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DSGE-SVt: An Econometric Toolkit for High-Dimensional DSGE Models with SV and t Errors

Author

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  • Siddhartha Chib
  • Minchul Shin
  • Fei Tan

Abstract

Currently, there is growing interest in dynamic stochastic general equilibrium (DSGE) models that have more parameters, endogenous variables, exogenous shocks, and observables than the Smets and Wouters (2007) model, and substantial additional complexities from non-Gaussian distributions and the incorporation of time-varying volatility. The popular DYNARE software package, which has proved useful for small and medium-scale models is, however, not capable of handling such models, thus inhibiting the formulation and estimation of more re-alistic DSGE models. A primary goal of this paper is to introduce a user-friendly MATLAB software program designed to reliably estimate high-dimensional DSGE models. It simulates the posterior distribution by the tailored random block Metropolis-Hastings (TaRB-MH) algo-rithm of Chib and Ramamurthy (2010), calculates the marginal likelihood by the method of Chib (1995) and Chib and Jeliazkov (2001), and includes various post-estimation tools that are important for policy analysis, for example, functions for generating point and density forecasts. Another goal is to provide pointers on the prior, estimation, and comparison of these DSGE models. An extended version of the new Keynesian model of Leeper, Traum, and Walker (2017) that has 51 parameters, 21 endogenous variables, 8 exogenous shocks, 8 observables, and 1,494 non-Gaussian and nonlinear latent variables is considered in detail.

Suggested Citation

  • Siddhartha Chib & Minchul Shin & Fei Tan, 2021. "DSGE-SVt: An Econometric Toolkit for High-Dimensional DSGE Models with SV and t Errors," Working Papers 21-02, Federal Reserve Bank of Philadelphia.
    • Handle: RePEc:fip:fedpwp:89577
    DOI: 10.21799/frbp.wp.2021.02
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    1. Alejandro Justiniano & Giorgio E. Primiceri, 2008. "The Time-Varying Volatility of Macroeconomic Fluctuations," American Economic Review, American Economic Association, vol. 98(3), pages 604-641, June.
    2. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 361-393.
    3. Young Min Kim & Kyu Ho Kang, 2019. "Likelihood inference for dynamic linear models with Markov switching parameters: on the efficiency of the Kim filter," Econometric Reviews, Taylor & Francis Journals, vol. 38(10), pages 1109-1130, November.
    4. Angelo Mele, 2020. "Does School Desegregation Promote Diverse Interactions? An Equilibrium Model of Segregation within Schools," American Economic Journal: Economic Policy, American Economic Association, vol. 12(2), pages 228-257, May.
    5. Champagne, Julien & Kurmann, André, 2013. "The great increase in relative wage volatility in the United States," Journal of Monetary Economics, Elsevier, vol. 60(2), pages 166-183.
    6. Vasco Cúrdia & Marco Del Negro & Daniel L. Greenwald, 2014. "Rare Shocks, Great Recessions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1031-1052, November.
    7. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    8. Siddhartha Chib & Srikanth Ramamurthy, 2014. "DSGE Models with Student- t Errors," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 152-171, June.
    9. Chib, Siddhartha & Greenberg, Edward, 1995. "Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models," Journal of Econometrics, Elsevier, vol. 68(2), pages 339-360, August.
    10. Eben Lazarus & Daniel J. Lewis & James H. Stock & Mark W. Watson, 2018. "HAR Inference: Recommendations for Practice Rejoinder," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 574-575, October.
    11. Liu, Xiaochun, 2019. "On tail fatness of macroeconomic dynamics," Journal of Macroeconomics, Elsevier, vol. 62(C).
    12. Edward P. Herbst & Frank Schorfheide, 2016. "Bayesian Estimation of DSGE Models," Economics Books, Princeton University Press, edition 1, number 10612.
    13. Alexander Rathke & Tobias Straumann & Ulrich Woitek, 2017. "OVERVALUED: SWEDISH MONETARY POLICY IN THE 1930s," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 58(4), pages 1355-1369, November.
    14. Kulish, Mariano & Morley, James & Robinson, Tim, 2017. "Estimating DSGE models with zero interest rate policy," Journal of Monetary Economics, Elsevier, vol. 88(C), pages 35-49.
    15. Diebold, Francis X. & Schorfheide, Frank & Shin, Minchul, 2017. "Real-time forecast evaluation of DSGE models with stochastic volatility," Journal of Econometrics, Elsevier, vol. 201(2), pages 322-332.
    16. Kapetanios, George & Masolo, Riccardo M. & Petrova, Katerina & Waldron, Matthew, 2019. "A time-varying parameter structural model of the UK economy," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.
    17. Eric M. Leeper & Nora Traum & Todd B. Walker, 2017. "Clearing Up the Fiscal Multiplier Morass," American Economic Review, American Economic Association, vol. 107(8), pages 2409-2454, August.
    18. Born, Benjamin & Pfeifer, Johannes, 2014. "Policy risk and the business cycle," Journal of Monetary Economics, Elsevier, vol. 68(C), pages 68-85.
    19. Sims, Christopher A, 2002. "Solving Linear Rational Expectations Models," Computational Economics, Springer;Society for Computational Economics, vol. 20(1-2), pages 1-20, October.
    20. Rong Chen & Jun S. Liu, 2000. "Mixture Kalman filters," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(3), pages 493-508.
    21. Franta, Michal, 2017. "Rare shocks vs. non-linearities: What drives extreme events in the economy? Some empirical evidence," Journal of Economic Dynamics and Control, Elsevier, vol. 75(C), pages 136-157.
    22. Chiu, Ching-Wai (Jeremy) & Mumtaz, Haroon & Pintér, Gábor, 2017. "Forecasting with VAR models: Fat tails and stochastic volatility," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1124-1143.
    23. Sims, Christopher A. & Waggoner, Daniel F. & Zha, Tao, 2008. "Methods for inference in large multiple-equation Markov-switching models," Journal of Econometrics, Elsevier, vol. 146(2), pages 255-274, October.
    24. Chan, Joshua C.C. & Grant, Angelia L., 2015. "Pitfalls of estimating the marginal likelihood using the modified harmonic mean," Economics Letters, Elsevier, vol. 131(C), pages 29-33.
    25. Siddhartha Chib & Xiaming Zeng, 2020. "Which Factors are Risk Factors in Asset Pricing? A Model Scan Framework," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(4), pages 771-783, October.
    26. Chib, Siddhartha & Ramamurthy, Srikanth, 2010. "Tailored randomized block MCMC methods with application to DSGE models," Journal of Econometrics, Elsevier, vol. 155(1), pages 19-38, March.
    27. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
    28. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
    29. Eben Lazarus & Daniel J. Lewis & James H. Stock & Mark W. Watson, 2018. "HAR Inference: Recommendations for Practice," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 541-559, October.
    30. Chib, Siddhartha & Ergashev, Bakhodir, 2009. "Analysis of Multifactor Affine Yield Curve Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1324-1337.
    31. Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2002. "Markov chain Monte Carlo methods for stochastic volatility models," Journal of Econometrics, Elsevier, vol. 108(2), pages 281-316, June.
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    Cited by:

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    2. Li, Bing & Pei, Pei & Tan, Fei, 2021. "Financial distress and fiscal inflation," Journal of Macroeconomics, Elsevier, vol. 70(C).
    3. Chang, Yoosoon & Maih, Junior & Tan, Fei, 2021. "Origins of monetary policy shifts: A New approach to regime switching in DSGE models," Journal of Economic Dynamics and Control, Elsevier, vol. 133(C).

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    More about this item

    Keywords

    Bayesian inference; Marginal likelihood; Tailored proposal densities; Random blocks; Student-t shocks; Stochastic volatility.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E63 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Comparative or Joint Analysis of Fiscal and Monetary Policy; Stabilization; Treasury Policy

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