Kernel estimation for panel data with heterogeneous dynamics

Summary 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 fu...

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Bibliographic Details
Published in:The econometrics journal 2020-01, Vol.23 (1), p.156-175
Main Authors: Okui, Ryo, Yanagi, Takahide
Format: Article
Language:English
Subjects:
Bias
Density
Longitudinal studies
Panel data
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Summary:Summary 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 illustrates our procedure.
ISSN:1368-4221
1368-423X
DOI:10.1093/ectj/utz019