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The small sample properties of Indirect Inference in testing and estimating DSGE models

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Abstract

Indirect inference testing can be carried out with a variety of auxiliary models. Asymptotically these different models make no difference. However, the small sample properties can differ. We explore small sample power and estimation bias both with different variable combinations and descriptive models (Vector Auto Regressions, Impulse Response Functions or Moments) in the auxiliary model. We find that both power and bias are similar when the number of variables used is the same. Raising the number of variables lowers the bias but may also raise the power unacceptably because it lowers the chances of finding a tractable model to pass the test.

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  • 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.
  • Handle: RePEc:cdf:wpaper:2018/7
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    15. Le, Vo Phuong Mai & Meenagh, David & Minford, Patrick, 2016. "Monetarism rides again? US monetary policy in a world of Quantitative Easing," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 85-102.
    16. 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.
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    1. Le, Vo Phuong Mai & Meenagh, David & Minford, Patrick, 2019. "A long-commodity-cycle model of the world economy over a century and a half — Making bricks with little straw," Energy Economics, Elsevier, vol. 81(C), pages 503-518.

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    More about this item

    Keywords

    Indirect Inference; DGSE model; Auxiliary Models; Simulated Moments Method; Impulse Response Functions; VAR; Moments; power; bias;
    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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