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Marginalized predictive likelihood comparisons of linear Gaussian state-space models with applications to DSGE, DSGEVAR, and VAR models

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

Abstract

The predictive likelihood is of particular relevance in a Bayesian setting when the purpose is to rank models in a forecast comparison exercise. This paper discusses how the predictive likelihood can be estimated for any subset of the observable variables in linear Gaussian state-space models with Bayesian methods, and proposes to utilize a missing observations consistent Kalman filter in the process of achieving this objective. As an empirical application, we analyze euro area data and compare the density forecast performance of a DSGE model to DSGE-VARs and reduced-form linear Gaussian models.

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  • Warne, Anders & Coenen, Günter & Christoffel, Kai, 2014. "Marginalized predictive likelihood comparisons of linear Gaussian state-space models with applications to DSGE, DSGEVAR, and VAR models," CFS Working Paper Series 478, Center for Financial Studies (CFS).
  • Handle: RePEc:zbw:cfswop:478
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    Cited by:

    1. Tim Oliver Berg, 2016. "Multivariate Forecasting with BVARs and DSGE Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(8), pages 718-740, December.
    2. Michael Cai & Marco Del Negro & Edward Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2019. "Online Estimation of DSGE Models," PIER Working Paper Archive 19-014, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    3. Lindé, Jesper & Smets, Frank & Wouters, Rafael, 2016. "Challenges for Central Banks' Macro Models," CEPR Discussion Papers 11405, C.E.P.R. Discussion Papers.
    4. Francesco Simone Lucidi, 2021. "The Misalignment of Fiscal Multipliers in Italian Regions," Working Papers in Public Economics 204, University of Rome La Sapienza, Department of Economics and Law.
    5. Lindé, J. & Smets, F. & Wouters, R., 2016. "Challenges for Central Banks’ Macro Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 2185-2262, Elsevier.
    6. Markku Lanne & Jani Luoto, 2018. "Data†Driven Identification Constraints for DSGE Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(2), pages 236-258, April.
    7. Benjamin K. Johannsen & Elmar Mertens, 2021. "A Time‐Series Model of Interest Rates with the Effective Lower Bound," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(5), pages 1005-1046, August.
    8. Diebold, Francis X. & Schorfheide, Frank & Shin, Minchul, 2017. "Real-time forecast evaluation of DSGE models with stochastic volatility," Journal of Econometrics, Elsevier, vol. 201(2), pages 322-332.
    9. McAdam, Peter & Warne, Anders, 2019. "Euro area real-time density forecasting with financial or labor market frictions," International Journal of Forecasting, Elsevier, vol. 35(2), pages 580-600.
    10. Mokinski, Frieder, 2017. "A severity function approach to scenario selection," Discussion Papers 34/2017, Deutsche Bundesbank.
    11. Fabian Krüger & Sebastian Lerch & Thordis Thorarinsdottir & Tilmann Gneiting, 2021. "Predictive Inference Based on Markov Chain Monte Carlo Output," International Statistical Review, International Statistical Institute, vol. 89(2), pages 274-301, August.
    12. Michael Cai & Marco Del & Edward Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2021. "Online estimation of DSGE models," Econometrics Journal, Royal Economic Society, vol. 24(1), pages 33-58.
    13. McAdam, Peter & Warne, Anders, 2020. "Density forecast combinations: the real-time dimension," Working Paper Series 2378, European Central Bank.

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    More about this item

    Keywords

    Bayesian inference; density forecasting; Kalman filter; missing data; Monte Carlo integration; predictive likelihood;
    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
    • 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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