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Comparing different data descriptors in Indirect Inference tests on DSGE models

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  • Minford, Patrick
  • Wickens, Michael
  • Xu, Yongdeng

Abstract

Indirect inference testing can be carried out with a variety of auxiliary models. Asymptotically these different models make no difference. However, in small samples power can differ. We explore small sample power with three different auxiliary models: a VAR, average Impulse Response Functions and Moments. The latter corresponds to the Simulated Moments Method. We find that in a small macro model there is no difference in power. But in a large complex macro model the power with Moments rises more slowly with increasing misspecification than with the other two which remain similar.

Suggested Citation

  • Minford, Patrick & Wickens, Michael & Xu, Yongdeng, 2016. "Comparing different data descriptors in Indirect Inference tests on DSGE models," Economics Letters, Elsevier, vol. 145(C), pages 157-161.
  • Handle: RePEc:eee:ecolet:v:145:y:2016:i:c:p:157-161
    DOI: 10.1016/j.econlet.2016.06.016
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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. Guerron-Quintana, Pablo & Inoue, Atsushi & Kilian, Lutz, 2017. "Impulse response matching estimators for DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 144-155.
    3. M. R. Wickens, 1982. "The Efficient Estimation of Econometric Models with Rational Expectations," Review of Economic Studies, Oxford University Press, vol. 49(1), pages 55-67.
    4. McCallum, Bennett T, 1976. "Rational Expectations and the Natural Rate Hypothesis: Some Consistent Estimates," Econometrica, Econometric Society, vol. 44(1), pages 43-52, January.
    5. 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.
    6. Mark Gertler & Jordi Gali & Richard Clarida, 1999. "The Science of Monetary Policy: A New Keynesian Perspective," Journal of Economic Literature, American Economic Association, vol. 37(4), pages 1661-1707, December.
    7. 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.
    8. Michael Wickens, 2014. "How Useful are DSGE Macroeconomic Models for Forecasting?," Open Economies Review, Springer, vol. 25(1), pages 171-193, February.
    9. Mark Gertler & Jordi Gali & Richard Clarida, 1999. "The Science of Monetary Policy: A New Keynesian Perspective," Journal of Economic Literature, American Economic Association, vol. 37(4), pages 1661-1707, December.
    10. 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.
    11. Vo Le & David Meenagh & Patrick Minford & Michael Wickens & Yongdeng Xu, 2016. "Testing Macro Models by Indirect Inference: A Survey for Users," Open Economies Review, Springer, vol. 27(1), pages 1-38, February.
    12. Vo Le & David Meenagh & Patrick Minford & Michael Wickens & Yongdeng Xu, 2016. "Testing Macro Models by Indirect Inference: A Survey for Users," Open Economies Review, Springer, vol. 27(1), pages 1-38, February.
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    Citations

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    Cited by:

    1. David Meenagh & Patrick Minford & Michael Wickens & Yongdeng Xu, 2019. "Testing DSGE Models by Indirect Inference: a Survey of Recent Findings," Open Economies Review, Springer, vol. 30(3), pages 593-620, July.
    2. Le, Vo Phuong Mai & Meenagh, David & Minford, Patrick & Wickens, Michael, 2017. "A Monte Carlo procedure for checking identification in DSGE models," Journal of Economic Dynamics and Control, Elsevier, vol. 76(C), pages 202-210.
    3. Meenagh, David & Minford, Patrick & Wickens, Michael & Xu, Yongdeng, 2018. "The small sample properties of Indirect Inference in testing and estimating DSGE models," Cardiff Economics Working Papers E2018/7, Cardiff University, Cardiff Business School, Economics Section.
    4. repec:kap:openec:v:30:y:2019:i:1:d:10.1007_s11079-018-9512-1 is not listed on IDEAS
    5. Ponomarenko, Alexey, 2019. "A note on observational equivalence of micro assumptions on macro level," Economics Discussion Papers 2019-51, Kiel Institute for the World Economy (IfW).

    More about this item

    Keywords

    Indirect Inference; DGSE model; Auxiliary models; Simulated Moments Method;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E1 - Macroeconomics and Monetary Economics - - General Aggregative Models

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