor abroad, or there being substantial unemployment etc.)
If there really is term for this pertaining to an individual worker... it might be:

The term Stakhanovite (стахановец) originated in the Soviet Union and referred to workers who modeled themselves after Alexey Stakhanov. These workers took pride in their ability to produce more than was required, by working harder and more efficiently, thus strengthening the socialist state. [...]  The movement eventually encountered resistance as the increased productivity led to increased demands on workers.

But note it assumes some kind of political motivation on behalf of the worker... In a more "normal" capitalist setting, that could be substituted with other hopes/goals, e.g. not being (the one) fired or maybe even being promoted... but I am extrapolating.
I'll also say that the term is somewhat unrelated to how Stakhanov actually improved
productivity (not only of his own, but of his mine). His innovation was no different in what it involved than what Ford or McDonalds did for other kinds production: a combination of
task sequencing (pipelining) [more] specialized workers and using new technology, although the Stakhanovite movement in the Soviet Union later involved training workers in highly specialized roles, in view of increasing their individual productivity. Stakhanov himself was promoted to mine management, so even in the Soviet Union you can't say there were no individualized incentives.
It's even been claimed that Stakhanovism was merely the Soviet form of Taylorism, however this
ignores that Taylor proposed a clear process for deriving quotas, piece-rate (with a subunitary bonus for over-quota), and firing the least
productive workers continuously, while Stakhanovism in practice had a haphazard approach to these. As far as terminology goes, Taylor only named the opposite behavior, i.e. "soldiering", which he defined as working at the slowest pace that goes unpunished.
There may actually be some terms in Japanese that more or less equate "Stakhanovite" in a different context, namely

The victims of
karoshi are known in their companies as moretsu shain (fanatical
workers) and yoi kigyo senshi (good corporate soldiers).

cf. Organizational Behavior and Management, 9th ed., p. 252. (I'm not really familiar with Japanese work slang and a bit of searching finds that at lest "moretsu shain" can have some positive meanings as well, so take that with a grain of salt.)  Another (English) term given in this book (p. 288) that's probably more clear is

rate-busters (Producing above the group norm of acceptance).

Oxford Reference actually links this term with Stakhanovite

An employee who is highly productive and exceeds the formally agreed rate of output for the particular task. Whilst this is advantageous for management, rate-busters are usually disliked by their colleagues because their action provides managers with the excuse to raise the rate of output for all the other employees. Typically, there is informal social regulation of work in most workgroups where rate-busting is deemed antisocial behaviour and potential rate-busters are brought into line by their work colleagues through a mixture of persuasion and coercion. [See Stakhanovite.]

The Merriam-Webster definition for the same term more clearly links it to a reduction in income for co-workers, albeit in a per-piece rather than salary setting:

a pieceworker who produces to the utmost of his ability despite opposition by his fellows who fear that his high earnings may cause a reduction in the piece rate.

A similar term that in even more specific circumstances may be "strikebreaker".
If you go just by dictionary defs, you'd think rate-busting is always
viewed negatively by co-workers, but that's not really the case, see e.g.
a case study done at a warehouse, which involved a gamification aspect to a certain extent, but which also had loads of "standard" Taylorism elements.


Extensive Labor Margin and Full Insurance Extensive margin models use Rogerson (1988) style lotteries in order to simplify the savings problem under non-full-insurance.

However, one could still write down the model in that style but instead of assuming full insurance (that is, marginal utility of consumption equalized among all individuals in the household, no matter whether working or not), do it with partial insurance, in order to match the data better.

I however have not seen this approach so far. Did I miss something? Or what would be arguments against this approach? Besides the obvious one: If you don't have full insurance but forbid savings at the individual agent level, you are an evil person.

I'm not very well versed in macro-labor, but I'm aware of a few papers that might be close to what you mean. One could make unemployment uninsurable, and instead let agents partially self-insure by saving capital, like in Chang & Kim (2007) AER paper. Also Krusell & coauthors have some papers (like 2010 ReStud) where they combine uninsurable unemployment with search/matching framework.

The obvious problem is that dealing with heterogeneity is painful, because one must keep track of distribution of variables across agents (e.g. employment status and savings). If you're interested in computation and what can go wrong with it, check out comment on Chang & Kim paper by Takahashi (as well as reply by original authors in the same issue).


Why are the financial intermediation theory of banking or fractional reserve theory of banking still accepted despite evidence to the contrary? In most textbook economics I came across the financial intermediation theory of banking and the fractional reserve theory of banking are presented as theories as how money is created.
However there is evidence that these theories are wrong. See for example
Economist A. Werner writes in his empirical case study:

An empirical test is conducted, whereby money is borrowed from a
cooperating bank, while its internal records are being monitored, to
establish whether in the process of making the loan available to the
borrower, the bank transfers these funds from other accounts within or
outside the bank, or whether they are newly created. This study
establishes for the first time empirically that banks individually
create money out of nothing. The money supply is created as ‘fairy
dust’ produced by the banks individually, "out of thin air".

The study even includes a letter signed by the manager of the bank in which Werner did his experiments:

Dear Prof. Dr. Werner,

Confirmation of Facts

In connection with the extension of credit to you in August 2014 I am pleased to confirm that neither I as director of Raiffeisenbank Wildenberg eG, nor our staff checked either before or during the granting of the loan to you, whether we keep sufficient funds with our central bank, DZ Bank AG, or the Bundesbank. We also did not engage in any such related transaction, nor did we undertake any transfers or account bookings in order to finance the credit balance in your account. Therefore we did not engage in any checks or transactions in order to provide liquidity.

Yours sincerely,

M. Rebl,

Director, Raiffeisenbank Wildenberg e.G.

Similar claims are made by Kumhof and Jakab published by the IMF (International Monetary Fund):

Specifically, virtually all recent mainstream neoclassical economic research is based on the highly misleading “intermediation of loanable funds” description of banking, which dates to the 1950s and 1960s and back to the 19th century.We argue instead for the “financing through money creation” description, which is consistent with the 1930s view of economists associated with the Chicago School. These two views have radically different implications for a country’s macroeconomic response to financial and other shocks. This in turn has obvious relevance for key policy choices today.
Lending, in this narrative, starts with banks collecting deposits of previously saved real resources (perishable consumer goods, consumer durables, machines and equipment, etc.) from savers and ends with the lending of those same real resources to borrowers. But such institutions simply do not exist in the real world.

There are several reason for it. Please note the reasons are not necessarily listed in order of importance, the last point is actually most relevant answer to your question.

The 'Evidence' You Cite is Controversial

First, in fact the work you cite itself states that this issue is matter of ongoing controversy (Werner, 2014). So it is far from settled.
However, what even more the empirical 'test' of Werner (2014) is itself actually quite controversial and has been criticized heavily  for confusing accounting with economics (e.g. see Rendahl & Freund 2019 or Spearman 2016).  Accounting and economics are completely distinct disciplines so it is often not possible to conclusively test economic theory just by accounting arguments.
For example, a theory of perfect competition predicts that in equilibrium there will be no economic profit. However, you can't test whether firm has zero economic profit by looking at the firm's profit and loss statements because accounting does not capture economic reality and does not include things like opportunity cost which could make profit zero. Depreciation in accounting is also treated in a way that economically has almost no sense etc. As a result even if in real life there would be some perfectly competitive industry you cannot test for that just by looking at firm's profit and loss statement but you need better more nuanced test.
In a similar way the paper by Werner is criticized for making essentially the same error of assuming that just because on the balance sheet money seemingly appear out of nothing that means that banks actually can create them out of nothing, and as explained in greater detail in Rendahl & Freund (2019), that is to a degree just accounting fiction and does not really prove that banks do not create money against some assets as understood in economics (as opposed to what accounting understands them to be - which again is completely separate discipline with its own terminology and very little relation to economics).
Next, when it comes to financial intermediation, it would again be quite controversial to state that banks do not engage in some financial intermediation. Even McLeay et. al. (2014) argue financial intermediation plays some role in money creation. Now this is not the same as claiming the authors endorse financial intermediation theory the point is that stating that banks do not engage in financial intermediation at all would be controversial.
Textbooks are generally not being rewritten if one controversial paper is published. It takes time for literature to settle. Even if the new controversial paper is entirely correct it can take few years before that is reflected in textbooks (especially undergraduate ones graduate textbooks are much more up to date).

All Models are Wrong but Some are Useful (George Box)

As the title of this section says all models are wrong, you can imagine model as being a map. Any map that is not 1:1 replica of landscape will be wrong in some way, however having 1:1 replica of landscape as a map ceases to be model and quite frankly it is completely useless even though it is 100% correct. Both 'fractional reserve theory' and 'financial intermediation theory' have their utility.
Starting with 'fractional reserve theory'.  For example, before 2009 when central banks started engaging in 'unconventional' monetary policy,  the 'fractional reserve theory' was quite useful for back of the envelope calculations. This is because pre-2008 commercial banks held virtually no excess reserves as you can see from the data provided by FRED below:

In such situation, the 'fractional reserve theory' can be actually quite useful and reasonable approximation. As you can see from graphs below, the $M_2/M_1$ ratio and amount of reserves before 2008 tended to move in same direction.

In fact if you remove observations post 2007 and calculate correlation between $M_2/M_1$ and required reserves you would find the correlation to be significant with point estimate approximately  $0.55$ (with $95\%$ conf.  interval (rounded to 2 significant digits) $[0.49, 0.60]$)*. Sure simple correlation is no rigorous test, but with such high correlation it is difficult to claim that there is no relationship between reserves and ratio of $M_2/M_1$ meaning that at least in past money were created in a way that looks like a situation where banks just multiplied reserves (again I am not saying this is necessary rigorous test, one can invoke reverse causality and so on,  point is that its not an unreasonable model for a first approximation).
Now for sure utility of the 'fractional reserve theory' diminishes greatly under the new unconventional monetary policy, and especially now that many central banks around the world (but not all) completely abolished reserve requirement altogether (see Fed explainer on its abolishment of reserve requirements here). However, it is not clear if this is new normal. These policies are still referred to in the literature as 'unconventional' and  it is not clear if they will last. In fact before the Covid-19 hit world economy, Fed was already contemplating of returning to normal policies (e.g. see news articles pre-Covid such as this one). Consequently, an argument can be made that this model is useful for students to understand because it described banking relatively (relative to its simplicity) well in the past, and it is not so clear if the current unconventional monetary policies that make the model give very wrong predictions will last.  You should also note, that it is not like this is the only model textbooks discuss. For example, Blanchard et al Macroeconomics: A European Perspective pp 71 described failures of simple multiplier model and alternative endogenous money supply theories already in 2013.
When it comes to 'financial intermediation of banking' theory this one has still its uses even contemporary empirical research (See discussion in Freixas & Rochet Microeconomics of Banking). Consequently, when it comes to this theory it is even more useful for students to know, even if you can of course criticize it and competing theories exist.

Both Theories are Important for Didactic Reason

Since we are discussing textbooks and not applied research it is important to realize that textbooks, beyond anything else, serve didactic purpose. They are intended to be used as teaching tool, and especially undergraduate textbooks (which is where you will see 'fractional reserve theory' and 'financial intermediation theory' given most space) have to build foundations for future study.
For example, every single intro physics textbook in existence will teach Newtonian physics, even though we now know that Newtonian physics is wrong and at best special case of general relativity. Yet learning incorrect Newtonian physics is still useful as it can be used for back of the envelope calculation in many situations  and it will still give reasonably good answers. As shown under point 2 even simple 'fractional reserve theory' provides quite reasonable predictions (although it is definitely not as close as Newtonian theory to general relativity so please do not take this analogy too far).
This is like asking why textbooks typically include linear demand and supply when in reality demand and supply is almost never linear. Well answer is that solving models with simple linear demand and supply is easy for students and facilitates learning, as it serves as a stepping stone to understanding more complex models. For example, in typical graduate microeconomic textbook you will virtually never see linear demand or supply save for some rare exceptions, yet if you would try to teach 101 econ students from graduate textbooks such as MWG Microeconomic Theory or Varian Microeconomic Analysis, all but the top $5\%$ of class would fail miserably.  Again you would end up in analogous situation if you would start physics 101 with  Einstein's  field equations. Anyone except for top students would not be able to follow. As a result you will fail in your task to educate students, since even students that might become excellent physicist when they learn physics in small steps instead of trying to directly tackle graduate level physics, would never be able to achieve their full potential if you would just stress teaching more realistic but infinitely more complex models. Again this does not mean such models should not be taught but there is plenty of room for that in graduate courses.
Even McLeay et al (2014) who very harshly criticize 'fractional reserve theory' argue it can be a

useful way of introducing money and banking in economic textbooks,

Next, it is also important to understand that both 'fractional reserve theory' and 'financial intermediation theory' are part of exogenous money supply theory (a theory where private banks are passive agents that just expand/contract money supply in a way that is exogenously dictated by central bank), and regardless whether you consider exogenous money supply theory right or wrong (I personally think exogenous money supply theory is far from completely correct), learning exogenous money supply theory is important part in learning endogenous moneys supply theory (which is theory where private banks have active role in money supply creation).
This is because as discussed earlier, even authors who are proponents of endogenous money supply theory such as McLeavy et al (2014), do not deny that banks are constrained by central bank's policy and that some financial intermediation is taking place.
The difference between exogenous money supply theory and endogenous money supply theory is that under exogenous money supply theory the chain of causality goes just from central bank policy to money supply, whereas under endogenous money supply theory there is relationship that goes both ways (see more details on this in above mentioned Blanchard et al). Consequently,  endogenous money supply models will still feature central banks policy be it via reserves or more often other ways that affect money supply like bank regulation/interest rates etc,  and will still often have banks that still take in deposits. Rather under endogenous money supply theory there will be additional relationships where money demand will cause banks to lend more and in turn banks will then create more reserves at the central bank (like described in McLeavy et al).
Consequently, even if you think exogenous money supply theory is completely wrong, it is didactically better to first teach exogenous money supply theory, and then explain to students that this is wrong because there are additional channels that lead to simultaneity/reverse causality and build your explanation of endogenous money supply theory based on exogenous money supply theory. Jumping directly to endogenous money supply theory would likely just facilitate less learning and create more confusion even if your goal is to teach only endogenous money supply theory.
The above alone is the most important reason why these models heavily feature and will likely continue to feature in most 101 macro textbooks in foreseeable future. However, you should note that the space most mainstream macro textbooks devote to exogenous money supply models has shirked substantially, you can verify that by comparing the latest editions of Mankiw Macroeconomics or Blanchard et al Macroeconomics to their earlier editions.   Nowadays, you will get quite more space devoted to endogenous money supply theories (however, do not confuse endogenous money supply theory with controversial assertions of Werner (2014) of money being created completely ex-Nihilo by private banks - that is very controversial compared to just general endogenous money supply theory).

* Code for the correlation calculation in the spoiler:

 #data are obtained from fred data linked above, data after 2007 are deleted, data were merged in excel before runing the code (with raw output below):
 cor(fred$M2SL_M1SL, fred$REQRESNS, method = c("pearson"))
 > [1] 0.54867
 cor.test(fred$M2SL_M1SL, fred$REQRESNS, method=c("pearson"))
 > Parson's product-moment correlation
   data:  fred$M2SL_M1SL and fred$REQRESNS
   t = 15.887, df = 586, p-value < 2.2e-16
  alternative hypothesis: true correlation is not equal to 0
 95 percent confidence interval:
  0.4895299 0.6027855


How can I motivate a dynamic model using utility and consumer preference where only one good is affected by past consumption? I've been looking how I can motivate the model I want to estimate using a utility optimization framework. Basically, I want to write a model using two goods: x and y, in two periods: 1 and 2, where the optimal amount of $x_2$ is negatively affected by the amount $x_1$ that was consumed in the previous period. For example, a person's preference between Italian (x) and Thai (y) restaurant at period t should depend on whether or not a person Italian food at period t-1, so consuming $Italian_{t-1}$ should negatively affect the optimal amount of $Italian_{t}$ by some proportional amount. The nature of the problem rules out the Discounted Utility Model because it assumes consumption independence. I read about alternative such as habit forming models,but these models describe the opposite of what I want and can lead to intractable. Any reference or help will be greatly appreciated.

I just wanted to clarify on Starfall's comment a bit. $\phi$ can be a bit confusing.

Let me appeal to Chris Carroll who uses the CRRA utility function $v(\frac{c}{x^{\gamma}})$ where $c$ is consumption, $x$ is the habit, and $\gamma$ is the "importance" of habits. In this framework, $\phi$ can be seen as the speed at which habits catch up to consumption. If $\phi = 0$, then the consumer is constantly maximizing their utility with a constant multiplicative factor in the denominator of the function's argument. If $\phi=1$, then habits collapse to last period's consumption.

There are a number of interesting features from this model. For instance, if persistence is low enough then the intertemporal elasticity of substitution tends to zero. It is used famously in the asset pricing literature to explain the excess return premium puzzle. The development of habit formation theory in microeconomics (both theory and econometrics) is slow but finally arriving.

Resources:
http://www.econ2.jhu.edu/people/ccarroll/HabitsEconLett.pdf
http://www.isaacbaley.com/uploads/6/7/3/5/6735245/lecture_7_baley.pdf