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Theoretical Priors for BVAR Models & Quasi-Bayesian DSGE Model Estimation

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  • Thomai Filippeli

    (Buckingham University)

Abstract

We build upon the work of Ingram and Whiteman (1994), De Jong et al. (1993) and Del Negro and Schorfheide (2004) to propose a methodology of constructing Dynamic Stochastic General Equilibrium (DSGE) consistent prior distributions for Bayesian Vector Autoregressive (BVAR) models. Further, motivated by the studies of Del Negro and Schorfheide (2004), Christiano et al. (2010) and the theoretical results developed by Chernozhukov and Hong (2003) we illustrate how the posterior moments of the BVAR parameter vector can be used to obtain the posterior inference with respect to the DSGE model. The moments of the assumed Normal-Inverse Wishart prior distribution of the VAR parameter vector are derived using the results developed by Fernandez-Villaverde et al. (2007), Christiano et al. (2006) and Ravenna (2007) regarding structural VAR (SVAR) models and the prior density of the DSGE parameter vector. Two data driven hyper-parameters unwind the "intensity" of these theoretical priors avoiding bimodality problems that could possibly arise from the strong disagreement between "tight" priors and the data (see, De Jong et al., 1993). The combination of the VAR marginal likelihood function - approximated using the "Laplace" transform - with the prior distribution of the DSGE parameter vector delivers the posterior distribution of the latter. In line with the results from previous studies, BVAR models with theoretical priors seem to achieve forecasting performance that is comparable - if not better - with the one obtained using theory free "Minnesota" priors (Doan et al., 1984). Finally, a small monte carlo experiment and an empirical exercise reveals very supportive results for the quasi bayesian estimator proposed in this study relatively to the standard full information bayesian maximum likelihood estimator.

Suggested Citation

  • Thomai Filippeli, 2011. "Theoretical Priors for BVAR Models & Quasi-Bayesian DSGE Model Estimation," 2011 Meeting Papers 396, Society for Economic Dynamics.
  • Handle: RePEc:red:sed011:396
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    File URL: https://economicdynamics.org/meetpapers/2011/paper_396.pdf
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    References listed on IDEAS

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    1. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2007. "Assessing Structural VARs," NBER Chapters,in: NBER Macroeconomics Annual 2006, Volume 21, pages 1-106 National Bureau of Economic Research, Inc.
    2. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
    3. Patrick Minford & Konstantinos Theodoridis & David Meenagh, 2009. "Testing a Model of the UK by the Method of Indirect Inference," Open Economies Review, Springer, vol. 20(2), pages 265-291, April.
    4. Jordà, Òscar & Knüppel, Malte & Marcellino, Massimiliano, 2010. "Empirical simultaneous confidence regions for path-forecasts," Discussion Paper Series 1: Economic Studies 2010,06, Deutsche Bundesbank.
    5. Dale J. Poirier, 1995. "Intermediate Statistics and Econometrics: A Comparative Approach," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262161494, March.
    6. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    7. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    8. Ingram, Beth F. & Whiteman, Charles H., 1994. "Supplanting the 'Minnesota' prior: Forecasting macroeconomic time series using real business cycle model priors," Journal of Monetary Economics, Elsevier, vol. 34(3), pages 497-510, December.
    9. Smith, A A, Jr, 1993. "Estimating Nonlinear Time-Series Models Using Simulated Vector Autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 63-84, Suppl. De.
    10. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
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    Cited by:

    1. Tim Robinson, 2013. "Estimating and Identifying Empirical BVAR-DSGE Models for Small Open Economies," RBA Research Discussion Papers rdp2013-06, Reserve Bank of Australia.

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