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Predictive likelihood comparisons with DSGE and DSGE-VAR models

Author

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  • Warne, Anders
  • Coenen, Günter
  • Christoffel, Kai

Abstract

This paper shows how to compute the h-step-ahead predictive likelihood for any subset of the observed variables in parametric discrete time series models estimated with Bayesian methods. The subset of variables may vary across forecast horizons and the problem thereby covers marginal and joint predictive likelihoods for a fixed subset as special cases. The basic idea is to utilize well-known techniques for handling missing data when computing the likelihood function, such as a missing observations consistent Kalman filter for linear Gaussian models, but it also extends to nonlinear, nonnormal state-space models. The predictive likelihood can thereafter be calculated via Monte Carlo integration using draws from the posterior distribution. As an empirical illustration, we use euro area data and compare the forecasting performance of the New Area-Wide Model, a small-open-economy DSGE model, to DSGEVARs, and to reduced-form linear Gaussian models. JEL Classification: C11, C32, C52, C53, E37

Suggested Citation

  • Warne, Anders & Coenen, Günter & Christoffel, Kai, 2013. "Predictive likelihood comparisons with DSGE and DSGE-VAR models," Working Paper Series 1536, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20131536
    Note: 563011
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    File URL: https://www.ecb.europa.eu//pub/pdf/scpwps/ecbwp1536.pdf
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    References listed on IDEAS

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    1. Malin Adolfson & Jesper Linde & Mattias Villani, 2007. "Forecasting Performance of an Open Economy DSGE Model," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 289-328.
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    Citations

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    Cited by:

    1. Pelin Ilbas & Øistein Røisland & Tommy Sveen, 2013. "The influence of the Taylor rule on US monetary policy," Working Paper 2013/04, Norges Bank.
    2. Patrick Fève & Jean‐Guillaume Sahuc, 2017. "In Search of the Transmission Mechanism of Fiscal Policy in the Euro Area," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 704-718, April.
    3. repec:wly:japmet:v:32:y:2017:i:1:p:103-119 is not listed on IDEAS
    4. Boris Blagov, 2018. "Financial crises and time-varying risk premia in a small open economy: a Markov-switching DSGE model for Estonia," Empirical Economics, Springer, vol. 54(3), pages 1017-1060, May.
    5. Negro, Marco Del & Schorfheide, Frank, 2013. "DSGE Model-Based Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 57-140, Elsevier.
    6. Anders Warne & Günter Coenen & Kai Christoffel, 2017. "Marginalized Predictive Likelihood Comparisons of Linear Gaussian State‐Space Models with Applications to DSGE, DSGE‐VAR, and VAR Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 103-119, January.
    7. Konstantinos Theodoridis & Francesco Zanetti, 2016. "News shocks and labour market dynamics in matching models," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 906-930, August.
    8. Smets, Frank & Warne, Anders & Wouters, Rafael, 2014. "Professional forecasters and real-time forecasting with a DSGE model," International Journal of Forecasting, Elsevier, vol. 30(4), pages 981-995.
    9. Markku Lanne & Jani Luoto, 2015. "Estimation of DSGE Models under Diffuse Priors and Data-Driven Identification Constraints," CREATES Research Papers 2015-37, Department of Economics and Business Economics, Aarhus University.
    10. Smets, Frank & Warne, Anders & Wouters, Raf, 2013. "Professional forecasters and the real-time forecasting performance of an estimated new keynesian model for the euro area," Working Paper Series 1571, European Central Bank.
    11. repec:eee:ecmode:v:67:y:2017:i:c:p:1-9 is not listed on IDEAS

    More about this item

    Keywords

    Bayesian inference; forecasting; Kalman filter; Missing data; Monte Carlo integration;

    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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