Asymptotically Unbiased Estimation of Autocovariances and Autocorrelations with Panel Data in the Presence of Individual and Time Effects

41 Pages Posted: 10 Sep 2011

See all articles by Ryo Okui

Ryo Okui

Seoul National University

Date Written: August 19, 2011

Abstract

This paper proposes asymptotically unbiased estimators of autocovariances and autocorrelations for panel data with both individual and time effects. We show that the conventional autocovariance estimators suffers from the bias caused by the elimination of individual and time effects. The bias related to individual effects is proportional to the long-run variance, and that related to time effects is proportional to the value of the estimated autocovariance. For the conventional autocorrelation estimators, the elimination of time effects does not cause a bias while the elimination of individual effects does. We develop methods to estimate the long-run variance and propose bias-corrected estimators based on the proposed long-run variance estimator. The theoretical results are given by employing double asymptotics under which both the number of observations and the length of the time series tend to infinity. Monte Carlo simulations show that the asymptotic theory provides a good approximation to the actual bias and that the proposed bias correction works.

Keywords: double asymptotics, individual effects, long-run variance, panel data, time effects

JEL Classification: C12, C23

Suggested Citation

Okui, Ryo, Asymptotically Unbiased Estimation of Autocovariances and Autocorrelations with Panel Data in the Presence of Individual and Time Effects (August 19, 2011). Available at SSRN: https://ssrn.com/abstract=1924646 or http://dx.doi.org/10.2139/ssrn.1924646

Ryo Okui (Contact Author)

Seoul National University ( email )

Seoul
Korea, Republic of (South Korea)

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