【主讲】Degui Li Professor (Department of Mathematics, The university of York)
【主题】Semiparametric Ultra-High Dimensional Model Averaging of Nonlinear Dynamic Time Series
【时间】2016年12月15日 (周四) 15：30-17：00
【地点】上海财经大学经济学院楼701室
【语言】英文
【摘要】We propose two semiparametric model averaging schemes for nonlinear dynamic time series regression models with a very large number of covariates including exogenous regressors and auto-regressive lags, aiming to obtain accurate forecasts of a response variable by making use of a large number of conditioning variables in a nonparametric way. In the first scheme, we introduce a Kernel Sure Independence Screening (KSIS) technique to screen out the regressors whose marginal regression (or auto-regression) functions do not make significant contribution to estimating the joint multivariate regression function; we then propose a semiparametric penalisedmethod of Model Averaging MArginal Regression (MAMAR) for the regressors and auto-regressors that survive the screening procedure, to further select the regressors that have significant effects on estimating the multivariate regression function and predicting the future values of the response variable. In the second scheme, we impose an approximate factor modelling structure on the ultra-high dimensional exogenous regressors and use the principal component analysis to estimate the latent common factors; we then apply the penalised MAMAR method to select the estimated common factors and the lags of the response variable that are significant. In each of the two semiparametric schemes, we construct the optimal combination of the significant marginal regression and auto-regression functions. Under some regularity conditions, we derive some asymptotic properties for these two semiparametric schemes. Numerical studies including both simulation and an empirical application are provided to illustrate the proposed methodology. This is a joint work with Jia Chen, Oliver Linton and Zudi Lu.
