a dynamic real option based investment model for renewable energy portfolios this work proposes a dynamic model to devise the optimal risk averse investment policy in a portfolio of complementary renewable sources for a generation company in the brazilian power system the proposed method merges a static energy contracting model based on a hybrid robust and stochastic optimization approach with a mean reverting binomial lattice model for real option valuation the proposed merge extends previous works by providing support to risk averse investment decisions in complementary renewable sources dynamically distributed over time the most important results of the model are how much capacity to invest or build from each renewable source how much to sell from the energy portfolio in bilateral contracts and the optimal timing to invest unlike previous reported works our model takes into account three classes of uncertainties simultaneously renewable production of candidate sources and prices in the spot and contract markets a case study with realistic data from the brazilian power system is presented to illustrate the value of our model

debt regimes and the effectiveness of monetary policy abstract this paper develops a medium scale non linear model of the us economy our proposed model a threshold vector autoregression with stochastic volatility assumes that changes in government debt to gdp ratios drive the transition between regimes capturing low and high debt regimes the introduction of hierarchical priors enables us to flexibly shrink the empirical model towards the moments implied by a dynamic stochastic general equilibrium model in the empirical application we analyze regime specific monetary policy shocks our findings indicate that the effect of monetary policy is less pronounced in high debt regimes pointing towards differences in the underlying monetary policy transmission mechanisms forecast error variance decompositions enable us to shed further light on the relative importance of monetary policy shocks within different debt regimes in terms of explaining the variance of forecast errors

investment and upgrade in distributed generation under uncertainty the ongoing deregulation of electricity industries worldwide is providing incentives for microgrids to use small scale distributed generation dg and combined heat and power chp applications via heat exchangers hxs to meet local energy loads although the electric only efficiency of dg is lower than that of central station production relatively high tariff rates and the potential for chp applications increase the attraction of on site generation nevertheless a microgrid contemplating the installation of gas fired dg has to be aware of the uncertainty in the natural gas price treatment of uncertainty via real options increases the value of the investment opportunity which then delays the adoption decision as the opportunity cost of exercising the investment option increases as well in this paper we take the perspective of a microgrid that can proceed in a sequential manner with dg capacity and hx investment in order to reduce its exposure to risk from natural gas price volatility in particular with the availability of the hx the microgrid faces a tradeoff between reducing its exposure to the natural gas price and maximising its cost savings by varying the volatility parameter we find that the microgrid prefers a direct investment strategy for low levels of volatility and a sequential one for higher levels of volatility

fiscal policy interventions at the zero lower bound we build on a new keynesian dynamic stochastic general equilibrium dsge model to explore the macroeconomic consequences of fiscal expansionary shocks during the economic crisis of 2008 in the eurozone in this setting we find that the big four eurozone economies france germany italy and spain can effectively escape from their liquidity trap through fiscal policy interventions caused by government purchases we estimate the government spending multiplier to be above 1 8 when this policy is associated with a long term commitment to keeping the nominal interest rate at the zero lower bound as suggested by krugman 1998 notably the short term deficit effect on the budget balance can be offset five years after the implementation of a large spending program we also show that alternative policies with tax cuts that expand the supply do not appear to have the same power in the short run moreover we provide novel empirical evidence that a large government debt renders a government spending policy ineffective

real time density forecasts from bayesian vector autoregressions with stochastic volatility central banks and other forecasters are increasingly interested in various aspects of density forecasts however recent sharp changes in macroeconomic volatility including the great moderation and the more recent sharp rise in volatility associated with increased variation in energy prices and the deep global recession pose significant challenges to density forecasting accordingly this paper examines with real time data density forecasts of u s gdp growth unemployment inflation and the federal funds rate from bayesian vector autoregression bvar models with stochastic volatility the results indicate that adding stochastic volatility to bvars materially improves the real time accuracy of density forecasts this article has supplementary material online

evaluating natural resource investments notwithstanding impressive advances in the theory of finance over the past 2 decades practical procedures for capital budgeting have evolved only slowly the standard technique which has remained unchanged in essentials since it was originally proposed see dean 1951 bierman and smidt 1960 derives from a simple adaptation of the fisher 1907 model of valuation under certainty under this technique expected cash flows from an investment project are discounted at a rate deemed appropriate to their risk and the resulting present value is compared with the cost of the project this standard textbook technique reflects modern theoretical developments only insofar as estimates of the discount rate may be obtained from crude application of single period asset pricing theory but see brennan 1973 bogue and roll 1974 turnbull 1977 constantinides 1978 the inadequacy of this approach to capital budgeting is widely acknowledged although not widely discussed its obvious deficiency is its the evaluation of mining and other natural resource projects is made particularly difficult by the high degree of uncertainty attaching to output prices it is shown that the techniques of continuous time arbitrage and stochastic control theory may be used not only to value such projects but also to determine the optimal policies for developing managing and abandoning them the approach may be adapted to a wide variety of contexts outside the natural resource sector where uncertainty about future project revenues is a paramount concern

rescaling contraction with a lower cost technology when revenue declines abstract a mature oil field rescaled contraction describes a switch to a technological alternative more appropriate for the depleted state of an underlying resource off shore oil rigs are an illustration since the original technological scale designed for very large output flows becomes inappropriate as the operational efficiency declines later in life and facing a dwindling output flow so a more appropriate extraction technology becomes economic a real option representation is formulated on a stochastic oil price and deteriorating output volume we consider investment divestment decisions both separately and jointly which have different implications for government policies and also option values the resulting model yields analytical or semi analytical results indicating that immediate switching to the lower cost technology could sometimes be hastened as the price volatility increases depending on the current revenue if divestment and switching are considered jointly however greater volatility could also promote hysteresis

a new index of financial conditions we use factor augmented vector autoregressive models with time varying coefficients and stochastic volatility to construct a financial conditions index that can accurately track expectations about growth in key us macroeconomic variables time variation in the models parameters allows for the weights attached to each financial variable in the index to evolve over time furthermore we develop methods for dynamic model averaging or selection which allow the financial variables entering into the financial conditions index to change over time we discuss why such extensions of the existing literature are important and show them to be so in an empirical application involving a wide range of financial variables

time to build and aggregate fluctuations some new evidence this paper presents maximum likelihood estimates and tests of a model similar to one kydland and prescott 1982 suggested for this purpose it derives equilibrium laws of motion for a set of aggregate variables as functions of the model s parameters and the innovation to the technology shock the paper shows that a single unobservable index can explain the variability in the observed series but identifying the single index with the innovation to the technology shock implies that per capita hours is not well explained it also shows that time separable preferences with respect to leisure are consistent with the data copyright 1989 by economics department of the university of pennsylvania and the osaka university institute of social and economic research association

search theoretic models of the labor market a survey we survey the literature on search theoretic models of the labor market we show how this approach addresses many issues including the following why do workers sometimes choose to remain unemployed what determines the lengths of employment and unemployment spells how can there simultaneously exist unemployed workers and unfilled vacancies what determines aggregate unemployment and vacancies how can homogeneous workers earn different wages what are the tradeoffs firms face from different wages how do wages and turnover interact what determines efficient turnover we discuss various modeling choices concerning wage determination and the meeting process including recent models of directed search

policy predictions if the model does not fit abstract this paper uses a novel method for conducting policy analysis with potentially misspecified dsge models del negro and schorfheide 2004 and applies it to a simple new keynesian dsge model we illustrate the sensitivity of the results to assumptions on the policy invariance of model misspecifications jel classification c32 e5 key words bayesian analysis dsge models misspecification policy analysis 1 introduction despite recent successes in improving the empirical performance of dynamic stochastic general equilibrium dsge models e g smets and wouters 2003 even large scale dsge models su r er to some extent from misspecification see del negro schorfheide smets and wouters 2004 in this paper misspecification means that the dsge model potentially imposes invalid cross coe or cient restrictions on the moving average representation of the macroeconomic time series that it aims to explain as a consequence one typically observes that the forecasting performance of dsge models is worse than that of vector autoregressions vars estimated with well calibrated shrinkage methods on the other hand dsge models have the advantage that one can explicitly assess the e r ect of policy regime changes on expectation formation and decision rules of private agents thus policy analysis with dsge models is robust to the lucas critique and potentially more reliable than conclusions drawn from vars this trade o r poses a challenge to policymakers who want to use dsge models in practice del negro and schorfheide 2004a proposed a framework that combines vars and dsge models extending earlier work by ingram and whiteman 1994 in this framework dsge model restrictions are neither completely ignored as in the unrestricted estimation of vars nor are they dogmatically imposed as in the direct estimation of dsge models instead the var estimates are tilted toward the restrictions implied by the dsge model where the degree of tilting is determined by a bayesian data driven procedure that trades o r model fit against complexity del negro and schorfheide 2004a show that priors arising from the same model used in this paper improve both the in sample and out of sample fit of a var in ongoing research del negro and schorfheide 2004b we build upon our earlier work and further develop procedures that are suitable to study the e r ects of rare regime shifts with potentially misspecified dsge models these procedures can be viewed as a bayesian alternative to the robust control and minimax approaches that recently have been proposed to cope with model misspecification e g hansen and sargent 2000 and onatsky and stock 2002 one advantage of bayesian procedures is that the policymaker can learn from existing data about the extent of the dsge model s misspecification and consequently adjust her policies the present paper applies these procedures to a simple new keynesian dsge model we illustrate that conclusions about the e r ects of changing the response to inflation are sensitive to assumptions about the policy invariance of observed discrepancies between model and reality section 2 briefly describes the dsge model in section 3 we outline our framework section 4 discusses our findings and section 5 concludes 2 the dsge model starting point is a dsge model in log linearized form the model used here is a standard new keynesian dsge model e g woodford 2003 which we now briefly describe see del negro and schorfheide 2004a for details the log linearized equilibrium conditions consist of three equations in nominal interest rates sub t output sub t and inflation sub t sup denotes percentage deviations from the steady state and delta is the temporal difference operator monetary policy rule sub t rho r sub t 1 1 rho r psi sub 1

international evidence on sticky consumption growth abstract this paper estimates the degree of stickiness in aggregate consumption growth sometimes interpreted as reflecting consumption habits for thirteen advanced economies we find that after controlling for measurement error consumption growth has a high degree of autocorrelation with a stickiness parameter of about 0 7 on average across countries the sticky consumption growth model outperforms the random walk model of hall 1978 and typically fits the data better than the popular mankiw 1989 model though in a few countries the sticky consumption growth and campbell mankiw models work about equally well

wage posting in two sided search and the minimum wage this article focuses on wage formation in an equilibrium two sided model of search with match specific heterogeneity despite a large number a continuum of employers search provides sufficient isolation to generate market power by posting wages employers without collusion capture most of the surplus that accrues to any match the equilibrium wage is below that which maximizes employment an example using calibrated parameter values is used to reconcile some recent otherwise contradictory empirical results on the employment effects of minimum wage adjustments

testing for indeterminacy an application to u s monetary policy reply this paper considers a prototypical new keynesian model in which the equilibrium is undetermined if monetary policy is passive the likelihood based estimation of dynamic equilibrium models is extended to allow for indeterminacies and sunspot fluctuations we construct posterior weights for the determinacy and indeterminacy region of the parameter space and estimates for the propagation of fundamental and sunspot shocks according to the estimated model u s monetary policy post 1982 is consistent with determinacy whereas the pre volcker policy is not we find that before 1979 indeterminacy substantially altered the propagation of shocks

the new keynesian phillips curve and lagged inflation a case of spurious correlation the new keynesian phillips curve nkpc specifies a relationship between inflation and a forcing variable and the current period s expectation of future inflation most empirical estimates of the nkpc typically based on generalized method of moments gmm estimation have found a significant role for lagged inflation producing a hybrid nkpc using u s quarterly data this article examines whether the role of lagged inflation in the nkpc might be due to the spurious outcome of specification biases like previous investigators we employ gmm estimation and like those investigators we find a significant effect for lagged inflation we also use time varying coefficient tvc estimation a procedure that produces consistency under a variety of sources of misspecification using two separate measures of expected inflation we find strong support for the view that under tvc estimation the coefficient on expected inflation is near unity and that the role of lagged inflation in the nkpc is spurious

cross sectional efficiency and labour hoarding in a matching model of unemployment we study positive and normative aspects of steady state equilibrium in a market where firms of endogenous size experience idiosyncratic shocks and undergo a costly search process to hire their workers the stylized model we propose highlights interactions between job security provisions and sectoral shocks in determining the natural rate of unemployment the allocation of labour and the extent of labour hoarding and rationalizes cross sectional asymmetries of gross employment flows at the firm level in our model where productivity and search costs are dynamically heterogeneous across firms decentralized wage bargains imply important cross sectional inefficiencies which overshadow the static search inefficiencies on which simpler models focus

ces technology and business cycle fluctuations we contribute to an emerging literature that brings the constant elasticity of substitution ces specification of the production function into the analysis of business cycle fluctuations using us data we estimate by bayesian maximum likelihood methods a standard medium sized dsge model with a ces rather than cobb douglas cd technology we estimate a elasticity of substitution between capital and labour well below unity at 0 15 0 18 in a marginal likelihood race ces decisively beats the cd production and this is matched by its ability to fit the data better in terms of second moments we show that this result is mainly driven by the implied fluctuations of factor shares under the ces specification the ces model performance is further improved when the estimation is carried out under an imperfect information assumption hence the main message for dsge models is that we should dismiss once and for all the use of cd for business cycle analysis

a method for taking models to the data abstract this paper develops a method for combining the power of a dynamic stochastic general equilibrium model with the flexibility of a vector autoregressive time series model to obtain a hybrid that can be taken directly to the data it estimates this hybrid model via maximum likelihood and uses the results to address a number of issues concerning the ability of a prototypical real business cycle model to explain movements in aggregate output and employment in the postwar us economy the stability of the real business cycle model s structural parameters and the performance of the hybrid model s out of sample forecasts

gas fired power plants investment timing operating flexibility and co2 capture we analyze investments in gas fired power plants based on stochastic electricity and natural gas prices a simple but realistic two factor model is used for price processes enabling analysis of the value of operating flexibility the opportunity to abandon the capital equipment as well as finding thresholds for energy prices for which it is optimal to enter into the investment we develop a method to compute upper and lower bounds on plant values and investment threshold levels our case study uses representative power plant investment and operations data and historical forward prices from well functioning energy markets we find that when the decision to build is considered the abandonment option does not have significant value whereas the operating flexibility and time to build option have significant effect on the building threshold furthermore the joint value of the operating flexibility and the abandonment option is much smaller than the sum of their separate values because both are options to shut down the effects of emission costs on the value of installing co2 capture technology are also analyzed

using regional variation in wages to measure the effects of the federal minimum wage the imposition of a national minimum wage standard provides a natural experiment in which the treatment effect varies across states depending on the fraction of workers initially earning less than the new minimum the author exploits this fact to evaluate the effect of the april 1990 increase in the federal minimum wage on teenagers wages employment and school enrollment comparisons of grouped and individual state data confirm that the rise in the minimum wage increased teenagers wages there is no evidence of corresponding losses in teenage employment or changes in teenage school enrollment

optimal technology choice and investment timing a stochastic model of industrial cogeneration vs heat only production abstract in this paper we develop an economic model that explains the decision making problem under uncertainty of an industrial firm that wants to invest in a process technology more specifically the decision is between making an irreversible investment in a combined heat and power production cogeneration system or to invest in a conventional heat only generation system steam boiler and to purchase all electricity from the grid in our model we include the main economic and technical variables of the investment decision process we also account for the risk and uncertainty inherent in volatile energy prices that can greatly affect the valuation of the investment project the dynamic stochastic model presented allows us to simultaneously determine the optimal technology choice and investment timing we apply the theoretical model and illustrate our main findings with a numerical example that is based on realistic cost values for industrial oil or gas fired cogeneration and heat only generation in switzerland we also briefly discuss expected effects of a co 2 tax on the investment decision

gdp nowcasting application and constraints in a small open developing economy despite data limitations an attempt is made to find out if a gdp nowcasting model can provide reliable forecasts for a small open economy two competing bayesian vector autoregressive models are tested rigorously to obtain the optimal model by minimizing in sample forecasting errors the main finding of this study is that gdp nowcasting can produce reliable results for a small open economy despite the unavailability of sufficient data sets and the lack of high frequency indicators

the excess sensitivity of layoffs and quits to demand excessive layoffs in bad times and excessive quits in good times both stem from the same weakness in practical employment arrangements the specific nature of worker firm relations creates a situat

the dynamic effects of aggregate demand and supply disturbances we interpret fluctuations in gnp and unemployment as due to two types of disturbances disturbances that have a permanent effect on output and disturbances that do not we interpret the first as supply disturbances the second as demand disturbances demand disturbances have a hump shaped mirror image effect on output and unemployment the effect of supply disturbances on output increases steadily over time peaking after two years and reaching a plateau after five years

improving monetary policy models abstract if macroeconomic models are to be useful in policy making where uncertainty is pervasive the models must be treated as probability models whether formally or informally use of explicit probability models allows us to learn systematically from past mistakes to integrate model based uncertainty with uncertain subjective judgment and to bind data based forecasting together with theory based projection of policy effects yet in the last few decades policy models at central banks have steadily shed any claims to being believable probability models of the data to which they are fit here we describe the current state of policy modeling suggest some reasons why we have reached this state and assess some promising directions for future progress

mind your ps and qs improving arma forecasts with rbc priors abstract we use an rbc model to help inform arma parameters for univariate out of sample forecasting of us output bayesian estimation methods yield an interpretable structural model with forecasting performance at least competitive with arma models estimated with standard techniques

capacity switching options under rivalry and uncertainty abstract deregulated infrastructure industries exhibit stiff competition for market share firms may be able to limit the effects of competition by launching new projects in stages using a two stage real options model we explore the value of such flexibility we first demonstrate that the value of investing in a sequential manner for a monopolist is positive but decreases with uncertainty next we find that a typical duopoly firm s value relative to a monopolist s decreases with uncertainty as long as the loss in market share is high intriguingly this result is reversed for a low loss in market share we finally show that this loss in value is reduced if a firm invests in a sequential manner and specify the conditions under which sequential capacity expansion is more valuable for a duopolist firm than for a monopolist

structural dynamic factor analysis using prior information from macroeconomic theory dynamic factor models are becoming increasingly popular in empirical macroeconomics due to their ability to cope with large datasets dynamic stochastic general equilibrium dsge models on the other hand are suitable for the analysis of policy interventions from a methodical point of view in this article we provide a bayesian method to combine the statistically rich specification of the former with the conceptual advantages of the latter by using information from a dsge model to form a prior belief about parameters in the dynamic factor model because the method establishes a connection between observed data and economic theory and at the same time incorporates information from a large dataset our setting is useful to study the effects of policy interventions on a large number of observed variables an application of the method to u s data shows that a moderate weight of the dsge prior is optimal and that the model performs well in terms of forecasting we then analyze the impact of monetary shocks

monetary policy analysis and inflation targeting in a small open economy a var approach empirical monetary policy research has increased in the last decade possibly because deregulation and explicit monetary targets have made monetary policy issues more interesting in particular within the inflation targeting framework it has been argued that inflation forecasts can be used as optimal intermediate targets for monetary policy and the development of empirical models that have good forecasting properties is therefore important this paper shows that a var model with long run restrictions justified by economic theory is useful for both forecasting inflation and for analysing other issues that are central to the conduct of monetary policy copyright 2001 john wiley sons ltd

forecasting canadian time series with the new keynesian model this paper assesses the out of sample forecasting accuracy of the new key nesian model for canada we estimate a variant of the model on a series of rolling sub samples computing out of sample forecasts one to eight quarters ahead at each step we compare these forecasts with those arising from vector autoregression var models us ing econometric tests of forecasting accuracy we show that the forecasting accuracy of the new keynesian model compares favourably with that of the benchmarks particularly as the forecasting horizon increases these results suggest that the model could become a useful forecasting tool for canadian time series jel classification e32 e37

bayesian prior elicitation in dsge models macro vs micropriors abstract bayesian approaches to the estimation of dsge models are becoming increasingly popular prior knowledge is normally formalized either directly on deep parameters values microprior or indirectly on macroeconomic indicators e g moments of observable variables macroprior we introduce a non parametric macroprior which is elicited from impulse response functions and assess its performance in shaping posterior estimates we find that using a macroprior can lead to substantially different posterior estimates we probe into the details of our result showing that model misspecification is likely to be responsible of that in addition we assess to what extent the use of macropriors is impaired by the need of calibrating some hyperparameters

bayesian evaluation of dsge models with financial frictions we evaluate two most popular approaches to implementing financial frictions into dsge models the bernanke gertler and gilchrist 1999 setup where frictions affect the price of loans and the kiyotaki and moore 1997 model where they concern the quantity of loans we take both models to the data and check how well they fit it on several margins overall comparing the models favors the bernanke gertler and gilchrist framework however even this model does not make a clear improvement over the new keynesian benchmark in terms of marginal likelihood and similarity of impulse responses to those obtained from a var

evaluating real time var forecasts with an informative democratic prior this paper proposes bayesian forecasting in a vector autoregression using a democratic prior this prior is chosen to match the predictions of survey respondents in particular the unconditional mean for each series in the vector autoregression is centered around long horizon survey forecasts heavy shrinkage toward the democratic prior is found to give good real time predictions of a range of macroeconomic variables as these survey projections are good at quickly capturing endpoint shifts

comparing dsge var forecasting models how big are the differences abstract i generate priors for a vector autoregression var from a standard real business cycle rbc model an rbc model with capital adjustment costs and habit formation and a sticky price model with an unaccommodating monetary authority the response of hours worked to a tfp shock differs sharply across these models i compare the accuracy of forecasts made from each of the resulting dynamic stochastic general equilibrium vector autoregression dsge var models despite having different structural characteristics the dsge vars are comparable in terms of forecasting performance as in previous work dsge vars compare favorably with atheoretical vars

forecasting inflation in an inflation targeting economy structural versus nonstructural models abstractwe propose a comparison between atheoretical and theoretical models in forecasting the inflation rate for an inflation targeting country such as south africa in a pseudo real time environment our results show that for shorter horizons the atheoretical error correction models with and without factors perform better while for longer horizons theoretical dynamic stochastic general equilibrium based models outperform their competitors

the aggregate implications of regional business cycles during the last recession and its aftermath aggregate price growth and aggregate nominal wage growth remained robust despite the high rate of unemployment we document that u s states with weaker labor markets experienced lower price growth lower nominal wage growth and lower real wage growth during the great recession given that reliable measures of local prices do not exist we create our own state price indices using scanner data from the nielsen retail scanner database the main innovation of the paper is the development of an econometric procedure that combines cross region data with aggregate time series data to infer the underlying shocks driving both aggregate and regional business cycles applying our procedure to the great recession we find that a combination of both demand and supply shocks are necessary to account for the joint dynamics of aggregate prices wages and employment during the 2007 2012 period in contrast we find that only demand shocks are needed to explain the cross region variation the results suggest that only using cross region variation to explain aggregate fluctuations is insufficient when shocks do not have a substantive regional component

indivisible labor and the business cycle abstract a growth model with shocks to technology is studied labor is indivisible so all variability in hours worked is due to fluctuations in the number employed we find that unlike previous equilibrium models of the business cycle this economy displays large fluctuations in hours worked and relatively small fluctuations in productivity this finding is independent of individuals willingness to substitute leisure across time this and other findings are the result of studying and comparing summary statistics describing this economy an economy with divisible labor and post war u s time series

supplanting the minnesota prior forecasting macroeconomic time series using real business cycle model priors abstract although general equilibrium models are in wide use in the theoretical macroeconomic literature their empirical relevance is uncertain we develop procedures for using dynamic general equilibrium models to aid in analyzing the observed time series relationships among macroeconomic variables our strategy is based on that developed by doan litterman and sims 1984 who constructed a procedure for improving time series forecasts by shrinking vector autoregression coefficient estimates toward a prior view that vector time series are well described as collections of independent random walks in our case the prior is derived from a fully specified general equilibrium model we demonstrate that like the atheoretical random walk priors real business cycle model priors can aid in forecasting

multiple equilibria and minimum wages in labor markets with informational frictions and heterogeneous production technologies it is often argued that a mandatory minimum wage is binding only if the wage density displays a spike at it in this article we analyze a model with search frictions and heterogeneous production technologies in which imposition of a minimum wage affects wages even though after imposition the lowest wage in the market exceeds the minimum wage the model has multiple equilibria as a result of the fact that the reservation wage of the unemployed and the lowest production technology in use affect each other imposition of a minimum wage may improve social welfare

evaluating point and density forecasts of dsge models this paper investigates the accuracy of point and density forecasts of four dsge models for inflation output growth and the federal funds rate model parameters are estimated and forecasts are derived successively from historical u s data vintages synchronized with the fed s greenbook projections point forecasts of some models are of similar accuracy as the forecasts of nonstructural large dataset methods despite their common underlying new keynesian modeling philosophy forecasts of different dsge models turn out to be quite distinct weighted forecasts are more precise than forecasts from individual models the accuracy of a simple average of dsge model forecasts is comparable to greenbook projections for medium term horizons comparing density forecasts of dsge models with the actual distribution of observations shows that the models overestimate uncertainty around point forecasts

the macroeconomic effects of distortionary taxation abstract prescott 1986 estimates that technology shocks account for 75 of the fluctuations in the postwar u s economy this paper reestimates the contribution of technological change for a standard business cycle model that includes a public sector and fiscal disturbances i find that a significant fraction of the variance of aggregate consumption investment output capital stock and hours of work can be explained by disturbances in labor and capital tax rates and government consumption i also use the model to quantify the welfare costs of capital and labor taxation for both the time series and welfare calculations maximum likelihood estimates of taste technology and policy parameters are used

in search of the liquidity effect abstract a short run negative relationship between monetary aggregates and interest rates the liquidity effect is central to discussions of monetary policy this paper searches for this empirical relationship we investigate whether the characterization of the liquidity effect is sensitive to i changes in sample period ii conditioning the correlations on past information iii assuming money growth is exogenous and iv treating monetary changes as anticipated or unanticipated the correlations change substantially in each case we conclude that the traditional analysis and modern models which rely completely on demand side behavior cannot explain the observed correlations a successful characterization of the liquidity effect requires identification of both private and policy behavior

did technology shocks cause the 1990 91 recession real business cycle theory has been used to determine the statistical properties of aggregate fluctuations induced by technology shocks the finding is that technology shocks have been an important contributor to fluctuations in the u s economy for example finn e kydland and prescott 1991 estimate that if the only impulses were technology shocks the u s economy would have been 70 percent as volatile as it has been over the postwar period in this paper we employ the theory to answer the question did technology shocks cause the 1990 1991 recession answering this question requires the determination of the effects of technology shocks on the path of the economy the procedure that we use to make this determination is as follows first we construct a model economy and calibrate it so that its steady state matches actual 1987 values data for other years are used to determine the realizations of the shocks and the nature of the stochastic processes generating these shocks given these stochastic processes the equilibrium decision rules for our calibrated economy are computed we then construct the path for the economy implied by the model for the period 1984 1 1992 3 we set 1984 1 model values of the state variables equal to actual 1984 1 values and use the decision rules and the realized shocks to construct this path finally we examine whether the model economy experiences a recession in 1991 as did the u s economy business cycles are variations in output per adult that are in large part accounted for by variation in the per adult time allocated to market production figure 1 plots the time path of the per adult labor input during the 1983 1 1989 1 period there was a remarkable 12 percent increase in the labor input per adult beginning in 1990 2 there has been a decline of nearly 6 percent in this per adult labor input an unusual feature of the recovery subsequent to the 1990 1991 recession is that this labor input variable has continued to decline well into the recovery this unusual behavior leads to a related question did technology shocks cause the slow recovery we found it necessary to modify the standard real business cycle model in four ways in order to answer the posed question first given the enormous changes in the relative prices of durables as depicted in figure 2 we could not treat technology change as being neutral with respect to different types of final goods following john b long and charles plosser 1983 and jeremy greenwood et al 1992 we consider multiple production sectors with technology change differing across sectors second we assume that there is a technology employed within the household sector that produces a consumption flow from the stock of consumer durables third we consider land to be a factor of production in addition to labor and capital our fourth modification is to introduce population growth

monetary policy analysis with potentially misspecified models abstract the paper proposes a novel method for conducting policy analysis with potentially misspecified dynamic stochastic general equilibrium dsge models and applies it to a new keynesian dsge model along the lines of christiano eichenbaum and evans jpe 2005 and smets and wouters jeea 2003 we first quantify the degree of model misspecification and then illustrate its implications for the performance of different interest rate feedback rules we find that many of the prescriptions derived from the dsge model are robust to model misspecification jel classification c32 key words bayesian analysis dsge models model misspecification 1 introduction the quantitative evaluation of monetary policy rules plays an important role in the design of stabilization policies much of the recent debate about optimal monetary policy rules has been carried out with new keynesian dynamic stochastic general equilibrium dsge models see woodford 2003 dsge models have the advantage that one can explicitly assess the effect of policy regime changes on expectation formation and decision rules of private agents yet until recently these models were scarcely used by central banks because they were perceived as being inferior to less structural model in terms of fit recent work by christiano eichenbaum and evans 2005 and smets and wouters 2003 has changed this perception these papers show that the fit of large scale new keynesian dsge models is comparable to that of more heavily parameterized models as a consequence a number of central banks have begun to show interest in these models as tools for quantitative policy analysis despite the success in improving the empirical performance of dsge models misspecification remains a concern as shown in del negro schorfheide smets and wouters 2004 accounting for misspecification and model uncertainty in general is an important aspect of the assessment of monetary policies after all it has long been recognized that model and parameter uncertainty affects optimal policies e g brainard 1967 chow 1975 and craine 1979 1 this paper proposes a new method for taking model misspecification into account when assessing the performance of alternative interest rate feedback rules we apply this approach to evaluate different policies in the context of a new keynesian dsge model including the estimated volcker greenspan rule a natural approach in the presence of model uncertainty is to evaluate policy rules within all the model specifications that are under consideration in choosing the best performing rule one can either follow a bayesian route assigning probabilities to models and minimizing the overall posterior expected loss alternatively one can follow a minimax strategy by adopting a policy that minimizes the worst case loss across models the literature contains numerous applications of these ideas e g mccallum 1988 levin wieland and williams 1999 2003 rudebusch 2001 2003 onatski and stock 2002 onatski and williams 2003 brock durlauf and west 2004 cogley and sargent 2005 and hansen and sargent 2005 all these papers differ with regard to the type of models included in the model set and the formulation of the decision problem that leads to the choice of a preferred policy our analysis differs from previous work with respect to the method that is used to construct a model set and assign probabilities to these models so that a bayesian calculation of posterior expected performance is possible we start from a dsge model in which monetary policy is modelled through an interest rate feedback rule the dsge model imposes cross coefficient restrictions on a vector autoregressive representation of the data while we are assuming that the data obey a vector autoregressive law of motion we allow for potential deviations from the cross coefficient restrictions at the same time we maintain the assumption that monetary policy follows the same interest rate rule as in the dsge model

have standard vars remained stable since the crisis small or medium scale vars are commonly used in applied macroeconomics for forecasting and evaluating the shock transmission mechanism this requires the var parameters to be stable over the evaluation and forecast sample or to explicitly consider parameter time variation the earlier literature focused on whether there were sizable parameter changes in the early 1980s in either the conditional mean or variance parameters and in the subsequent period till the beginning of the new century in this paper we conduct a similar analysis but focus on the effects of the recent crisis using a range of techniques we provide substantial evidence against parameter stability the evolution of the unemployment rate seems particularly different relative to its past behavior we then discuss and evaluate alternative methods to handle parameter instability in a forecasting context while none of the methods clearly emerges as best some techniques turn out to be useful to improve the forecasting performance

the effect of the minimum wage on the fast food industry using a longitudinal survey of fast food restaurants in texas the authors examine the impact of recent increases in the federal minimum wage on a low wage labor market less than 5 of fast food restaurants were using the new youth subminimum wage in july august 1991 even though the vast majority paid a starting wage below the new hourly minimum wage immediately before it became effective although some restaurants increased wages beyond the level needed to comply with higher minimum wages in both 1990 and 1991 those federal minimum wage increases greatly compressed the distribution of starting wages in the texas fast food industry two findings at variance with conventional predictions are that 1 employment increased more in those firms likely to have been most affected by the 1991 minimum wage increase than in other firms and 2 price changes were unrelated to mandated wage changes

common drifting volatility in large bayesian vars the general pattern of estimated volatilities of macroeconomic and financial variables is often broadly similar we propose two models in which conditional volatilities feature comovement and study them using u s macroeconomic data the first model specifies the conditional volatilities as driven by a single common unobserved factor plus an idiosyncratic component we label this model bvar with general factor stochastic volatility bvar gfsv and we show that the loss in terms of marginal likelihood from assuming a common factor for volatility is moderate the second model which we label bvar with common stochastic volatility bvar csv is a special case of the bvar gfsv in which the idiosyncratic component is eliminated and the loadings to the factor are set to 1 for all the conditional volatilities such restrictions permit a convenient kronecker structure for the posterior variance of the var coefficients which in turn permits estimating the model even with large datasets while perhaps misspecifi

christopher a sims and vector autoregressions three decades ago christopher a sims suggested that vector autoregressions vars are useful statistical devices for evaluating alternative macroeconomic models his suggestion has stood the test of time well in the early days vars played an important role in the evaluation of alternative models they continue to play that role today

combining var and dsge forecast densities a popular macroeconomic forecasting strategy utilizes many models to hedge against instabilities of unknown timing see among others stock and watson 2004 clark and mccracken 2010 and jore et al 2010 existing studies of this forecasting strategy exclude dynamic stochastic general equilibrium dsge models despite the widespread use of these models by monetary policymakers in this paper we use the linear opinion pool to combine inflation forecast densities from many vector autoregressions vars and a policymaking dsge model the dsge receives a substantial weight in the pool at short horizons provided the var components exclude structural breaks in this case the inflation forecast densities exhibit calibration failure allowing for structural breaks in the vars reduces the weight on the dsge considerably but produces well calibrated forecast densities for inflation

figuring out the fed beliefs about policymakers and gains from transparency in this paper i use a markov chain monte carlo algorithm to estimate a model of private sector behavior that does not feature private sector knowledge of the monetary policymaking process and instead leaves firms and households uncertain about how monetary policy is set the private sector entertains two competing views of monetary policymaking which i estimate firms and households use bayes law on a rolling data sample to distinguish between those two models i use this setup to study the evolution of beliefs about the federal reserve and the possible gains from transparency

an economic evaluation of the potential for distributed energy in australia we present here economic findings from a major study by australia s commonwealth scientific and industrial research organisation csiro on the value of distributed energy technologies de collectively demand management energy efficiency and distributed generation for reducing greenhouse gas emissions from australia s energy sector csiro 2009 the study covered potential economic environmental technical social policy and regulatory impacts that could result from their wide scale adoption partial equilibrium modeling of the stationary energy and transport sectors found that australia could achieve a present value welfare gain of around 130 billion when operating under a 450 ppm carbon reduction trajectory through to 2050 modeling also suggests that reduced volatility in the spot market could decrease average prices by up to 12 in 2030 and 65 in 2050 by using local resources to better cater for an evolving supply demand imbalance further modeling suggests that even a small amount of distributed generation located within a distribution network has the potential to significantly alter electricity prices by changing the merit order of dispatch in an electricity spot market changes to the dispatch relative to a base case can have both positive and negative effects on network losses

combining judgment and models this paper proposes a parsimonious and model consistent method for combining forecasts generated by structural microfounded models and judgmental forecasts the method delivers along several dimensions first it improves the forecasting performance of the model second it allows interpreting the judgmental forecasts through the lens of the model finally it provides a framework to assess the informational content of the judgmental forecasters i illustrate the proposed methodology with a real time forecasting exercise using a simple neo keynesian dynamic stochastic general equilibrium model and the survey of professional forecasters 2010 the ohio state university

forecasting with medium and large bayesian vars this paper is motivated by the recent interest in the use of bayesian vars for forecasting even in cases where the number of dependent variables is large in such cases factor methods have been traditionally used but recent work using a particular prior suggests that bayesian var methods can forecast better in this paper we consider a range of alternative priors which have been used with small vars discuss the issues which arise when they are used with medium and large vars and examine their forecast performance using a us macroeconomic dataset containing 168 variables we find that bayesian vars do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches typically we find that the simple minnesota prior forecasts well in medium and large vars which makes this prior attractive relative to computationally more demanding alternatives our empirical results show the importance of using forecast metrics based on the entire predictive density instead of relying solely on those based on point forecasts

investment in new power generation under uncertainty benefits of chp vs condensing plants in a copula based analysis abstract in this paper we apply a spread based real options approach to analyze the decision making problem of an investor who has the choice between an irreversible investment in a condensing power plant without heat utilization and a plant with combined heat and power chp generation our investigation focuses on large scale fossil fueled generation technologies and is based on a stochastic model that uses copula functions to provide the input parameters of the real options model we define the aggregated annual spread as assessment criteria for our investigation since it contains all relevant volatile input parameters that have an impact on the evaluation of investment decisions we show that the specific characteristics of chp plants such as additional revenues from heat sales promotion schemes specific operational features and a beneficial allocation of co 2 allowances have a significant impact on the option value and therefore on the optimal timing for investment for the two fossil fueled chp technologies investigated combined cycle gas turbine and steam turbine we conclude from our analysis that a high share of chp generation reduces the risk exposure for the investor the maximal possible chp generation depends significantly on the local heat demand in the surroundings of the power plant considering this the size of the heat sink available could gain more relevance in the future selection process of sites for new large scale fossil power plants

identification versus misspecification in new keynesian monetary policy models abstract in this paper we study identification and misspecification problems in standard closed and open economy empirical new keynesian dsge models used in monetary policy analysis we find that problems with model misspecification still appear to be a first order issue in monetary dsge models and argue that it is problems with model misspecification that may benefit the most from moving from a classical to a bayesian framework we also argue that lack of identification should neither be ignored nor be assumed to affect all dsge models fortunately identification problems can be readily assessed on a case by case basis by applying recently developed pre tests of identification

technology shocks and robust sign restrictions in a euro area svar we use a model based identification strategy to estimate the impact of technology shocks on hours worked and employment in the euro area the sign restrictions applied in the vector autoregression var analysis are consistent with a large class of dynamic stochastic general equilibrium dsge models and are robust to parameter uncertainty the results are in line with the conventional real business cycle rbc interpretation that hours worked rise as a result of a positive technology shock by comparing the sign restrictions method to the long run restriction approach of gali quaterly journal of economics 1992 709 38 we show that the results do not depend on the stochastic specification of the hours worked series or the data sample but only on the identification scheme

identification issues in limited information bayesian analysis of structural macroeconomic models the likelihood of the parameters in structural macroeconomic models typically has non identification regions over which it is constant when sufficiently diffuse priors are used the posterior piles up in such non identification regions use of informative priors can lead to the opposite so both can generate spurious inference we propose priors posteriors on the structural parameters that are implied by priors posteriors on the parameters of an embedding reduced form model an example of such a prior is the jeffreys prior we use it to conduct bayesian limited information inference on the new keynesian phillips curve with a var reduced form for us data

on the informational content of wage offers this article investigates signaling and screening roles of wage offers in a single play matching model with two sided unobservable characteristics it generates the following predictions as matching equilibrium outcomes i good jobs offer premia if high quality worker population is large ii bad jobs pay compensating differentials if the proportion of good jobs to low quality workers is large iii all firms may offer a pooling wage in markets dominated by high quality workers and firms or iv gresham s law prevails good types withdraw if bad types dominate the population the screening signaling motive thus has the potential of explaining a variety of wage patterns

bayesian methods for dynamic multivariate models if dynamic multivariate models are to be used to guide decisionmaking it is important that probability assessments of forecasts or policy projections be provided when identified bayesian vector autoregression var models are presented with error bands in the existing literature both conceptual and numerical problems have not been dealt with in an internally consistent way in this paper the authors develop methods to introduce prior information in both reduced form and structural var models without introducing substantial new computational burdens their approach makes it feasible to use a single large dynamic framework for example twenty variable models for tasks of policy projections copyright 1998 by economics department of the university of pennsylvania and the osaka university institute of social and economic research association

propagation mechanisms for government spending shocks a bayesian comparison the inability of a simple real business cycle model to predict a rise in consumption in response to increased government expenditures observed in many empirical studies has stimulated the development of alternative theories of government spending shocks using the bayesian approach we evaluate the quantitative performance of five extant models and find that neither of the considered transmission mechanisms for government spending helps improve the fit of the baseline model moreover we find that consumption decreases in all estimated models in response to a rise in government spending

real time forecasting with a mixed frequency var this article develops a vector autoregression var for time series which are observed at mixed frequencies quarterly and monthly the model is cast in state space form and estimated with bayesian methods under a minnesota style prior we show how to evaluate the marginal data density to implement a data driven hyperparameter selection using a real time dataset we evaluate forecasts from the mixed frequency var and compare them to standard quarterly frequency var and to forecasts from midas regressions we document the extent to which information that becomes available within the quarter improves the forecasts in real time this article has online supplementary materials

alternative theories of wage dispersion abstract we analyze labor market models where the law of one price fails i e models with equilibrium wage dispersion we begin considering ex ante heterogeneous workers but highlight a problem with this approach if search is costly the market shuts down we then assume homogeneous workers but ex post heterogeneous matches this model is robust to search costs and delivers equilibrium wage dispersion however we prove that the law of two prices holds equilibrium implies at most two wages we explore other models including one combining ex ante and ex post heterogeneity which is robust and delivers more realistic wage dispersion

priors from general equilibrium models for vars abstract this paper uses a simple new keynesian dynamic stochastic general equilibrium model as a prior for a vector autoregression shows that the resulting model is competitive with standard benchmarks in terms of forecasting and can be used for policy analysis jel classification c11 c32 c53 key words bayesian analysis dsge models forecasting vector autoregressions 1 introduction dynamic stochastic general equilibrium dsge models are popular nowadays in macroeconomics they are taught in virtually every economics ph d program and represent a predominant share of publications in the field yet when it comes to policy making these models are scarcely used at least from a quantitative point of view the main quantitative workhorse for policy making at the federal reserve system is frb us a macro econometric model built in the cowles foundation tradition a style of macroeconomics that is no longer taught in many ph d programs 1 in their decision process fed policy makers rely heavily on forecasting they want to know the expected path of inflation in the next few quarters and by how much a 50 basis point increase in the federal funds rate would affect that path frb us offers answers to these questions answers that many macroeconomists would regard with suspicion given both the lucas 1976 critique and the fact that in general the restrictions imposed by cowles foundation models are at odds with dynamic general equilibrium macroeconomics sims 1980 general equilibrium models on the other hand have a hard time providing alternative answers the fact that these models are perceived to do badly in terms of forecasting as they are scarcely parameterized is perhaps one of the reasons why they are not at the forefront of policy making while progress is being made in the development of dsge models that deliver acceptable forecasts e g smets and wouters 2003 this paper proposes an approach that combines a stylized general equilibrium model with a vector autoregression var to obtain a specification that both forecasts well and is usable for policy analysis specifically the approach involves using prior information coming from a dsge model in the estimation of a vector autoregression loosely speaking this prior can be thought of as the result of the following exercise i simulate time series data from the dsge model ii fit a var to these data in practice we replace the sample moments of the simulated data by population moments computed from the dsge model solution since the dsge model depends on unknown structural parameters we use a hierarchical prior in our analysis by placing a distribution on the dsge model parameters a tightness parameter controls the weight of the dsge model prior relative to the weight of the actual sample markov chain monte carlo methods are used to generate draws from the joint posterior distribution of the var and dsge model parameters the paper shows that the approach makes even a fairly stylized new keynesian dsge model competitive with standard benchmarks in terms of forecasting real output growth inflation and the nominal interest rate the three variables that are of most interest to policy makers 2 up to this point our procedure borrows from the work of dejong ingram and whiteman 1993 and ingram and whiteman 1994 who are the first to use priors from dsge models for vars ingram and whiteman showed that prior information from the bare bones stochastic growth model of king plosser and rebelo 1988 is helpful in forecasting real economic activity such as output consumption investment and hours worked in addition to documenting the forecasting performance of a trivariate var with a prior derived from a monetary dsge model this paper makes two contributions that significantly extend the earlier work first we show formally how posterior inference for the var parameters can be translated into posterior inference for dsge model parameters

on the fit of new keynesian models this article provides new tools for the evaluation of dynamic stochastic general equilibrium dsge models and applies them to a large scale new keynesian model we approximate the dsge model by a vector autoregression and then systematically relax the implied cross equation restrictions and document how the model fit changes we also compare the dsge model s impulse responses to structural shocks with those obtained after relaxing its restrictions we find that the degree of misspecification in this large scale dsge model is no longer so large as to prevent its use in day to day policy analysis yet is not small enough to be ignored

on the precision of calvo parameter estimates in structural nkpc models abstract we study the extent of empirical information that can be obtained from alternative structural new keynesian inflation equations concerning the average duration of prices in the united states given that such specifications may be hard to identify using four different indexation and real wage rigidity based models in conjunction with identification robust econometric methods we evaluate the precision of calvo parameter estimates while results are sensitive to calibration and instrument selection we find confidence bounds on the average duration of prices that line up with available micro founded studies statistically significant coefficients for the forcing variables and non zero estimates on the coefficient of lagged inflation

evaluating replacement project of nuclear power plants under uncertainty this paper investigates the valuation of a replacement project of nuclear power plants under the deregulated electricity market the replacement project consists of two components the decision to decommission an existing plant and the decision to construct a new plant in the replacement project the decommissioning decision should be made considering not only the profitability of the existing plant but also the profitability and costs of the construction of the new plant real options theory is used to determine the optimal timing of the decommissioning and construction in order to examine the effect of decommissioning time and decision making one we consider a time lag for these decision making times we show the dependence of the replacement project value on uncertainty and time lag

the past present and future of macroeconomic forecasting he reports of the death of large scale macroeconomic forecasting models are not exaggerated but many observers interpret the failure of the early models as indicative of a bleak future for macroeconomic forecasting more generally such is not the case although the large scale macroeconomic forecasting models didn t live up to their original promise they nevertheless left a useful legacy of lasting contributions from which macroeconomic forecasting will continue to benefit they spurred the development of powerful identification and estimation theory computational and simulation techniques comprehensive machinereadable macroeconomic databases and much else moreover past failures do not necessarily imply a bleak future we learn from our mistakes just as macroeconomics has benefitted from rethinking since the 1970s so too will macroeconomic forecasting understanding the future of macroeconomic forecasting requires understanding the interplay between measurement and theory and the corresponding evolution of the nonstructural and structural approaches to forecasting nonstructural macroeconomic forecasting methods attempt to exploit the reduced form correlations in observed macroeconomic time series with little reliance on economic theory structural models in contrast view and interpret economic data through the lens of a particular economic theory structural econometric forecasting because it is based on explicit theory rises and falls with theory typically with a lag structural keynesian macroeconomic forecasting based on postulated systems of decision rules enjoyed a golden age in the 1950s and 1960s following the advances in keynesian theory in the 1930s and 1940s

wage differentials employer size and unemployment the unique equilibrium solution to a game in which a continuum of individual employers choose permanent wage offers and a continuum of workers search by sequentially sampling from the set of offers is characterized wage dispersion is a robust outcome provided that workers search while employed as well as when unemployed the unique nondegenerate equilibrium distribution of wage offers is constructed for three cases 1 identical workers and employers 2 identical employers and an atomless distribution of worker supply prices and 3 identical workers and an atomless distribution of job productivities copyright 1998 by economics department of the university of pennsylvania and the osaka university institute of social and economic research association

on the informational role of term structure in the us monetary policy rule abstract this paper uses a structural approach based on the indirect inference principle to estimate a standard version of the new keynesian monetary nkm model augmented with term structure using both revised and real time data the estimation results show that the term spread and policy inertia are both important determinants of the us estimated monetary policy rule whereas the persistence of shocks plays a small but significant role when revised and real time data of output and inflation are both considered more importantly the relative importance of term spread and persistent shocks in the policy rule and the shock transmission mechanism drastically change when it is taken into account that real time data are not well behaved

choice of nuclear power investments under price uncertainty valuing modularity this work aims at examining how to compare two investment projects in the electricity market the first project is a flexible sequence of small nuclear power plants whereas the second is a nuclear power plant of large capacity we measure the option value generated by the modularity of the first project given the uncertain future competitive price of electricity using a realistic calibration of the model we show that the option value of modularity has a sizeable effect on the optimal dynamic strategy of the producer in particular in terms of the optimal timing of the decision to invest in the first module