This seminar will be delivered by Ryo Okui (Kyoto University). It is based on a paper ‘Panel Data Analysis with Heterogeneous Dynamics’ by Ryo Okui and Takahide Yanagi.
This paper proposes the analysis of panel data whose dynamic structure is heteroge- neous across individuals. Our proposed method is easy to implement without assuming any specific model for the dynamics. We first compute the sample mean, autocovariances, and autocorrelations for each individual and then estimate the parameter of interest based on the empirical distributions of the estimated mean, autocovariances, and autocorrelations. We first illustrate the usefulness of our proposed procedures by applying earning dynamics and productivity dynamics and find that both dynamics exhibit lots of heterogeneity.
The asymptotic properties of the proposed estimators are then investigated using double asymp- totics under which both the cross-sectional sample size and the length of the time series tend to infinity. We prove the functional central limit theorem for the proposed distribu- tion estimator. If the parameter of interest can be written as the expectation of a smooth function of the individual mean and/or autocovariances, the bias can be reduced by the split-panel jackknife bias-correction. We also develop an inference procedure based on the cross-sectional bootstrap. The results of Monte Carlo simulations show that our asymptotic results are informative regarding the finite-sample properties.