Estimation and inference for high dimensional factor model with regime switching
Holder： Fa Wang （Peking University）
Location：Room 217, Guanghua Building 2
This paper proposes maximum (quasi)likelihood estimation for high dimensional factor models with regime switching in the loadings. The model para- meters are estimated jointly by the EM (expectation maximization) algorithm, which in the current context only requires iteratively calculating regime prob- abilities and principal components of the weighted sample covariance matrix. When regime dynamics are taken into account, smoothed regime probabilities are calculated using a recursive algorithm. Consistency, convergence rates and limit distributions of the estimated loadings and the estimated factors are established under weak cross-sectional and temporal dependence as well as heteroscedasticity. It is worth noting that due to high dimension, regime switching can be identified consistently after the switching point with only one observation. Simulation results show good performance of the proposed method. An application to the FRED-MD dataset illustrates the potential of the proposed method for detection of business cycle turning points.
About the Speaker:
王法博士于2020年9月加入北京大学经济学院，担任金融学副教授。他2016年博士毕业于美国雪城大学，曾任教于上海财经大学和伦敦大学卡斯商学院。他的研究领域是金融计量经济学和高维时间序列，近期主要方向是非线性因子模型及其在资产定价，宏观预测和经济周期分析中的应用。他在Journal of Econometrics, Econometric Reviews等期刊发表多篇论文，并多次担任Journal of Econometrics, Journal of Business and Economic Statistics等期刊的审稿人。他讲授的课程包括金融计量经济学，衍生品定价，理论统计学，固定收益证券，时间序列分析等。
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