Finite-sample corrected inference for two-step GMM in time series

Jungbin Hwang and Gonzalo Valdés

Journal of Econometrics, 2023, vol. 234, issue 1, 327-352

Abstract: This paper develops a finite-sample corrected inference for the efficient generalized method of moments (GMM) in time series. To capture a higher-order uncertainty embodied in estimating the time series GMM weight matrix, we extend the finite-sample corrected variance formula of Windmeijer (2005) to heteroskedasticity autocorrelated robust (HAR) inference. Using fixed-smoothing asymptotics, we show that our finite-sample corrected test statistics lead to standard asymptotic t or F critical values and suffer from less over-rejection of the null hypothesis than existing GMM procedures on finite-samples, including continuously updating GMM. Not only does our finite-sample corrected variance formula correct for the bias arising from the plugged-in long-run variance estimation, but it is also not exposed to a potential side effect of Windmeijer’s formula, which can introduce an additional source of over-rejection after the correction.

Keywords: Generalized method of moments; Heteroskedasticity autocorrelated robust; Finite-sample correction; Fixed-smoothing asymptotics; t and F tests (search for similar items in EconPapers)
JEL-codes: C12 C13 C32 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:234:y:2023:i:1:p:327-352

DOI: 10.1016/j.jeconom.2021.12.007

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