Factor Analysis of a Large DSGE Model

Alexei Onatski () and Francisco Ruge-Murcia

Cahiers de recherche from Universite de Montreal, Departement de sciences economiques

Abstract: We study the workings of the factor analysis of high-dimensional data using artificial series generated from a large, multi-sector dynamic stochastic general equilibrium (DSGE) model. The objective is to use the DSGE model as a laboratory that allows us to shed some light on the practical benefits and limitations of using factor analysis techniques on economic data. We explain in what sense the artificial data can be thought of having a factor structure, study the theoretical and finite sample properties of the principal components estimates of the factor space, investigate the substantive reason(s) for the good performance of diffusion index forecasts, and assess the quality of the factor analysis of highly disaggregated data. In all our exercises, we explain the precise relationship between the factors and the basic macroeconomic shocks postulated by the model.

Keywords: Multisector economies; principal components; forecasting; pervasiveness; FAVAR. (search for similar items in EconPapers)
JEL-codes: C3 C5 E3 (search for similar items in EconPapers)
Pages: 61 pages
Date: 2010
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https://papyrus.bib.umontreal.ca/xmlui/handle/1866/4231 (application/pdf)

Related works:
Journal Article: FACTOR ANALYSIS OF A LARGE DSGE MODEL (2013)
Working Paper: Factor Analysis of a Large DSGE Model (2010)
Working Paper: Factor Analysis of a Large DSGE Model (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:mtl:montde:2010-08

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