Talk:Instrumental variables estimation

message: It's great that an example is provided, but it is not very clear and does help someone who is still learning about econometrics to understand this topic. Improving this example and adding others would be great. Thanks! ChelseaH (talk) 06:55, 4 April 2009 (UTC)
thread_title: Better and more examples needed

message: I think it would be a good idea to combine the two pages, as they are on the same topic, altho one is a simple algebraic derivation of the IV estimator and the other is a more discursive presentation of the purposes and assumptions underpinning IV estimation.  The two pages are quite complementary.
I agree, merge them.
SCB
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There is an important subtlety that is missing from the discussion, and is even often missing in texts.  OLS still provides an excellent predictor of response under the current data generating process (DGP).  In other words, if I know market price and I want to predict market quantity, OLS does a fine job.  On the other hand, if I want to recover the structural equations, supply and demand, I need 'IV'.  That allows me to predict quantity given an exogenous shock to price, such as a tax.  So, when we say a consistent estimator, it's a little ambiguous.  We ought to be saying what we want to estimate consisistently.
Just to be clear, suppose we are trying to guess future earning based on IQ.  However, IQ is measured with an additive error.  Suppose Jim's measured IQ is 100 and I want to guess his future earnings.  I'll do just fine plugging his measured IQ into an OLS estimate, so long as the measurement error for him follows the same DGP as the data for the regression.  On the other hand, if I want to know the impact of giving Jim a pill that increases his true IQ by 10 points, then I need an IV estimator.  That's because the slope coefficient is biased towards zero to adjust for the measurement error.  But, I am adjusting the IQ by a known amount, and thus want to have the true slope coefficient for true (not measured) IQ.
This may be too fine a point for the article, but it's one I often see misunderstood in papers.  The ambiguity being what we are trying to estimate: a structural model, or a good predictor under the current DGP. Derex @ 23:55, 8 October 2005 (UTC)
replies: text: I take it you're referring to the text about the simple IV?  Both are right, depending on the experimental assumptions.  If X is viewed as fixed in repeated samples, then it is unbiased.  If X is viewed as random, then IV is consistent but likely biased.  The latter is the realistic case, though the former is often presented in introductory texts.  Probably should change it though, as "consistent" is always correct. Derex 02:54, 1 April 2006 (UTC)
thread_title: ?

message: what are the corresponding regression equation for each stage? This is very unclear. 
* stage 1: Xi=Zb+residual   we get Xihat=Zbhat=Z(Z'Z)^(-1)Z'Xi.
:Xhat=(Xihat ...)         
* stage 2: y=Xhat*beta+residual.
Jackzhp (talk) 04:30, 25 March 2008 (UTC)
thread_title: 2 stage?

message: The discussion under "hypothesis testing" is simply wrong.  The first moment of the simple IV estimator doesn't exist, therefore, the estimator is neither biased nor unbiased.  The normality result is asymptotic; in small samples the coefficients are not normally distributed, and the t-ratio does not follow a Student distribution.  Since this section is superfluous it would be wise to delete it.
A minor problem is the second sentence is incoherent: ``endogeneity" means that the error term and regressors are correlated, so the piece should not claim that one reason that the error term and the covariates may be correlated is endogeneity!
 
68.146.25.175 (talk) 22:28, 20 April 2008 (UTC)
thread_title:  Hypothesis testing 

message: I have substantially overhauled this page.  I entirely rewrote the previous hypothesis testing and "testable implications" sections, which were, as noted above, simply wrong.  An example has been added (this material could be expanded and moved to a new section), the bits on weak and invalid instruments have been largely rewritten, a misleading claim about nonlinear models has been modified, and finite sample properties and overidentification tests are discussed.  Several references to influential papers have been added.  Some of the previous material could be better referenced, and a few more examples would help illustrate ideas.  —Preceding unsigned comment added by 68.146.25.175 (talk) 19:21, 22 July 2008 (UTC)
thread_title: Major edit

message: The start of the "Estimation" section begins with a specification of where the data come from.  Describing that equation as the "model" may be misleading, as we specifically do not mean that this is the approximation we are using to model the data but rather the actual process generating the data.  The best descriptive would be the technical term "data generating process."  There is no WP page on that term, however. Somewhat more opaque would be referring to the equation as the "population process."   A correct but more generic substitute which actually has a WP page would be "stochastic process."  It is not "STANDARD" (I see no call to YELL at me) to fail to distinguish between the estimable model and the data generating process. 
Sked123 (talk) 16:00, 7 August 2008 (UTC)
thread_title:  Estimation: describing the DGP 

message: The section "Example" ends with a paragraph that is a discussion of the limitations of the IV approach.
:Because demonstrating that the third variable 'z' is causally related to 'y' exclusively via 'x' is an experimental impossibility, and because the same limitations...
Should this be moved to a different section? If so, which?
Blossomonte (talk) 06:59, 15 January 2014 (UTC)
thread_title:  Paragraph on limitations seems out of place in "Example" Section. 

message: The term "Instrumental variable" is either poorly defined, or else not defined at all.
The first sentence in the lede says what the method is used for. The second sentence says what the method allows you to do (and, worse, says this by jumping immediately into mostly incomprehensible technical jargon.) Neither section says what an instrumental variable actually is.
There is, later, a section called "definition", but it starts out as history, and then moves to math that does not really define an instrumental variable (it starts by labeling an instrumental variable as "Z" which is defined in terms of an equation which does not include Z)... then goes on to say that this definition is confusing and ambiguous. Yes, I agree. 
In simple language: what is an instrumental variable? Is it a proxy? Is it what you read out from an instrument?Geoffrey.landis (talk) 17:03, 9 November 2015 (UTC)
thread_title:  Never defined 

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*Reference : Angrist, Joshua & Chen, Stacey, 2008. "Long-Term Economic Consequences of Vietnam-Era Conscription: Schooling, Experience and Earnings," IZA Discussion Papers 3628, Institute for the Study of Labor (IZA).
ExpertIdeasBot (talk) 03:01, 28 May 2016 (UTC)
thread_title:  Dr. Angrist's comment on this article 

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thread_title:  Dr. Santos Silva's comment on this article 

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*Reference : Stanislav Anatolyev & Nikolay Gospodinov, 2008. "Specification Testing in Models with Many Instruments," Working Papers w0124, Center for Economic and Financial Research (CEFIR).
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thread_title:  Dr. Anatolyev's comment on this article 

message: Dr. Carrasco has reviewed this Wikipedia page, and provided us with the following comments to improve its quality:
} {\vec {\beta }} not based on whether x {\displaystyle x} x is uncorrelated with u {\displaystyle u} u, but based on whether another variable z {\displaystyle z} z (or set of variables) is (are) uncorrelated with u {\displaystyle u} u."
There should be no arrow on beta.
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*Reference : Marine Carrasco & Rachidi Kotchoni, 2013. "Efficient Estimation Using the Characteristic Function," Working Papers hal-00867850, HAL.
ExpertIdeasBot (talk) 15:29, 24 August 2016 (UTC)
thread_title:  Dr. Carrasco's comment on this article 

message: The smoking/general health example, and the proximity/tutoring example seem to be a superficial rewrite of the examples on this page:
http://www.statisticshowto.com/instrumental-variable/
Which uses smoking/depression and proximity/community centre. 
Sentence after sentence matches.  — Preceding unsigned comment added by 130.113.148.220 (talk) 21:33, 22 February 2017 (UTC)
thread_title:  Close paraphasing in the article? (plaigarism) 

message: A group of PhD students were made to write a number of articles on econometrics topics. Encyclopedia quality is poor and they were blocked for not disclosing that they made paid edits, but to make at least some use of their work maybe someone can look at Draft:Three-Stage Least Squares Estimator and see what if any of it can be used. Antimanipulator (talk) 16:28, 28 September 2017 (UTC)
thread_title:  3SLS 

message: The same group from #3SLS also wrote Endogeneity with an exponential regression function, which contains salvageable material on instrumental variables estimation in Poisson regression. I suggest that the usable material be merged into a new section here. Wikiacc (¶) 14:57, 22 May 2019 (UTC)
replies: text: Pretty ugly stuff. Nevertheless, support merge and suggest some refinement in situ.  Klbrain (talk) 07:06, 6 July 2020 (UTC)
thread_title:  Endogeneity with an exponential regression function 

message: As a result of the discussion above, I have revised the merge proposal to the following: Binary response model with continuous endogenous explanatory variables and #Poisson regression both get merged to a new section in control function (econometrics) about residual inclusion methods in nonlinear models. Wikiacc (¶) 02:44, 5 August 2020 (UTC)
replies: text: At the binary response model article, there is a method estimated purely by conditional MLE. Separating that from the residual inclusion method seems silly. Therefore, I suggest the following revision. Instead of control function (econometrics), how about making the target a new article? Say, nonlinear simultaneous equations model (by analogy with simultaneous equations model). This new page could also contain material on the "forbidden regression" (see above) and other related topics, focusing more on the setting than on the particular method. Wikiacc (¶) 03:33, 14 August 2020 (UTC)
replies: text: Scratch that new proposal—too ambitious. I have moved the Poisson regression section to control function (econometrics). Not exactly sure what to do with Binary response model with continuous endogenous explanatory variables. Wikiacc (¶) 04:29, 11 December 2020 (UTC)
thread_title: Rebooted merge proposal