A0686
Title: A review on semiparametric model averaging for dynamic time series with application to economic risk forecasting
Authors: Zudi Lu - University of Southampton (United Kingdom) [presenting]
Abstract: The purpose is to review some recent progress on semiparametric model averaging schemes for nonlinear dynamic time series regression models with a very large number of covariates including exogenous regressors and autoregressive lags. Our objective is to obtain more accurate estimates and forecasts of time series by using a large number of conditioning variables in a nonparametric way. We have proposed several semiparametric penalized methods of Model Averaging MArginal Regression (MAMAR) for the regressors and auto-regressors either through an initial screening procedure to screen out the regressors whose marginal contributions are not significant in estimating the joint multivariate regression function or by imposing an approximate factor modelling structure on the ultra-high dimensional exogenous regressors with principal component analysis used to estimate the latent common factors. In either case, we construct the optimal combination of the significant marginal regression and auto-regression functions to approximate the objective joint multivariate regression function. Asymptotic properties for these schemes are derived under some regularity conditions. Empirical applications of the proposed methodology to forecasting the economic risk, such as inflation risk in the UK, will be demonstrated.
