题目：Optimal Bandwidth Selection in Nonlinear Cointegrating Regression
报告人：Professor Qiying Wang (悉尼大学数学与统计学院)
We study optimal bandwidth selection in nonparametric cointegrating regression where the regressor is a stochastic trend process driven by short or long memory innovations. Unlike stationary regression, the optimal bandwidth is found to be a random sequence which depends on the sojourn time of the process. All random sequences that lie within a wide band of rates as the sample size goes to infinite have the property that local level and local linear kernel estimates are asymptotically normal, which enables inference and conveniently corresponds to limit theory in the stationary regression case. This finding reinforces the distinctive flexibility of data-based nonparametric regression procedures for nonstationary nonparametric regression. This is a joint work with Prof Peter Phillips at Yale University.
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