This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Evaluation of Dynamic Stochastic General Equilibrium Models Based on Distributional Comparison of Simulated and Historical Data

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Valentina Corradi () (Queen Mary, University of London)
Norman R. Swanson () (Rutgers University)

Additional information is available for the following registered author(s):

Abstract

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 the construction of a new tool for comparing the empirical joint distribution of historical time series with the empirical distribution of 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. Our comparison is done using a distributional generalization of White's (2000) reality check. In particular, we test whether at least one of the alternative models provides a more "accurate" approximation to the true cumulative distribution than does the benchmark model. Accuracy is measured in terms of distributional square error. As the data are simulated using estimated parameters (as well as previously calibrated parameters), the limiting distribution of the test statistic is a Gaussian process with a covariance kernel that reflects the contribution of parameter estimation error. Thus, the limiting distribution is not nuisance parameter free, and critical values cannot be tabulated. In order to address this issue, we show the validity of two versions of the block bootstrap in our context. An illustrative example is also given, in which the testing approach is applied to a real business cycle model. It is shown that alternative versions of the 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 non-plausible variances and/or distributional assumptions.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help file. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: ftp://snde.rutgers.edu/Rutgers/wp/2003-20.pdf
File Format: application/pdf
File Function:
Download Restriction: no

Publisher Info
Paper provided by Rutgers University, Department of Economics in its series Departmental Working Papers with number 200320.

Download reference. The following formats are available: HTML, plain text, BibTeX, RIS (EndNote), ReDIF
Length:
Date of creation: 27 Oct 2003
Date of revision:
Handle: RePEc:rut:rutres:200320

Contact details of provider:
Postal: New Jersey Hall - 75 Hamilton Street, New Brunswick, NJ 08901-1248
Phone: (732) 932-7482
Fax: (732) 932-7416
Web page: http://snde.rutgers.edu/Rutgers/wp/rutgers-wplist.html
More information through EDIRC

For technical questions regarding this item, or to correct its listing, contact: ().

Related research
Keywords: Real Business Cycles Output empirical Distribution Simulated Models Model Selection

Other versions of this item:

Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models

This paper has been announced in the following NEP Reports:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Atsushi Inoue & Mototsugu Shintani, 2001. "Bootstrapping GMM Estimators for Time Series," Working Papers 0129, Department of Economics, Vanderbilt University, revised Aug 2003. [Downloadable!]
    Other versions:
  2. Silvia Goncalves & Halbert White, 2002. "Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models," University of California at San Diego, Economics Working Paper Series 2000-32r, Department of Economics, UC San Diego. [Downloadable!]
    Other versions:
  3. Francis X. Diebold & Lee E. Ohanian & Jeremy Berkowitz, 1997. "Dynamic equilibrium economies: a framework for comparing models and data," Finance and Economics Discussion Series 1997-23, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
    Other versions:
  4. Long, John B, Jr & Plosser, Charles I, 1983. "Real Business Cycles," Journal of Political Economy, University of Chicago Press, vol. 91(1), pages 39-69, February. [Downloadable!] (restricted)
  5. Yongsung Chang & Joao Gomes & Frank Schorfheide, 2002. "Learning by Doing as a Propagation Mechanism," Macroeconomics 0204002, EconWPA. [Downloadable!]
    Other versions:
  6. DeJong, David N. & Ingram, Beth F. & Whiteman, Charles H., 2000. "A Bayesian approach to dynamic macroeconomics," Journal of Econometrics, Elsevier, vol. 98(2), pages 203-223, October. [Downloadable!] (restricted)
  7. Danielsson, J & Richard, J-F, 1993. "Accelerated Gaussian Importance Sampler with Application to Dynamic Latent Variable Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages S153-73, Suppl. De. [Downloadable!] (restricted)
  8. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
  9. Duffie, Darrell & Singleton, Kenneth J, 1993. "Simulated Moments Estimation of Markov Models of Asset Prices," Econometrica, Econometric Society, vol. 61(4), pages 929-52, July. [Downloadable!] (restricted)
  10. Donald W. K. Andrews, 1997. "A Conditional Kolmogorov Test," Econometrica, Econometric Society, vol. 65(5), pages 1097-1128, September.
    Other versions:
  11. Hall, Peter & Horowitz, Joel L, 1996. "Bootstrap Critical Values for Tests Based on Generalized-Method-of-Moments Estimators," Econometrica, Econometric Society, vol. 64(4), pages 891-916, July. [Downloadable!] (restricted)
  12. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-70, November. [Downloadable!] (restricted)
    Other versions:
  13. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-33, March. [Downloadable!] (restricted)
  14. Valentina Corradi & Norman R. Swanson, 2003. "A Test for Comparing Multiple Misspecified Conditional Distributions," Departmental Working Papers 200314, Rutgers University, Department of Economics. [Downloadable!]
  15. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October. [Downloadable!] (restricted)
  16. Donald W. K. Andrews, 2002. "Higher-Order Improvements of a Computationally Attractive "k"-Step Bootstrap for Extremum Estimators," Econometrica, Econometric Society, vol. 70(1), pages 119-162, January. [Downloadable!] (restricted)
    Other versions:
  17. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
    Other versions:
  18. King, Robert G. & Plosser, Charles I. & Rebelo, Sergio T., 1988. "Production, growth and business cycles : I. The basic neoclassical model," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 195-232. [Downloadable!] (restricted)
Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Norman Swanson & Oleg Korenok, 2006. "How Sticky Is Sticky Enough? A Distributional and Impulse Response Analysis of New Keynesian DSGE Models. Extended Working Paper Version," Departmental Working Papers 200612, Rutgers University, Department of Economics. [Downloadable!]
  2. Valentina Corradi & Norman R. Swanson, 2003. "Bootstrap Specification Tests for Diffusion Processes," Departmental Working Papers 200321, Rutgers University, Department of Economics. [Downloadable!]
    Other versions:
  3. Oleg Korenok & Stanislav Radchenko & Norman R. Swanson, 2006. "International Evidence on the Efficacy of new-Keynesian Models of Inflation Persistence," Working Papers 0602, VCU School of Business, Department of Economics. [Downloadable!]
    Other versions:
  4. Michael P. Clements & Philip Hans Franses & Norman R. Swanson, 2003. "Forecasting economic and financial time-series with non-linear models," Departmental Working Papers 200309, Rutgers University, Department of Economics. [Downloadable!]
    Other versions:
  5. Minford, Patrick & Theodoridis, Konstantinos & Meenagh, David, 2007. "Testing a model of the UK by the method of indirect inference," Cardiff Economics Working Papers E2007/2, Cardiff University, Cardiff Business School, Economics Section, revised Apr 2008. [Downloadable!]
    Other versions:
  6. Norman Swanson & Oleg Korenok, 2006. "The Incremental Predictive Information Associated with Using Theoretical New Keynesian DSGE Models Versus Simple Linear Alternatives," Departmental Working Papers 200615, Rutgers University, Department of Economics. [Downloadable!]
  7. Valentina Corradi & Norman Swanson & Geetesh Bhardwaj, 2006. "A Simulation Based Specification Test for Diffusion Processes," Departmental Working Papers 200614, Rutgers University, Department of Economics. [Downloadable!]
Statistics
Access and download statistics

Did you know? IDEAS was sponsored from 1997 to 2002 by the Université du Québec à Montréal.

This page was last updated on 2008-9-16.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.