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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:

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    2. Linde, Jesper & Smets, Frank & Wouters, Rafael, 2016. "Challenges for Central Banks' Macro Models," CEPR Discussion Papers 11405, C.E.P.R. Discussion Papers.
    3. Warne, Anders, 2023. "DSGE model forecasting: rational expectations vs. adaptive learning," Working Paper Series 2768, European Central Bank.
    4. Francesco Simone Lucidi, 2023. "The misalignment of fiscal multipliers in Italian regions," Regional Studies, Taylor & Francis Journals, vol. 57(10), pages 2073-2086, October.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Yuliya Rychalovska & Sergey Slobodyan & Raf Wouters, 2024. "Survey Expectations, Adaptive Learning and Inflation Dynamics," CERGE-EI Working Papers wp781, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    11. Peter McAdam & Anders Warne, 2024. "Density forecast combinations: The real‐time dimension," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1153-1172, August.
    12. 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.
    13. Mokinski, Frieder, 2017. "A severity function approach to scenario selection," Discussion Papers 34/2017, Deutsche Bundesbank.
    14. 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.
    15. Ciccarelli, Matteo & Darracq Pariès, Matthieu & Priftis, Romanos & Angelini, Elena & Bańbura, Marta & Bokan, Nikola & Fagan, Gabriel & Gumiel, José Emilio & Kornprobst, Antoine & Lalik, Magdalena & Mo, 2024. "ECB macroeconometric models for forecasting and policy analysis," Occasional Paper Series 344, 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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