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Factor Analysis of a Large DSGE Model

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  • ONATSKI, Alexei
  • RUGE-MURCIA, Francisco J.

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.

Suggested Citation

  • ONATSKI, Alexei & RUGE-MURCIA, Francisco J., 2010. "Factor Analysis of a Large DSGE Model," Cahiers de recherche 2010-08, Universite de Montreal, Departement de sciences economiques.
    • Handle: RePEc:mtl:montde:2010-08
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    Cited by:

    1. Ivashchenko, S., 2020. "Long-term growth sources for sectors of Russian economy," Journal of the New Economic Association, New Economic Association, vol. 48(4), pages 86-112.
    2. Francisco J. Ruge-Murcia & Alexander L. Wolman, 2022. "Relative Price Shocks and Inflation," Working Paper 22-07, Federal Reserve Bank of Richmond.

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    More about this item

    Keywords

    Multisector economies; principal components; forecasting; pervasiveness; FAVAR.;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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