Forecasting with Approximate Dynamic Factor Models: The Role of Non-Pervasive Shocks

24 Pages Posted: 14 Sep 2011

See all articles by Matteo Luciani

Matteo Luciani

Board of Governors of the Federal Reserve System

Date Written: July 1, 2011

Abstract

In this paper we investigate whether accounting for non-pervasive shocks improves the forecast of a factor model. We compare four models on a large panel of US quarterly data: factor models, factor models estimated on selected variables, Bayesian shrinkage, and factor models together with Bayesian shrinkage for the idiosyncratic component. The results of the forecasting exercise show that the four approaches considered perform equally well and produce highly correlated forecasts, meaning that non-pervasive shocks are of no helps in forecasting. We conclude that comovements captured by factor models are informative enough to make accurate forecasts.

Keywords: Dynamic Factor Models, Penalized Regressions, Local Factors, Bayesian Shrinkage, Forecasting

JEL Classification: C13, C32, C33, C52, C53

Suggested Citation

Luciani, Matteo, Forecasting with Approximate Dynamic Factor Models: The Role of Non-Pervasive Shocks (July 1, 2011). Available at SSRN: https://ssrn.com/abstract=1925807 or http://dx.doi.org/10.2139/ssrn.1925807

Matteo Luciani (Contact Author)

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

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