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Estimating and Identifying Empirical BVAR-DSGE Models for Small Open Economies

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  • Tim Robinson

    (Reserve Bank of Australia)

Abstract

Different approaches to modelling the macroeconomy vary in the emphasis they place on coherence with theory relative to their ability to match the data. Dynamic stochastic general equilibrium (DSGE) models place greater emphasis on theory, while vector autoregression (VAR) models tend to provide a better fit of the data. Del Negro and Schorfheide (2004) develop a method of using a DSGE model to inform the priors of a Bayesian VAR. The resulting BVAR-DSGE model partially relaxes the relationships in the DSGE so as to fit the data better. However, their approach does not accommodate the typical restriction of small open economy models which ensures that developments in the small economy cannot affect the large economy. I develop a method that allows this restriction to be imposed and introduce a simple way, suitable for small open economies, of identifying the empirical BVAR-DSGE using information from the DSGE model. These methods are demonstrated using the Justiniano and Preston (2010a) DSGE model. Compared to the DSGE model, the empirical BVAR-DSGE model estimates that there is a larger role for foreign shocks in the small economy's business cycle.

Suggested Citation

  • Tim Robinson, 2013. "Estimating and Identifying Empirical BVAR-DSGE Models for Small Open Economies," RBA Research Discussion Papers rdp2013-06, Reserve Bank of Australia.
  • Handle: RePEc:rba:rbardp:rdp2013-06
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    References listed on IDEAS

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    1. Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez & Thomas J. Sargent & Mark W. Watson, 2007. "ABCs (and Ds) of Understanding VARs," American Economic Review, American Economic Association, vol. 97(3), pages 1021-1026, June.
    2. Lees, Kirdan & Matheson, Troy & Smith, Christie, 2011. "Open economy forecasting with a DSGE-VAR: Head to head with the RBNZ published forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 512-528.
    3. Renée Fry & Adrian Pagan, 2011. "Sign Restrictions in Structural Vector Autoregressions: A Critical Review," Journal of Economic Literature, American Economic Association, vol. 49(4), pages 938-960, December.
    4. Lubik, Thomas A. & Schorfheide, Frank, 2007. "Do central banks respond to exchange rate movements? A structural investigation," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1069-1087, May.
    5. Philip Liu & Konstantinos Theodoridis, 2012. "DSGE Model Restrictions for Structural VAR Identification," International Journal of Central Banking, International Journal of Central Banking, vol. 8(4), pages 61-95, December.
    6. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    7. Justiniano, Alejandro & Preston, Bruce, 2010. "Can structural small open-economy models account for the influence of foreign disturbances?," Journal of International Economics, Elsevier, vol. 81(1), pages 61-74, May.
    8. Marco Del Negro & Frank Schorfheide, 2004. "Priors from General Equilibrium Models for VARS," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(2), pages 643-673, May.
    9. Alejandro Justiniano & Bruce Preston, 2010. "Monetary policy and uncertainty in an empirical small open‐economy model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 93-128, January.
    10. 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.
    11. Kuttner, Ken & Robinson, Tim, 2010. "Understanding the flattening Phillips curve," The North American Journal of Economics and Finance, Elsevier, vol. 21(2), pages 110-125, August.
    12. Thomai Filippeli, 2011. "Theoretical Priors for BVAR Models & Quasi-Bayesian DSGE Model Estimation," 2011 Meeting Papers 396, Society for Economic Dynamics.
    13. Waggoner, Daniel F. & Zha, Tao, 2003. "A Gibbs sampler for structural vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 28(2), pages 349-366, November.
    14. Philip Liu, 2007. "Stabilizing The Australian Business Cycle: Good Luck Or Good Policy?," CAMA Working Papers 2007-24, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    15. Michael Plumb & Christopher Kent & James Bishop, 2013. "Implications for the Australian Economy of Strong Growth in Asia," RBA Research Discussion Papers rdp2013-03, Reserve Bank of Australia.
    16. Leon Berkelmans, 2005. "Credit and Monetary Policy: An Australian SVAR," RBA Research Discussion Papers rdp2005-06, Reserve Bank of Australia.
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    Cited by:

    1. Andrew Binning & Junior Maih, 2015. "Applying Flexible Parameter Restrictions in Markov-Switching Vector Autoregression Models," Working Paper 2015/17, Norges Bank.
    2. Oana Simona HUDEA, 2016. "The New Keynesian Theory And Its Associated Model," Network Intelligence Studies, Romanian Foundation for Business Intelligence, Editorial Department, issue 8, pages 151-159, December.
    3. Sean Langcake & Tim Robinson, 2013. "An Empirical BVAR-DSGE Model of the Australian Economy," RBA Research Discussion Papers rdp2013-07, Reserve Bank of Australia.

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    More about this item

    Keywords

    BVAR-DSGE; small open economy;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

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