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Bayesian Forecasting with a Factor-Augmented Vector Autoregressive DSGE model

Author

Listed:
  • Stelios D. Bekiros

    () (Department of Economics, European University Institute (EUI) and Rimini Centre for Economic Analysis (RCEA), Italy)

  • Alessia Paccagnini

    () (Department of Economics, Università degli Studi di Milano-Bicocca, Italy)

Abstract

In this paper we employ advanced Bayesian methods in estimating dynamic stochastic general equilibrium (DSGE) models. Although policymakers and practitioners are particularly interested in DSGE models, these are typically too stylized to be taken directly to the data and often yield weak prediction results. Very recently, hybrid models have become popular for dealing with some of the DSGE model misspecifications. Major advances in Bayesian estimation methodology could allow these models to outperform well-known time series models and effectively deal with more complex real-world problems as richer sources of data become available. This study includes a comparative evaluation of the out-of-sample predictive performance of many different specifications of estimated DSGE models and various classes of VAR models, using datasets from the US economy. Simple and hybrid DSGE models are implemented, such as DSGE-VAR and tested against standard, Bayesian and Factor Augmented VARs. In this study we focus on a Factor Augmented DSGE model that is estimated using Bayesian approaches. The investigated period spans 1960:Q4 to 2010:Q4 for the real GDP, the harmonized CPI and the nominal short-term interest rate. We produce their forecasts for the out-of-sample testing period 1997:Q1-2010:Q4. This comparative validation can be useful to monetary policy analysis and macro-forecasting with the use of advanced Bayesian methods.

Suggested Citation

  • Stelios D. Bekiros & Alessia Paccagnini, 2013. "Bayesian Forecasting with a Factor-Augmented Vector Autoregressive DSGE model," Working Paper series 22_13, Rimini Centre for Economic Analysis.
  • Handle: RePEc:rim:rimwps:22_13
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    File URL: http://www.rcea.org/RePEc/pdf/wp22_13.pdf
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    References listed on IDEAS

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    6. Altug, Sumru, 1989. "Time-to-Build and Aggregate Fluctuations: Some New Evidence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 30(4), pages 889-920, November.
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    More about this item

    Keywords

    Bayesian estimation; Forecasting; Metropolis-Hastings; Markov chain monte carlo; Marginal data density; Factor Augmented DSGE;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: 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

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