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On the fit and forecasting performance of New-Keynesian models

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Listed:
  • Smets, Frank
  • Wouters, Raf
  • Del Negro, Marco
  • Schorfheide, Frank

Abstract

The paper provides new tools for the evaluation of DSGE models, and applies it to a large-scale New Keynesian dynamic stochastic general equilibrium (DSGE) model with price and wage stickiness and capital accumulation. Specifically, we approximate the DSGE model by a vector autoregression (VAR), and then systematically relax the implied cross-equation restrictions. Let λ denote the extent to which the restrictions are being relaxed. We document how the in- and out-of sample fit of the resulting specification (DSGE-VAR) changes as a function of λ. Furthermore, we learn about the precise nature of the misspecification by comparing the DSGE model's impulse responses to structural shocks with those of the best-fitting DSGE-VAR. We find that the degree of misspecification in large-scale DSGE models is no longer so large to prevent their use in day-to-day policy analysis, yet it is not small enough that it cannot be ignored. JEL Classification: C11, C32, C53

Suggested Citation

  • Smets, Frank & Wouters, Raf & Del Negro, Marco & Schorfheide, Frank, 2005. "On the fit and forecasting performance of New-Keynesian models," Working Paper Series 491, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:2005491
    Note: 58657
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    More about this item

    Keywords

    Bayesian analysis; DSGE models; model evaluation; vector autoregressions;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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