主题：Bayesian Rank Selection in Multivariate Regression

时间：2016年6月1（星期三）14：00-15：00

地点：学院南路校区，学术会堂603

报告人：Anastasios Panagiotelis 教授，澳大利亚蒙纳什大学计量经济和商务统计系

报告摘要：

Estimating the rank of the coefficient matrix is a major challenge in multivariate regression, including vector autoregression (VAR). In this paper, we develop a novel fully Bayesian approach that allows for rank estimation. The key to our approach is reparameterizing the coefficient matrix using its singular value decomposition and conducting Bayesian inference on the decomposed parameters. By implementing a stochastic search variable selection on the singular values of the coefficient matrix, the ultimate selected rank can be identified as the number of nonzero singular values. Our approach is appropriate for small multivariate regressions as well as for higher dimensional models with up to about 40 predictors. In macroeconomic forecasting using VARs, the advantages of shrinkage through proper Bayesian priors are well documented. Consequently, the shrinkage approach proposed here that selects or averages over low rank coefficient matrices is evaluated in a forecasting environment. We show in both simulations and empirical studies that our Bayesian approach provides forecasts that are highly competitive against two of the most promising benchmark methods, dynamic factor models and factor augmented VARs.

报告人简介：

Anastasios Panagiotelis 教授就职于澳大利亚蒙纳什大学计量经济和商务统计系。他是统计计算和贝叶斯计算领域的权威专家，在本领域国际著名期刊Journal of the American Statistical Association、Journal of Econometrics等发表发表学术论文多篇，同时还主持了多项澳大利亚国家科学基金项目。此外还担任Journal of Econometrics, Journal of Economic Dynamics and Control, International Journal of Forecasting, Computational Statistics and Data Analysis, Technometrics, Australian and New Zealand Journal of Statistics, Electronic Journal of Statistics, and the Journal of Statistical Software, Competitive Grant for the Estonian Research Council等多个国际统计期刊的审稿。他的最新成果在Joint Statistical Meetings， International Society for Bayesian Analysis World Meeting， International Symposium on Forecasting等多个国际会议作为特邀嘉宾做主旨报告。他提出和发展了贝叶斯计算分析领域的一些重要的方法，如离散高维Copula建模、贝叶斯高维有偏选择，贝叶斯密度预测方法、贝叶斯半参数可加模型等，并将这些方法应用到金融、互联网金融等多个领域，取得了丰富的科研成果。