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Forecasting using DSGE models with financial frictions

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  • Kolasa, Marcin
  • Rubaszek, Michał

Abstract

This paper compares the quality of forecasts from DSGE models with and without financial frictions. We find that accounting for financial market imperfections does not result in a uniform improvement in the accuracy of point forecasts during non-crisis times, while the average quality of density forecast actually deteriorates. In contrast, adding frictions in the housing market proves very helpful during times of financial turmoil, outperforming both the frictionless benchmark and the alternative that incorporates financial frictions in the corporate sector. Moreover, we detect complementarities among the analyzed setups that can be exploited in the forecasting process.

Suggested Citation

  • Kolasa, Marcin & Rubaszek, Michał, 2015. "Forecasting using DSGE models with financial frictions," International Journal of Forecasting, Elsevier, vol. 31(1), pages 1-19.
  • Handle: RePEc:eee:intfor:v:31:y:2015:i:1:p:1-19
    DOI: 10.1016/j.ijforecast.2014.05.001
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    Cited by:

    1. Galvão, Ana Beatriz & Giraitis, Liudas & Kapetanios, George & Petrova, Katerina, 2016. "A time varying DSGE model with financial frictions," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 690-716.
    2. Chin, Kuo-Hsuan & Li, Xue, 2019. "Bayesian forecast combination in VAR-DSGE models," Journal of Macroeconomics, Elsevier, vol. 59(C), pages 278-298.
    3. Alice Albonico & Alessia Paccagnini & Patrizio Tirelli, 2019. "Limited Asset Market Participation And The Euro Area Crisis: An Empirical Dsge Model," Economic Inquiry, Western Economic Association International, vol. 57(3), pages 1302-1323, July.
    4. Kolasa, Marcin & Rubaszek, Michał, 2018. "Does the foreign sector help forecast domestic variables in DSGE models?," International Journal of Forecasting, Elsevier, vol. 34(4), pages 809-821.
    5. Ana Beatriz Galvão & Liudas Giraitis & George Kapetanios & Katerina Petrova, 2015. "A Bayesian Local Likelihood Method for Modelling Parameter Time Variation in DSGE Models," Working Papers 770, Queen Mary University of London, School of Economics and Finance.
    6. Marcin Kolasa & Michal Rubaszek, 2015. "How Frequently Should We Reestimate DSGE Models?," International Journal of Central Banking, International Journal of Central Banking, vol. 11(4), pages 279-305, December.
    7. Cardani, Roberta & Paccagnini, Alessia & Villa, Stefania, 2019. "Forecasting with instabilities: An application to DSGE models with financial frictions," Journal of Macroeconomics, Elsevier, vol. 61(C), pages 1-1.
    8. Hasumi, Ryo & Iiboshi, Hirokuni & Matsumae, Tatsuyoshi & Nakamura, Daisuke, 2019. "Does a financial accelerator improve forecasts during financial crises? Evidence from Japan with prediction-pooling methods," Journal of Asian Economics, Elsevier, vol. 60(C), pages 45-68.
    9. Aliaga Miranda, Augusto, 2020. "Monetary policy rules for an open economy with financial frictions: A Bayesian approach," Dynare Working Papers 62, CEPREMAP.
    10. Fritz Breuss, 2016. "Would DSGE Models have Predicted the Great Recession in Austria?," WIFO Working Papers 530, WIFO.
    11. Hasumi, Ryo & Iiboshi, Hirokuni & Matsumae, Tatsuyoshi & Nakamura, Daisuke, 2018. "Does a financial accelerator improve forecasts during financial crises?: Evidence from Japan with Prediction Pool Methods," MPRA Paper 85523, University Library of Munich, Germany.
    12. Alice Albonico & Alessia Paccagnini & Patrizio Tirelli, 2014. "Estimating a DSGE model with Limited Asset Market Participation for the Euro Area," Working Papers 286, University of Milano-Bicocca, Department of Economics, revised Nov 2014.
    13. Roberta Cardani & Alessia Paccagnini & Stelios D. Bekiros, 2017. "The Effectiveness of Forward Guidance in an Estimated DSGE Model for the Euro Area: the Role of Expectations," Working Papers 201701, School of Economics, University College Dublin.
    14. Del Negro, Marco & Hasegawa, Raiden B. & Schorfheide, Frank, 2016. "Dynamic prediction pools: An investigation of financial frictions and forecasting performance," Journal of Econometrics, Elsevier, vol. 192(2), pages 391-405.
    15. Fritz Breuss, 2018. "Would DSGE Models Have Predicted the Great Recession in Austria?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 105-126, April.
    16. Aliaga, Augusto, 2020. "Reglas de política monetaria para una economía abierta con fricciones financieras: Un enfoque Bayesiano [Monetary policy rules for an open economy with financial frictions: A Bayesian approach]," MPRA Paper 100604, University Library of Munich, Germany.
    17. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    18. Josef Hollmayr & Michael Kuehl, 2016. "Imperfect Information about Financial Frictions and Consequences for the Business Cycle," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 22, pages 179-207, October.
    19. 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.
    20. Sean Langcake & Tim Robinson, 2018. "Forecasting the Australian economy with DSGE and BVAR models," Applied Economics, Taylor & Francis Journals, vol. 50(3), pages 251-267, January.
    21. Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2015. "Forecasting in a DSGE Model with Banking Intermediation: Evidence from the US," Working Papers 292, University of Milano-Bicocca, Department of Economics, revised Feb 2015.
    22. Caraiani, Petre, 2016. "The role of money in DSGE models: a forecasting perspective," Journal of Macroeconomics, Elsevier, vol. 47(PB), pages 315-330.
    23. Alberto Bucci & Simone Marsiglio, 2019. "Financial development and economic growth: long‐run equilibrium and transitional dynamics," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(3), pages 331-359, July.

    More about this item

    Keywords

    Forecasting; DSGE models; Financial frictions; Housing market;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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