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Forecasting in a DSGE Model with Banking Intermediation: Evidence from the US

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  • Roberta Cardani
  • Alessia Paccagnini
  • Stefania Villa

Abstract

This paper examines the forecasting performance of DSGE models with and without banking intermediation for the US economy. Over the forecast period 2001-2013, the model augmented with a banking sector leads to an improvement of point and density forecasts for inflation and the short term interest rate, while the better forecast for output depends on the forecasting horizon/period. To interpret this finding it is crucial to take into account parameters instabilities showed by a recursive-window estimation. Moreover, rolling estimates of point forecasts show that a banking sector helps improving the forecasting performance of output and inflation in the recent period.

Suggested Citation

  • 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.
  • Handle: RePEc:mib:wpaper:292
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    References listed on IDEAS

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    1. Villa, Stefania, 2016. "Financial Frictions In The Euro Area And The United States: A Bayesian Assessment," Macroeconomic Dynamics, Cambridge University Press, vol. 20(05), pages 1313-1340, July.
    2. Malin Adolfson & Jesper Lindé & Mattias Villani, 2007. "Forecasting Performance of an Open Economy DSGE Model," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 289-328.
    3. Bommier, Antoine & Chassagnon, Arnold & Le Grand, François, 2012. "Comparative risk aversion: A formal approach with applications to saving behavior," Journal of Economic Theory, Elsevier, vol. 147(4), pages 1614-1641.
    4. Malte Knüppel, 2015. "Evaluating the Calibration of Multi-Step-Ahead Density Forecasts Using Raw Moments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(2), pages 270-281, April.
    5. Matteo Iacoviello, 2005. "House Prices, Borrowing Constraints, and Monetary Policy in the Business Cycle," American Economic Review, American Economic Association, vol. 95(3), pages 739-764, June.
    6. De Graeve, Ferre, 2008. "The external finance premium and the macroeconomy: US post-WWII evidence," Journal of Economic Dynamics and Control, Elsevier, vol. 32(11), pages 3415-3440, November.
    7. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-474, October.
    8. Hurtado, Samuel, 2014. "DSGE models and the Lucas critique," Economic Modelling, Elsevier, vol. 44(S1), pages 12-19.
    9. Andrew Foerster & Juan F. Rubio‐Ramírez & Daniel F. Waggoner & Tao Zha, 2016. "Perturbation methods for Markov‐switching dynamic stochastic general equilibrium models," Quantitative Economics, Econometric Society, vol. 7(2), pages 637-669, July.
    10. Markus K. Brunnermeier & Thomas M. Eisenbach & Yuliy Sannikov, 2012. "Macroeconomics with Financial Frictions: A Survey," Levine's Working Paper Archive 786969000000000384, David K. Levine.
    11. Maik H. Wolters, 2015. "Evaluating Point and Density Forecasts of DSGE Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 74-96, January.
    12. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    13. Marco Del Negro & Frank Schorfheide, 2009. "Monetary Policy Analysis with Potentially Misspecified Models," American Economic Review, American Economic Association, vol. 99(4), pages 1415-1450, September.
    14. Wolters, Maik H., 2011. "Forecasting under Model Uncertainty," Annual Conference 2011 (Frankfurt, Main): The Order of the World Economy - Lessons from the Crisis 48723, Verein für Socialpolitik / German Economic Association.
    15. Bernanke, Ben S. & Gertler, Mark & Gilchrist, Simon, 1999. "The financial accelerator in a quantitative business cycle framework," Handbook of Macroeconomics,in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 21, pages 1341-1393 Elsevier.
    16. Marcin Kolasa & Michał Rubaszek & Paweł Skrzypczyński, 2012. "Putting the New Keynesian DSGE Model to the Real‐Time Forecasting Test," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(7), pages 1301-1324, October.
    17. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    18. Dave Reifschneider & William Wascher & David Wilcox, 2015. "Aggregate Supply in the United States: Recent Developments and Implications for the Conduct of Monetary Policy," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 63(1), pages 71-109, May.
    19. Hugo Gerard & Kristoffer Nimark, 2008. "Combining Multivariate Density Forecasts Using Predictive Criteria," RBA Research Discussion Papers rdp2008-02, Reserve Bank of Australia.
    20. Eo, Yunjong, 2008. "Bayesian Analysis of DSGE Models with Regime Switching," MPRA Paper 13910, University Library of Munich, Germany, revised 11 Feb 2009.
    21. Herbst, Edward & Schorfheide, Frank, 2012. "Evaluating DSGE model forecasts of comovements," Journal of Econometrics, Elsevier, vol. 171(2), pages 152-166.
    22. Stelios Bekiros & Alessia Paccagnini, 2013. "On the predictability of time-varying VAR and DSGE models," Empirical Economics, Springer, vol. 45(1), pages 635-664, August.
    23. Matteo Ciccarelli & Angela Maddaloni & Jose Luis Peydro, 2015. "Trusting the Bankers: A New Look at the Credit Channel of Monetary Policy," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 18(4), pages 979-1002, October.
    24. Francesco Bianchi, 2013. "Regime Switches, Agents' Beliefs, and Post-World War II U.S. Macroeconomic Dynamics," Review of Economic Studies, Oxford University Press, vol. 80(2), pages 463-490.
    25. Yasuo Hirose & Atsushi Inoue, 2016. "The Zero Lower Bound and Parameter Bias in an Estimated DSGE Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(4), pages 630-651, June.
    26. Gürkaynak, Refet S. & Kisacikoglu, Burçin & Rossi, Barbara, 2013. "Do DSGE Models Forecast More Accurately Out-of-Sample than VAR Models?," CEPR Discussion Papers 9576, C.E.P.R. Discussion Papers.
    27. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    28. Negro, Marco Del & Schorfheide, Frank, 2013. "DSGE Model-Based Forecasting," Handbook of Economic Forecasting, Elsevier.
    29. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
    30. Yasuo Hirose & Atsushi Inoue, 2013. "Zero Lower Bound and Parameter Bias in an Estimated DSGE Model," UTokyo Price Project Working Paper Series 012, University of Tokyo, Graduate School of Economics.
    31. Dario Caldara & Jesus Fernandez-Villaverde & Juan Rubio-Ramirez & Wen Yao, 2012. "Computing DSGE Models with Recursive Preferences and Stochastic Volatility," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(2), pages 188-206, April.
    32. Schorfheide, Frank & Sill, Keith & Kryshko, Maxym, 2010. "DSGE model-based forecasting of non-modelled variables," International Journal of Forecasting, Elsevier, vol. 26(2), pages 348-373, April.
    33. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    34. Kling, John L & Bessler, David A, 1989. "Calibration-Based Predictive Distributions: An Application of Prequential Analysis to Interest Rates, Money, Prices, and Output," The Journal of Business, University of Chicago Press, vol. 62(4), pages 477-499, October.
    35. Christoffel, Kai & Warne, Anders & Coenen, Günter, 2010. "Forecasting with DSGE models," Working Paper Series 1185, European Central Bank.
    36. Giraitis, Liudas & Kapetanios, George & Theodoridis, Konstantinos & Yates, Tony, 2014. "Estimating time-varying DSGE models using minimum distance methods," Bank of England working papers 507, Bank of England.
    37. Kolasa, Marcin & Rubaszek, Michał, 2015. "Forecasting using DSGE models with financial frictions," International Journal of Forecasting, Elsevier, vol. 31(1), pages 1-19.
    38. Fabio Fornari & Livio Stracca, 2012. "What does a financial shock do? First international evidence," Economic Policy, CEPR;CES;MSH, vol. 27(71), pages 407-445, July.
    39. repec:eee:dyncon:v:78:y:2017:i:c:p:164-189 is not listed on IDEAS
    40. Cole, Harold, 2011. "Discussion of Gertler and Karadi: A model of unconventional monetary policy," Journal of Monetary Economics, Elsevier, vol. 58(1), pages 35-38, January.
    41. Massimiliano Marcellino & Yuliya Rychalovska, 2014. "Forecasting with a DSGE Model of a Small Open Economy within the Monetary Union," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(5), pages 315-338, August.
    42. Gertler, Mark & Karadi, Peter, 2011. "A model of unconventional monetary policy," Journal of Monetary Economics, Elsevier, vol. 58(1), pages 17-34, January.
    43. Sims, Christopher A, 2002. "Solving Linear Rational Expectations Models," Computational Economics, Springer;Society for Computational Economics, vol. 20(1-2), pages 1-20, October.
    44. Francis X. Diebold & Jinyong Hahn & Anthony S. Tay, 1999. "Multivariate Density Forecast Evaluation And Calibration In Financial Risk Management: High-Frequency Returns On Foreign Exchange," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 661-673, November.
    45. repec:dau:papers:123456789/4434 is not listed on IDEAS
    46. Gelain, Paolo & Ilbas, Pelin, 2017. "Monetary and macroprudential policies in an estimated model with financial intermediation," Journal of Economic Dynamics and Control, Elsevier, vol. 78(C), pages 164-189.
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    Citations

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    Cited by:

    1. Bekiros, Stelios & Cardani, Roberta & Paccagnini, Alessia & Villa, Stefania, 2016. "Dealing with financial instability under a DSGE modeling approach with banking intermediation: A predictability analysis versus TVP-VARs," Journal of Financial Stability, Elsevier, vol. 26(C), pages 216-227.
    2. 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.
    3. Bekiros, Stelios D.; Cardani, Roberta; Paccagnini, Alessia; Villa, Stefania, 2015. "Dealing with Financial Instability under a DSGE modeling approach with Banking Intermediation: a forecastability analysis versus TVP-VARs," Economics Working Papers ECO2015/04, European University Institute.
    4. Stefan Gebauer, 2017. "The Use of Financial Market Variables in Forecasting," DIW Roundup: Politik im Fokus 115, DIW Berlin, German Institute for Economic Research.
    5. Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2015. "Forecasting with Instabilities: an Application to DSGE Models with Financial Frictions," Working Papers 201523, School of Economics, University College Dublin.

    More about this item

    Keywords

    Bayesian estimation; Forecasting; Banking sector;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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