日程安排：

16:00-17:00
 Sangyeol Lee教授：Parameter Change Test for Poisson Autoregressive Models
摘    要：In this paper, we consider the problem of testing for a parameter change in Poisson autoregressive models. We suggest two types of cumulative sum (CUSUM)tests, namely, those based on estimates and residuals. We fisrt demonstrate that theconditional maximum likelihood estimator (CMLE) is strongly consistent and asymp-totically normal and then construct the CMLE-based CUSUM test. It is shown thatunder regularity conditions, its limiting null distribution is a function of independentBrownian bridges. Next, we construct the residual-based CUSUM test and derive itslimiting null distribution.  A real data is performed on data for polio incidence.
 
17:00-18:00
Cathy W.S.Chen教授：Bayesian estimation of smoothly mixing time-varying parameter GARCH models
摘    要：Smoothly time-varying (TV) GARCH models via an asymmetric logistic function mechanism are proposed, which are incorporated into the conditional volatility equation for capturing smooth structural breaks in the volatility of financial time series. The proposed models allow smooth transitions of varying ‘‘speed’’ between multiple, persistent regimes. A Bayesian computational method is employed to identify the locations of smooth structuraltransitions, and for estimation and inference, simultaneously accounting for heteroskedasticity. An informative prior is proposed to help ensure identification and allow accurate inference. The proposed methods are illustrated using simulated data, and an empirical study provides evidence for significant improvements in fit for the proposed smooth asymmetric time-varying volatility TV-GARCH models in two international stock market return series. A forecast study shows the proposed models significantly add to forecast accuracy for both volatility and Value-at-Risk.