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[数量经济学研讨会​]Kernel Estimation for Panel Data with Heterogeneous Dynamics
发布日期:2018-11-10
数量经济学研讨会

时间:11月22日 中午12:30
地点:明德主楼729
报告人:Ryo Okui

题目:Kernel Estimation for Panel Data with Heterogeneous Dynamics (joint with Takahide Yanagi)
摘要:This paper proposes nonparametric kernel-smoothing estimation for panel data to examine the degree of heterogeneity across cross-sectional units. Our procedure is model-free and easy to implement, and provides useful visual information, which enables us to understand intuitively the properties of heterogeneity. We first estimate the sample mean, autocovariances, and auto-correlations for each unit and then apply kernel smoothing to compute estimates of their density and cumulative distribution functions. The kernel estimators are consistent and asymptotically normal under double asymptotics, i.e., when both cross-sectional and time series sample sizes tend to infinity. However, as these exhibit biases given the incidental parameter problem and the nonlinearity of the kernel function, we propose jackknife methods to alleviate any bias. We also develop bandwidth selection methods and bootstrap inferences based on the asymptotic properties. Lastly, we illustrate the success of our procedure using an empirical application of the dynamics of US prices and Monte Carlo simulation.

报告人简介:Ryo Okui(奥井亮),上海纽约大学副教授,宾夕法尼亚大学博士,曾任教于香港科技大学与京都大学,研究方向为计量经济学理论与应用,研究发表于 Econometrica, Review of Economic Studies, Journal of Econometrics, Econometric Theory, Journal of Applied Econometrics等知名国际期刊
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