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29
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)
2004, “Estimation of Stable Distributions by Indirect Inference
"... This article deals with the estimation of the parameters of an αstable distribution with indirect inference, using the skewedt distribution as an auxiliary model. The latter distribution appears as a good candidate since it has the same number of parameters as the αstable distribution, with each ..."
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Cited by 15 (3 self)
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This article deals with the estimation of the parameters of an αstable distribution with indirect inference, using the skewedt distribution as an auxiliary model. The latter distribution appears as a good candidate since it has the same number of parameters as the αstable distribution, with each parameter playing a similar role. To improve the properties of the estimator in finite sample, we use constrained indirect inference. In a Monte Carlo study we show that this method delivers estimators with good properties in finite sample. We provide an empirical application to the distribution of jumps in the S&P 500 index returns.
Comparison of Misspecified Calibrated Models: The Minimum Distance Approach
, 2009
"... This paper presents testing procedures for comparison of misspecified calibrated models. The proposed tests are of the Vuongtype (Vuong, 1989; Rivers and Vuong, 2002). In our framework, an econometrician selects values for the parameters in order to match some characteristics of the data with those ..."
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Cited by 6 (1 self)
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This paper presents testing procedures for comparison of misspecified calibrated models. The proposed tests are of the Vuongtype (Vuong, 1989; Rivers and Vuong, 2002). In our framework, an econometrician selects values for the parameters in order to match some characteristics of the data with those implied by the competing theoretical models. We assume that all competing models are misspecified, and suggest a test for the null hypothesis that the models under consideration provide equivalent fit to the data characteristics, against the alternative that one of the models is a better approximation. We consider both nested and nonnested cases. We also relax the dependence of models’ ranking on the choice of weight matrix by suggesting averaged and supnorm procedures. The proposed method is illustrated by comparing standard cashinadvance and portfolio adjustment cost models in their ability to match the impulse responses of output and inflation to money growth shocks.
A Simulation Based Specification Test for Diffusion Processes
, 2007
"... This paper makes two contributions. First, we outline a simple simulation based framework for constructing conditional distributions for multifactor and multidimensional diffusion processes, for the case where the functional form of the conditional density is unknown. The distributions can be used ..."
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Cited by 6 (6 self)
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This paper makes two contributions. First, we outline a simple simulation based framework for constructing conditional distributions for multifactor and multidimensional diffusion processes, for the case where the functional form of the conditional density is unknown. The distributions can be used, for example, to form predictive confidence intervals for time period t + τ, given information up to period t. Second, we use the simulation based approach to construct a test for the correct specification of a diffusion process. The suggested test is in the spirit of the conditional Kolmogorov test of Andrews (1997). However, in the present context the null conditional distribution is unknown and is replaced by its simulated counterpart. The limiting distribution of the test statistic is not nuisance parameter free. In light of this, asymptotically valid critical values are obtained via appropriate use of the block bootstrap. The suggested test has power against a larger class of alternatives than tests that are constructed using marginal distributions/densities, such as those in AïtSahalia (1996) and Corradi and Swanson (2005a). The findings of a small Monte Carlo experiment underscore the good finite sample properties of the proposed test, and an empirical illustration underscores the ease with which the proposed simulation and testing methodology can be applied.
Houses as ATMs? Mortgage Refinancing and Macroeconomic Uncertainty.” Working Paper 19421. National Bureau of Economic Research
, 2013
"... Can liquidity constraints explain the dramatic buildup of household leverage during the housing boom of mid2000s? To answer this question we estimate a structural model of household liquidity management in the presence of longterm mortgages and shortterm home equity loans. Households face count ..."
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Cited by 5 (0 self)
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Can liquidity constraints explain the dramatic buildup of household leverage during the housing boom of mid2000s? To answer this question we estimate a structural model of household liquidity management in the presence of longterm mortgages and shortterm home equity loans. Households face countercyclical idiosyncratic labor income uncertainty and borrowing constraints, which affect optimal choices of leverage, precautionary saving in liquid assets and illiquid home equity, debt repayment, mortgage refinancing, and default. Taking the observed historical path of house prices, aggregate income, and interest rates as given, the model quantitatively accounts for the runup in household debt and consumption boom prior to the financial crisis, their subsequent collapse, and weak recovery following the Great Recession, especially among the most constrained households.
Predictive Density Construction and Accuracy Testing with Multiple Possibly Misspecified Diffusion Models
, 2009
"... This paper develops tests for comparing the accuracy of predictive densities derived from (possibly misspecified) diffusion models. In particular, we first outline a simple simulation based framework for constructing predictive densities for onefactor and stochastic volatility models. Then, we cons ..."
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Cited by 4 (4 self)
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This paper develops tests for comparing the accuracy of predictive densities derived from (possibly misspecified) diffusion models. In particular, we first outline a simple simulation based framework for constructing predictive densities for onefactor and stochastic volatility models. Then, we construct accuracy assessment tests that are in the spirit of Diebold and Mariano (1995) and White (2000). In order to establish the asymptotic properties of our tests, we also develop a recursive variant of the nonparametric simulated maximum likelihood estimator of Fermanian and Salanié (2004). In an empirical illustration, the predictive densities from several models of the onemonth federal funds rates are compared.
Optimal Comparison of Misspecified Moment Restriction Models
, 2009
"... This paper considers optimal testing of model comparison hypotheses for misspecified unconditional moment restriction models. We adopt the generalized NeymanPearson optimality criterion, which focuses on the convergence rates of the type I and II error probabilities under fixed global alternatives, ..."
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Cited by 3 (0 self)
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This paper considers optimal testing of model comparison hypotheses for misspecified unconditional moment restriction models. We adopt the generalized NeymanPearson optimality criterion, which focuses on the convergence rates of the type I and II error probabilities under fixed global alternatives, and derive an optimal but practically infeasible test. We then propose feasible approximation test statistics to the optimal one. For linear instrumental variable regression models, the conventional empirical likelihood ratio test statistic emerges. For general nonlinear moment restrictions, we propose a new test statistic based on an iterative algorithm. We derive asymptotic properties of these test statistics.
Impulse Response Matching Estimators for DSGE Models
, 2015
"... One of the leading methods of estimating the structural parameters of DSGE models is the VARbased impulse response matching estimator. The existing asympotic theory for this estimator does not cover situations in which the number of impulse response parameters exceeds the number of VAR model param ..."
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Cited by 1 (1 self)
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One of the leading methods of estimating the structural parameters of DSGE models is the VARbased impulse response matching estimator. The existing asympotic theory for this estimator does not cover situations in which the number of impulse response parameters exceeds the number of VAR model parameters. Situations in which this order condition is violated arise routinely in applied work. We establish the consistency of the impulse response matching estimator in this situation, we derive its asymptotic distribution, and we show how this distribution can be approximated by bootstrap methods. Our analysis sheds new light on the choice of the weighting matrix and covers both weakly and strongly identified DSGE model parameters. We also show that under our assumptions special care is needed to ensure the asymptotic validity of Bayesian methods of inference. A simulation study suggests that the interval estimators we propose are reasonably accurate in practice. We also show that using these methods may affect the substantive conclusions in empirical work.
Comparison of Misspeci ed Calibrated Models: The Minimum Distance Approach
, 2011
"... This paper proposes several testing procedures for comparison of misspeci ed calibrated models. The proposed tests are of the Vuongtype (Vuong, 1989; Rivers and Vuong, 2002). In our framework, the econometrician selects values for models parameters in order to match some characteristics of data wi ..."
Abstract

Cited by 1 (1 self)
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This paper proposes several testing procedures for comparison of misspeci ed calibrated models. The proposed tests are of the Vuongtype (Vuong, 1989; Rivers and Vuong, 2002). In our framework, the econometrician selects values for models parameters in order to match some characteristics of data with those implied by the theoretical model. We assume that all competing models are misspeci
ed, and suggest a test for the null hypothesis that they provide equivalent
t to data characteristics, against the alternative that one of the models is a better approximation. We consider both nested and nonnested cases. We also relax the dependence of models ranking on the choice of a weight matrix by suggesting averaged and supnorm procedures. The methods are illustrated by comparing the cashinadvance and portfolio adjustment cost models in their ability to match the impulse responses of output and ination to money growth shocks. JEL classi
cation: C51; C52
StateObservation Sampling and the Econometrics of Learning Models
, 2010
"... In nonlinearstatespacemodels, sequentiallearningaboutthe hidden statecanproceed byparticlefilteringwhen thedensityoftheobservationconditionalonthe stateisavailable analytically (e.g. Gordon et al. 1993). This condition need not hold in complex environments, such as the incompleteinformation equili ..."
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Cited by 1 (0 self)
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In nonlinearstatespacemodels, sequentiallearningaboutthe hidden statecanproceed byparticlefilteringwhen thedensityoftheobservationconditionalonthe stateisavailable analytically (e.g. Gordon et al. 1993). This condition need not hold in complex environments, such as the incompleteinformation equilibrium models considered in financial economics. In this paper, we make two contributions to the learning literature. First, we introduce a new filtering method, the stateobservation sampling (SOS) filter, for general statespace models with intractable observation densities. Second, we develop an indirect inferencebased estimator for a large class of incompleteinformation economies. We demonstrate the good performance of these techniques on an asset pricing model with investor learning applied to over 80 years of daily equity returns.