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52
Bayesian analysis of DSGE models
 ECONOMETRICS REVIEW
, 2007
"... This paper reviews Bayesian methods that have been developed in recent years to estimate and evaluate dynamic stochastic general equilibrium (DSGE) models. We consider the estimation of linearized DSGE models, the evaluation of models based on Bayesian model checking, posterior odds comparisons, and ..."
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Cited by 130 (5 self)
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This paper reviews Bayesian methods that have been developed in recent years to estimate and evaluate dynamic stochastic general equilibrium (DSGE) models. We consider the estimation of linearized DSGE models, the evaluation of models based on Bayesian model checking, posterior odds comparisons, and comparisons to vector autoregressions, as well as the nonlinear estimation based on a secondorder accurate model solution. These methods are applied to data generated from correctly specified and misspecified linearized DSGE models, and a DSGE model that was solved with a secondorder perturbation method. (JEL C11, C32, C51, C52)
Methods to Estimate Dynamic Stochastic General Equilibrium Models
 Journal of Economic Dynamics and Control
, 2007
"... This paper employs the onesector Real Business Cycle model as a testing ground for four di®erent procedures to estimate Dynamic Stochastic General Equilibrium (DSGE) models. The procedures are: 1) Maximum Likelihood (with and without measurement errors and incorporating priors), 2) Generalized Meth ..."
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Cited by 53 (3 self)
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This paper employs the onesector Real Business Cycle model as a testing ground for four di®erent procedures to estimate Dynamic Stochastic General Equilibrium (DSGE) models. The procedures are: 1) Maximum Likelihood (with and without measurement errors and incorporating priors), 2) Generalized Method of Moments, 3) Simulated Method of Moments, and 4) the Extended Method of Simulated Moments proposed by Smith (1993). Monte Carlo analysis shows that although all procedures deliver reasonably good estimates, there are substantial di®erences in statistical and computational e±ciency in the small samples currently available to estimate DSGE models. The implications of the singularity of DSGE models for each estimation procedure are fully discussed.
The Business Cycle and the Life Cycle
 In NBER Macroeconomics Annual 2004, edited by Mark Gertler and Kenneth Rogoff
, 2004
"... w o r k i n g ..."
Indeterminacy, Aggregate Demand, and the Real Business Cycle
, 2003
"... We show that under indeterminacy aggregate demand shocks are able to explain not only aspects of actual fluctuations that standard RBC models predict fairly well, but also aspects of actual fluctuations that standard RBC models cannot explain, such as the humpshaped, trend reverting impulse resp ..."
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Cited by 24 (9 self)
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We show that under indeterminacy aggregate demand shocks are able to explain not only aspects of actual fluctuations that standard RBC models predict fairly well, but also aspects of actual fluctuations that standard RBC models cannot explain, such as the humpshaped, trend reverting impulse responses to transitory shocks found in US output (Cogley and Nason, AER, 1995); the large forecastable movements and comovements of output, consumption and hours (Rotemberg and Woodford, AER, 1996); and the fact that consumption appears to lead output and investment over the business cycle.
A test for comparing multiple misspecified conditional distributions, manuscript
, 2003
"... This paper introduces a test for the comparison of multiple misspecified conditional interval models, for the case of dependent observations+ Model accuracy is measured using a distributional analog of mean square error, in which the approximation error associated with a given model, say, model i, ..."
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Cited by 24 (11 self)
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This paper introduces a test for the comparison of multiple misspecified conditional interval models, for the case of dependent observations+ Model accuracy is measured using a distributional analog of mean square error, in which the approximation error associated with a given model, say, model i, for a given interval, is measured by the expected squared difference between the conditional confidence interval under model i and the “true ” one+ When comparing more than two models, a “benchmark ” model is specified, and the test is constructed along the lines of the “reality check ” of White ~2000, Econometrica 68, 1097–1126!+ Valid asymptotic critical values are obtained via a version of the block bootstrap that properly captures the effect of parameter estimation error+ The results of a small Monte Carlo experiment indicate that the test does not have unreasonable finite sample properties, given small samples of 60 and 120 observations, although the results do suggest that larger samples should likely be used in empirical applications of the test+ 1.
Evaluation of Dynamic Stochastic General Equilibrium Models Based on
 Mary, University of London and Rutgers University
, 2003
"... We take as a starting point the existence of a joint distribution implied by different dynamic stochastic general equilibrium (DSGE) models, all of which are potentially misspecified. Our objective is to compare “true ” joint distributions with ones generated by given DSGEs. This is accomplished vi ..."
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Cited by 23 (14 self)
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We take as a starting point the existence of a joint distribution implied by different dynamic stochastic general equilibrium (DSGE) models, all of which are potentially misspecified. Our objective is to compare “true ” joint distributions with ones generated by given DSGEs. This is accomplished via comparison of the empirical joint distributions (or confidence intervals) of historical and simulated time series. The tool draws on recent advances in the theory of the bootstrap, Kolmogorov type testing, and other work on the evaluation of DSGEs, aimed at comparing the second order properties of historical and simulated time series. We begin by fixing a given model as the “benchmark ” model, against which all “alternative ” models are to be compared. We then test whether at least one of the alternative models provides a more “accurate ” approximation to the true cumulative distribution than does the benchmark model, where accuracy is measured in terms of distributional square error. Bootstrap critical values are discussed, and an illustrative example is given, in which it is shown that alternative versions of a standard DSGE model in which calibrated parameters are allowed to vary slightly perform equally well. On the other hand, there are stark differences between models when the shocks driving the models are assigned nonplausible variances and/or distributional assumptions. JEL classification: C12, C22.
Predictive Density Accuracy Tests
, 2004
"... This paper outlines a testing procedure for assessing the relative outofsample predictive accuracy of multiple conditional distribution models, and surveys existing related methods in the area of predictive density evaluation, including methods based on the probability integral transform and the K ..."
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Cited by 20 (3 self)
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This paper outlines a testing procedure for assessing the relative outofsample predictive accuracy of multiple conditional distribution models, and surveys existing related methods in the area of predictive density evaluation, including methods based on the probability integral transform and the KullbackLeibler Information Criterion. The procedure is closely related to Andrews ’ (1997) conditional Kolmogorov test and to White’s (2000) reality check approach, and involves comparing square ( (approximation) errors associated with models i, i =1,..., n, by constructing weighted averages over U of E Fi(uZ t, θ † i) − F0(uZ t)) 2, θ0) , where F0(··) and Fi(··) are true and approximate distributions, u ∈ U, and U is a possibly unbounded set on the real line. Appropriate bootstrap procedures for obtaining critical values for tests constructed using this measure of loss in conjunction with predictions obtained via rolling and recursive estimation schemes are developed. We then apply these bootstrap procedures to the case of obtaining critical values for our predictive accuracy test. A Monte Carlo experiment comparing our bootstrap methods with methods that do not include location bias adjustment terms is provided, and results indicate coverage improvement when our proposed bootstrap procedures are used. Finally, an empirical example comparing alternative predictive densities for U.S. inflation is given.