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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.

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  • 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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    10. 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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    Cited by:

    1. Meenagh, David & Minford, Patrick & Xu, Yongdeng, 2022. "Targeting moments for calibration compared with indirect inference," Cardiff Economics Working Papers E2022/12, Cardiff University, Cardiff Business School, Economics Section.
    2. 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.
    3. Patrick Minford & Michael Wickens & Yongdeng Xu, 2019. "Testing Part of a DSGE Model by Indirect Inference," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(1), pages 178-194, February.
    4. Meenagh, David & Minford, Patrick & Xu, Yongdeng, 2023. "Indirect Inference and Small Sample Bias - Some Recent Results," Cardiff Economics Working Papers E2023/15, Cardiff University, Cardiff Business School, Economics Section.
    5. Le, Vo Phuong Mai & Meenagh, David & Minford, Patrick, 2018. "Oil and Commodities Drive the World Business Cycle: A Long-Commodity-Cycle Model of the World Economy Over a Century and a Half," Cardiff Economics Working Papers E2018/16, Cardiff University, Cardiff Business School, Economics Section.
    6. Sevgi Coskun, 2020. "Technology Shocks and Non-stationary Hours in Emerging Countries and DSVAR," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 14(2), pages 129-163, May.
    7. 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.
    8. 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.
    9. Anna Watson, 2019. "Financial Frictions, the Great Trade Collapse and International Trade over the Business Cycle," Open Economies Review, Springer, vol. 30(1), pages 19-64, February.
    10. Ponomarenko, Alexey, 2020. "A note on observational equivalence of micro assumptions on macro level," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-15.
    11. Minford, Patrick & Xu, Yongdeng, 2024. "Indirect Inference- a methodological essay on its role and applications," Cardiff Economics Working Papers E2024/1, Cardiff University, Cardiff Business School, Economics Section.

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    More about this item

    Keywords

    Indirect Inference; DGSE model; Auxiliary models; Simulated Moments Method;
    All these keywords.

    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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