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49
AN ESTIMATED STOCHASTIC DYNAMIC GENERAL EQUILIBRIUM MODEL OF THE EURO AREA
, 2002
"... This paper develops and estimates a stochastic dynamic general equilibrium (SDGE) model with sticky prices and wages for the euro area. The model incorporates various other features such as habit formation, costs of adjustment in capital accumulation and variable capacity utilisation. It is estimate ..."
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Cited by 363 (11 self)
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This paper develops and estimates a stochastic dynamic general equilibrium (SDGE) model with sticky prices and wages for the euro area. The model incorporates various other features such as habit formation, costs of adjustment in capital accumulation and variable capacity utilisation. It is estimated with Bayesian techniques using seven key macroeconomic variables: GDP, consumption, investment, prices, real wages, employment and the nominal interest rate. The introduction of ten orthogonal structural shocks (including productivity, labour supply, investment, preference, costpush and monetary policy shocks) allows for an empirical investigation of the effects of such shocks and of their contribution to business cycle fluctuations in the euro area. Using the estimated model, the paper also analyses the output (real interest rate) gap, defined as the difference between the actual and modelbased potential output (real interest rate).
Predictive density evaluation
, 2005
"... This chapter discusses estimation, specification testing, and model selection of predictive density models. In particular, predictive density estimation is briefly discussed, and a variety of different specification and model evaluation tests due to various ..."
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Cited by 50 (8 self)
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This chapter discusses estimation, specification testing, and model selection of predictive density models. In particular, predictive density estimation is briefly discussed, and a variety of different specification and model evaluation tests due to various
Measurement with some theory: using sign restrictions to evaluate business cycle models
, 2007
"... ..."
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.
How Sticky Is Sticky Enough? A Distributional and Impulse Response Analysis of New Keynesian DSGE Models
, 2006
"... In this paper, we add to the literature on the assessment of how well data simulated from newKeynesian dynamic stochastic general equilibrium (DSGE) models reproduce the dynamic features of historical data. In particular, we evaluate sticky price, sticky price with dynamic indexation, and sticky in ..."
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Cited by 11 (2 self)
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In this paper, we add to the literature on the assessment of how well data simulated from newKeynesian dynamic stochastic general equilibrium (DSGE) models reproduce the dynamic features of historical data. In particular, we evaluate sticky price, sticky price with dynamic indexation, and sticky information models using impulse response and correlation measures and via implementation of a distribution based approach for comparing (possibly) misspecified DSGE models using simulated and historical inflation and output gap data. One of our main findings is that for a standard level of stickiness (i.e. annual price or information adjustment), the sticky price model with indexation dominates other models. We also find that when a lower level of information and price stickiness is used (i.e. biannual adjustment), there is much less to choose between the models (see Bils and Klenow (2004) for evidence in favor of lower levels of stickiness). This finding is due to the fact that simulated and historical densities are “much” closer under biannual adjustment.
How Much Structure in Empirical Models
 Palgrave Handbook of Econometricsvolume 2, Applied Econometrics, Palgrave
, 2008
"... This chapter highlights the problems that structural methods and SVAR approaches have when estimating DSGE models and examining their ability to capture important features of the data. We show that structural methods are subject to severe identification problems due, in large part, to the nature o ..."
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Cited by 10 (1 self)
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This chapter highlights the problems that structural methods and SVAR approaches have when estimating DSGE models and examining their ability to capture important features of the data. We show that structural methods are subject to severe identification problems due, in large part, to the nature of DSGE models. The problems can be patched up in a number of ways but solved only if DSGEs are completely reparametrized or respecified. The potential misspecification of the structural relationships give Bayesian methods an hedge over classical ones in structural estimation. SVAR approaches may face invertibility problems but simple diagnostics can help to detect and remedy these problems. A pragmatic empirical approach ought to use the flexibility of SVARs against potential misspecification of the structural relationships but must firmly tie SVARs to the class of DSGE models which could have have generated the data.