Hierarchical Shrinkage in Time-Varying Parameter Models

被引：70

作者：

Belmonte, Miguel A. G. ^{[1
]}

Koop, Gary ^{[1
]}

Korobilis, Dimitris ^{[2
]}

机构：

[1] Univ Strathclyde, Glasgow, Lanark, Scotland

[2] Univ Glasgow, Adam Smith Business Sch, Glasgow G12 8RT, Lanark, Scotland

来源：

JOURNAL OF FORECASTING | 2014年 / 33卷 / 01期

关键词：

forecasting; hierarchical prior; time-varying parameters; Bayesian Lasso; INFLATION;

D O I：

10.1002/for.2276

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

In this paper, we forecast EU area inflation with many predictors using time-varying parameter models. The facts that time-varying parameter models are parameter rich and the time span of our data is relatively short motivate a desire for shrinkage. In constant coefficient regression models, the Bayesian Lasso is gaining increasing popularity as an effective tool for achieving such shrinkage. In this paper, we develop econometric methods for using the Bayesian Lasso with time-varying parameter models. Our approach allows for the coefficient on each predictor to be: (i) time varying; (ii) constant over time; or (iii) shrunk to zero. The econometric methodology decides automatically to which category each coefficient belongs. Our empirical results indicate the benefits of such an approach. Copyright (c) 2013 John Wiley & Sons, Ltd.

引用

页码：80 / 94

页数：15

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