Forecasting with approximate dynamic factor models: The role of non-pervasive shocks

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• 作者：Matteo Luciani
• 关键词：Dynamic factor models ; Penalized regressions ; Local factors ; Bayesian shrinkage ; Forecasting
• 刊名：International Journal of Forecasting
• 出版年：January-March, 2014
• 年：2014
• 卷：30
• 期：1
• 页码：20-29
• 全文大小：401 K

文摘

This paper studies the role of non-pervasive shocks when forecasting with factor models. To this end, we first introduce a new model that incorporates the effects of non-pervasive shocks, an Approximate Dynamic Factor Model with a sparse model for the idiosyncratic component. Then, we test the forecasting performance of this model both in simulations, and on a large panel of US quarterly data. We find that, when the goal is to forecast a disaggregated variable, which is usually affected by regional or sectorial shocks, it is useful to capture the dynamics generated by non-pervasive shocks; however, when the goal is to forecast an aggregate variable, which responds primarily to macroeconomic, i.e. pervasive, shocks, accounting for non-pervasive shocks is not useful.