Kernel estimation for panel data with heterogeneous dynamics

Ryo Okui and Takahide Yanagi

The Econometrics Journal, 2020, vol. 23, issue 1, 156-175

Abstract: SummaryThis 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 () and the time-series length (). In particular, it makes the condition onandstronger 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.

Keywords: Autocorrelation; density estimation; heterogeneity; incidental parameter; jackknife; kernel smoothing (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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Working Paper: Kernel Estimation for Panel Data with Heterogeneous Dynamics (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:23:y:2020:i:1:p:156-175.

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