數(shù)量經(jīng)濟學研討會      
      時間：11月22日 中午12:30          地點：明德主樓729          報告人：Ryo Okui          
         題目：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.          
         報告人簡介：Ryo Okui（奧井亮)，上海紐約大學副教授，賓夕法尼亞大學博士，曾任教于香港科技大學與京都大學，研究方向為計量經(jīng)濟學理論與應用，研究發(fā)表于 Econometrica, Review of Economic Studies, Journal of Econometrics, Econometric Theory, Journal of Applied Econometrics等知名國際期刊