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How good are out of sample forecasting Tests on DSGE models?

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  • Minford, Patrick
  • Xu, Yongdeng
  • Zhou, Peng

Abstract

Out-of-sample forecasting tests of DSGE models against time-series benchmarks such as an unrestricted VAR are increasingly used to check a) the specification b) the forecasting capacity of these models. We carry out a Monte Carlo experiment on a widely-used DSGE model to investigate the power of these tests. We find that in specification testing they have weak power relative to an in-sample indirect inference test; this implies that a DSGE model may be badly mis-specified and still improve forecasts from an unrestricted VAR. In testing forecasting capacity they also have quite weak power, particularly on the lefthand tail. By contrast a model that passes an indirect inference test of specification will almost definitely also improve on VAR forecasts.

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  • Minford, Patrick & Xu, Yongdeng & Zhou, Peng, 2014. "How good are out of sample forecasting Tests on DSGE models?," CEPR Discussion Papers 10239, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:10239
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    1. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
    2. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    3. Michael Wickens, 2014. "How Useful are DSGE Macroeconomic Models for Forecasting?," Open Economies Review, Springer, vol. 25(1), pages 171-193, February.
    4. Michael P. Clements & David F. Hendry, 2005. "Evaluating a Model by Forecast Performance," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 931-956, December.
    5. Le, Vo Phuong Mai & Meenagh, David & Minford, Patrick, 2012. "What causes banking crises? An empirical investigation," CEPR Discussion Papers 9057, C.E.P.R. Discussion Papers.
    6. Le, Vo Phuong Mai & Meenagh, David & Minford, Patrick & Wickens, Michael, 2011. "How much nominal rigidity is there in the US economy? Testing a new Keynesian DSGE model using indirect inference," Journal of Economic Dynamics and Control, Elsevier, vol. 35(12), pages 2078-2104.
    7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    8. Gürkaynak, Refet S. & Kisacikoglu, Burçin & Rossi, Barbara, 2013. "Do DSGE Models Forecast More Accurately Out-of-Sample than VAR Models?," CEPR Discussion Papers 9576, C.E.P.R. Discussion Papers.
    9. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    10. Rochelle M. Edge & Michael T. Kiley & Jean-Philippe Laforte, 2009. "A comparision of forecast, simple reduced-form models, and a DSGE model," CAMA Working Papers 2009-03, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    11. Rochelle M. Edge & Michael T. Kiley & Jean-Philippe Laforte, 2010. "A comparison of forecast performance between Federal Reserve staff forecasts, simple reduced-form models, and a DSGE model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 720-754.
    12. Michel Juillard, 2001. "DYNARE: A program for the simulation of rational expectation models," Computing in Economics and Finance 2001 213, Society for Computational Economics.
    13. Kenneth S. Rogoff & Vania Stavrakeva, 2008. "The Continuing Puzzle of Short Horizon Exchange Rate Forecasting," NBER Working Papers 14071, National Bureau of Economic Research, Inc.
    14. 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.
    15. Ince, Onur, 2014. "Forecasting exchange rates out-of-sample with panel methods and real-time data," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 1-18.
    16. Le, Vo Phuong Mai & Meenagh, David & Minford, Patrick & Wickens, Michael, 2012. "Testing DSGE models by Indirect inference and other methods: some Monte Carlo experiments," Cardiff Economics Working Papers E2012/15, Cardiff University, Cardiff Business School, Economics Section.
    17. Warne, Anders & Coenen, Günter & Christoffel, Kai, 2010. "Forecasting with DSGE models," Working Paper Series 1185, European Central Bank.
    18. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
    19. Rochelle M. Edge & Refet S. Gurkaynak, 2010. "How Useful Are Estimated DSGE Model Forecasts for Central Bankers?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 41(2 (Fall)), pages 209-259.
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    Cited by:

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    3. Loberto, Michele & Perricone, Chiara, 2017. "Does trend inflation make a difference?," Economic Modelling, Elsevier, vol. 61(C), pages 351-375.

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    More about this item

    Keywords

    DSGE; forecast performance; indirect inference; out of sample forecasts; specification tests; VAR;
    All these keywords.

    JEL classification:

    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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