Data-Rich DSGE and Dynamic Factor Models

IMF Working Papers, September 1, 2011

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Data-Rich DSGE and Dynamic Factor Models

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Bibliographic details

• Authors: Maxym Kryshko
• Published: September 1, 2011
• Series: IMF Working Papers
• DOI: https://doi.org/10.5089/9781463903497.001

Summary

• Dynamic factor models and dynamic stochastic general equilibrium (DSGE) models are widely used for empirical research in macroeconomics.
• The empirical factor literature argues that the co-movement of large panels of macroeconomic and financial data can be captured by relatively few common unobserved factors.
• The dynamics in DSGE models are often governed by a handful of state variables and exogenous processes such as preference and/or technology shocks.
• Boivin and Giannoni (2006) combine a DSGE and a factor model into a data-rich DSGE model, in which DSGE states are factors and factor dynamics are subject to DSGE model implied restrictions.
• The paper compares a data-rich DSGE model with a standard New Keynesian core to an empirical dynamic factor model by estimating both on a rich panel of U.S. macroeconomic and financial data compiled by Stock and Watson (2008).
• Key empirical finding: "We find that the spaces spanned by the empirical factors and by the data-rich DSGE model states are very close."
• Practical implication: This proximity allows propagation of monetary policy and technology innovations in an otherwise non-structural dynamic factor model to obtain predictions for many more series than just a handful of traditional macro variables, including:
  • measures of real activity,
  • price indices,
  • labor market indicators,
  • interest rate spreads,
  • money and credit stocks,
  • exchange rates.

Methodology and Models

• Models compared:
  • Data-rich DSGE model (DSGE states treated as factors; factor dynamics subject to DSGE-implied restrictions).
  • Empirical dynamic factor model (DFM).
• Data: A rich panel of U.S. macroeconomic and financial data compiled by Stock and Watson (2008).
• Model core referenced: standard New Keynesian core.

Major Findings

• The empirical factors and the data-rich DSGE model states span very similar spaces.
• Because of this alignment, structural innovations (monetary policy, technology) can be propagated through dynamic factor models to produce predictions for a wide range of macroeconomic and financial series beyond the small set of traditional macro variables.

Subjects and Keywords

• Subjects: Consumption, Dynamic stochastic general equilibrium models, Econometric analysis, Factor models, Financial institutions, Inflation, National accounts, Prices, Stocks
• Keywords: Bayesian estimation, Consumption, Data-rich DSGE models, DFM model, DSGE model, DSGE state, dynamic factor models, Dynamic stochastic general equilibrium models, Factor models, GDP deflator, Inflation, interest rate, investment deflator inflation, model state, monetary policy, Stocks, WP

Content in this bundle

• Appendix A. Dfm: Gibbs Sampler: Drawing Transition Equation Matrix
  • Appendix A. Dfm: Gibbs Sampler: Drawing Transition Equation Matrix (Markdown version)
  • Appendix A. Dfm: Gibbs Sampler: Drawing Transition Equation Matrix (PDF)