[数量经济学研讨会]Forecasting in the presence of in and out of sample breaks
发文时间:2013-11-29

数量经济学研讨会

【201306

报告题目:Forecasting in the presence of in and out of sample breaks

报告人:徐佳助理教授

报告时间:2013年12月3日下午12:30-13:30

报告地点:明德主楼729

内容简介:

We present a frequentist-based approach to forecast time series in the presence of in-sample and out-of-sample breaks in the parameters of the forecasting model. We first model the parameters as following a random level shift process, with the occurrence of a shift governed by a Bernoulli process. In order to have a structure so that changes in the parameters be forecastable, we introduce two modifications. The first models the probability of shifts according to some covariates that can be forecasted. The second incorporates a built-in mean reversion mechanism to the time path of the parameters. Similar modifications can also be made to model changes in the variance of the error process. Our full model can be cast into a non-linear non-Gaussian state space framework. To estimate it, we use particle filtering and a Monte Carlo expectation maximization algorithm. Simulation results show that the algorithm delivers accurate in-sample estimates, in particular the filtered estimates of the time path of the parameters follow closely their true variations. We provide a number of empirical applications and compare the forecasting performance of our approach with a variety of alternative methods. These show that substantial gains in forecasting accuracy are obtained.

报告人简介:

徐佳,上海财经大学经济学院助理教授。2013年毕业于波士顿大学经济系,她的主要研究方向为计量经济学,时间序列,金融计量经济学。

中国人民大学经济学院数量经济教研室

中国人民大学运筹学与数量经济研究所

2013年12月

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