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Finite-sample Corrected Inference for Two-step GMM in Time Series

Author

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  • Jungbin Hwang

    (University of Connecticut)

  • Gonzalo Valdés

    (Universidad de Tarapacá)

Abstract

This paper develops a nite-sample corrected and robust inference for e¢ cient two-step generalized method of moments (GMM). One of the main challenges in e¢ cient GMM is that we do not observe the moment process and have to use the estimated moment process to construct a GMM weighting matrix. We use a non-parametric long run variance estimator as the optimal GMM weighting matrix. To capture the estimation uncertainty embodied in the weight matrix, we extend the nite-sample corrected formula of Windmeijer (2005) to a heteroskedasticity autocorrelated robust (HAR) inference in time series setting. Using xed-smoothing asymptotics, we show that our new test statistics lead to standard asymptotic F or t critical values and improve the nite sample performance of existing HAR robust GMM tests.

Suggested Citation

  • Jungbin Hwang & Gonzalo Valdés, 2020. "Finite-sample Corrected Inference for Two-step GMM in Time Series," Working papers 2020-02, University of Connecticut, Department of Economics.
    • Handle: RePEc:uct:uconnp:2020-02
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    References listed on IDEAS

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    More about this item

    Keywords

    Generalized Method of Moments; Heteroskedasticity Autocorrelated Robust; Finite-sample Correction; Fixed-smoothing Asymptotics; t and F tests.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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

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