Kernel Estimation for Panel Data with Heterogeneous Dynamics

51 Pages Posted: 5 Mar 2018 Last revised: 29 May 2019

See all articles by Ryo Okui

Ryo Okui

Seoul National University

Takahide Yanagi

Kyoto University - Graduate School of Economics

Date Written: May 2019

Abstract

This paper proposes nonparametric kernel-smoothing estimation for panel data to examine the degree of heterogeneity across cross-sectional units. We first estimate the sample mean, autocovariances, and autocorrelations for each unit and then apply kernel smoothing to compute their density functions. The dependence of the kernel estimator on bandwidth makes asymptotic bias of very high order affect the required condition on the relative magnitudes of the cross-sectional sample size (N) and the time-series length (T). In particular, it makes the condition on N and T stronger and more complicated than those typically observed in the long-panel literature without kernel smoothing. We also consider a split-panel jackknife method to correct bias and construction of confidence intervals. An empirical application and Monte Carlo simulations illustrate our procedure in finite samples.

Keywords: autocorrelation, density estimation, heterogeneity, incidental parameter, jackknife, kernel smoothing

JEL Classification: C13, C14, C23

Suggested Citation

Okui, Ryo and Yanagi, Takahide, Kernel Estimation for Panel Data with Heterogeneous Dynamics (May 2019). Available at SSRN: https://ssrn.com/abstract=3128885 or http://dx.doi.org/10.2139/ssrn.3128885

Ryo Okui

Seoul National University ( email )

Seoul
Korea, Republic of (South Korea)

Takahide Yanagi (Contact Author)

Kyoto University - Graduate School of Economics ( email )

Yoshida Honmachi
Sakyo
Kyoto, Kyoto 6068501
Japan

HOME PAGE: http://https://sites.google.com/view/takahide-yanagi/

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