# Comparing the DSGE model with the factor model: an out-of-sample forecasting experiment

*Mu-Chun Wang*

*Journal of Forecasting*, 2009, vol. 28, issue 2, pages 167-182

**Abstract:**
In this paper, we put dynamic stochastic general equilibrium DSGE forecasts in competition with factor forecasts. We focus on these two models since they represent nicely the two opposing forecasting philosophies. The DSGE model on the one hand has a strong theoretical economic background; the factor model on the other hand is mainly data-driven. We show that incorporating a large information set using factor analysis can indeed improve the short-horizon predictive ability, as claimed by many researchers. The micro-founded DSGE model can provide reasonable forecasts for US inflation, especially with growing forecast horizons. To a certain extent, our results are consistent with the prevailing view that simple time series models should be used in short-horizon forecasting and structural models should be used in long-horizon forecasting. Our paper compares both state-of-the-art data-driven and theory-based modelling in a rigorous manner. Copyright © 2008 John Wiley & Sons, Ltd.

**Date:** 2009

**References:** Add references at CitEc

**Citations** View citations in EconPapers (13) Track citations by RSS feed

**Downloads:** (external link)

http://hdl.handle.net/10.1002/for.1081 Link to full text; subscription required (text/html)

**Related works:**

Working Paper: Comparing the DSGE model with the factor model: an out-of-sample forecasting experiment (2008)

This item may be available elsewhere in EconPapers: Search for items with the same title.

**Export reference:** BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text

**Persistent link:** http://EconPapers.repec.org/RePEc:jof:jforec:v:28:y:2009:i:2:p:167-182

Access Statistics for this article

Journal of Forecasting is currently edited by *Derek W. Bunn*

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.

Series data maintained by Wiley-Blackwell Digital Licensing ().