Home Merging Structural and Reduced-Form Models for Forecasting
Article
Licensed
Unlicensed Requires Authentication

Merging Structural and Reduced-Form Models for Forecasting

  • Jaime Martinez-Martin EMAIL logo , Richard Morris , Luca Onorante and Fabio Massimo Piersanti
Published/Copyright: February 15, 2024

Abstract

Recent economic crises have posed important challenges for forecasting. Models estimated pre-crisis may perform badly when normal economic relationships have been disrupted. Meanwhile, forecasting, especially in central banks, is increasingly based on a suite of models, following two main approaches: structural (DSGE) and reduced form. The challenge remains to identify which model – or combination of models – is likely to make better forecasts in a changing environment. We explore this issue by assessing the forecasting performance of combinations of a medium-scale DSGE model with standard reduced-form methods applied to the Spanish economy and a reference period that includes both the great recession and the sovereign debt crisis. Our findings suggest that: (i) the mean reverting properties of the DSGE model cause it to underestimate the growth of real variables following the inclusion of crisis episodes in the estimation period; (ii) despite this, reduced-form VARs benefit from the imposition of an economic prior from the structural model; but (iii) pooling information in the form of variables extracted from the structural model with (B)VAR methods does not improve forecast accuracy. By analysing the quantiles of the predictive distributions, we also provide evidence that merging models can help improve the forecast in a context including crisis episodes.

JEL Classification: C54; E37; F3; F41

Corresponding author: Jaime Martinez-Martin, Banco de España, Madrid, Spain, E-mail:

We are extremely grateful to Marco Del Negro, Marco Ratto, Jesús Fernández-Villaverde, Philip Lane, Diego Rodríguez-Palenzuela, Barbara Rossi, Juan F. Rubio-Ramírez and seminar participants at the European Central Bank, at the 2019 International Association for Applied Econometrics Annual Conference (Nicosia), and at the 3rd Forecasting at Central Banks Conference (Ottawa) for their stimulating and helpful comments. Also, we are thankful to the Editor and two anonymous referees for their comments and suggestions. The views expressed in the paper are those of the authors and do not necessarily reflect those of the ECB, the Banco de España, the Banca d’Italia, the European Commission or the Eurosystem. No part of our compensation was, is or will be directly or indirectly related to the specific views expressed in this paper. All remaining errors are our own responsibility.


References

Adolfson, M., S. Laséen, J. Linďé, and M. Villani. 2007. “Bayesian Estimation of an Open Economy DSGE Model with Incomplete Pass-Through.” Journal of International Economics 72 (2): 481–511. https://doi.org/10.1016/j.jinteco.2007.01.003.Search in Google Scholar

Amisano, G., and R. Giacomini. 2007. “Comparing Density Forecasts via Weighted Likelihood Ratio Tests.” Journal of Business & Economic Statistics 25 (2): 177–90. https://doi.org/10.1198/073500106000000332.Search in Google Scholar

Andrés, J., P. Burriel, and A. Estrada. 2006. “BEMOD: A DSGE Model for the Spanish Economy and the Rest of the Euro Area.” Banco de España Working Paper No. 0631.10.2139/ssrn.949147Search in Google Scholar

Andrés, J., S. Hurtado, E. Ortega, and C. Thomas. 2010. “Spain in the Euro: A General Equilibrium Analysis.” SERIEs 1 (1–2): 67–95. https://doi.org/10.1007/s13209-009-0015-6.Search in Google Scholar

Andrés, J., O. Arce, and C. Thomas. 2017. “Structural Reforms in a Debt Overhang.” Journal of Monetary Economics 88: 15–34. https://doi.org/10.1016/j.jmoneco.2017.05.004.Search in Google Scholar

Blanchard, O. J., and C. M. Kahn. 1980. “The Solution of Linear Difference Models under Rational Expectations.” Econometrica: Journal of the Econometric Society 48 (5): 1305–11, https://doi.org/10.2307/1912186.Search in Google Scholar

Boscà, J. E., A. Díaz, R. Doménech, E. Pérez, and L. Puch. 2007. “The REMSDB Macroeconomic Database of the Spanish Economy.” Ministerio de Economia y Hacienda Working Paper No. 2007-04.Search in Google Scholar

Boscà, J. E., A. Díaz, R. Doménech, J. Ferri, E. Pérez, and L. Puch. 2010. “A Rational Expectations Model for Simulation and Policy Evaluation of the Spanish Economy.” SERIEs 1 (1–2): 135–69. https://doi.org/10.1007/s13209-009-0013-8.Search in Google Scholar

Boscà, J. E., A. Díaz, R. Doménech, J. Feri, and J. F. Rubio-Ramírez. 2018. “Financial and Fiscal Shocks in the Great Recession and Recovery of the Spanish Economy.” FEDEA Working Paper No. 2018-05.Search in Google Scholar

Burriel, P., J. Fernández-Villaverde, and J. F. Rubio-Ramírez. 2010. “MEDEA: A DSGE Model for the Spanish Economy.” SERIEs 1 (1–2): 175–243.10.1007/s13209-009-0011-xSearch in Google Scholar

Cai, M., M. Del Negro, M. P. Giannoni, A. Gupta, P. Li, and E. Moszkowski. 2018. “DSGE Forecasts of the Lost Recovery.” Federal Reserve Bank of New York Staff Reports No. 844.10.2139/ssrn.3138589Search in Google Scholar

Christiano, L. J., M. Eichenbaum, and C. L. Evans. 2005. “Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy.” Journal of Political Economy 113 (1): 1–45. https://doi.org/10.1086/426038.Search in Google Scholar

Del Negro, M., and F. Schorfheide. 2004. “Priors from General Equilibrium Models for VARs.” International Economic Review 45 (2): 643–73. https://doi.org/10.1111/j.1468-2354.2004.00139.x.Search in Google Scholar

Del Negro, M., and F. Schorfheide. 2006. “How Good Is what You’ve Got? DGSE-VAR as a Toolkit for Evaluating DSGE Models.” Federal Reserve Bank of Atlanta Economic Review 91 (2): 21–37.Search in Google Scholar

Del Negro, M., and F. Schorfheide. 2008. “Forming Priors for DSGE Models (And How it Affects the Assessment of Nominal Rigidities).” Journal of Monetary Economics 55 (7): 1191–208. https://doi.org/10.1016/j.jmoneco.2008.09.006.Search in Google Scholar

Del Negro, M., F. Schorfheide, F. Smets, and R. Wouters. 2007. “On the Fit of New Keynesian Models.” Journal of Business & Economic Statistics 25 (2): 123–43. https://doi.org/10.1198/073500107000000016.Search in Google Scholar

Del Negro, M., Raiden B. Hasegawa, and F. Schorfheide. 2016. “Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance.” Journal of Econometrics 192 (2): 391–405. https://doi.org/10.1016/j.jeconom.2016.02.006.Search in Google Scholar

Diebold, F. X. 1998. “The Past, Present, and Future of Macroeconomic Forecasting.” The Journal of Economic Perspectives 12 (2): 175–92. https://doi.org/10.1257/jep.12.2.175.Search in Google Scholar

Diebold, F. X., and R. S. Mariano. 1995. “Comparing Predictive Accuracy.” Journal of Business & Economic Statistics 13 (3): 253–63. https://doi.org/10.2307/1392185.Search in Google Scholar

Diebold, F. X., A. Gunther, and A. S. Tay. 1998. “Evaluating Density Forecasts with Applications to Financial Risk Management.” International Economic Review 39 (4): 863–83. https://doi.org/10.2307/2527342.Search in Google Scholar

Edge, R. M., and R. S. Gürkaynak. 2010. “How Useful Are Estimated DSGE Model Forecasts for Central Bankers?” CEPR Disussion Paper 8158.10.2139/ssrn.1810075Search in Google Scholar

Edge, R. M., M. T. Kiley, and J. P. Laforte. 2010. “A Comparison of Forecast Performance between Federal Reserve Staff Forecasts, Simple Reduced-form Models, and a DSGE Model.” Journal of Applied Econometrics 25 (4): 720–54. https://doi.org/10.1002/jae.1175.Search in Google Scholar

Elliott, G., and A. Timmermann. 2005. “Optimal Forecast Combination Using under Regime Switching.” International Economic Review 46: 1081–102. https://doi.org/10.1111/j.1468-2354.2005.00361.x.Search in Google Scholar

Erceg, C. J., L. Guerrieri, and C. J. Gust. 2006. “SIGMA: A New Open Economy Model for Policy Analysis.” International Journal of Central Banking 2 (1): 1–50. https://doi.org/10.17016/ifdp.2005.835r.Search in Google Scholar

Fernández-de-Córdoba, G., and J. L. Torres. 2011. “Forecasting the Spanish Economy with an Augmented VAR–DSGE Model.” SERIEs 2 (3): 379–99. https://doi.org/10.1007/s13209-010-0036-1.Search in Google Scholar

Giacomini, R. 2013. “The Relationship between DSGE and VAR Models.” In Advances in Econometrics in VAR Models in Macroeconomics – New Developments and Applications: Essays in Honor of Christopher A. Sims, 32, 1–25. Bingley: Emerald Group Publishing Limited.10.1108/S0731-9053(2013)0000031001Search in Google Scholar

Giacomini, R., and B. Rossi. 2010. “Forecast Comparisons in Unstable Environments.” Journal of Applied Econometrics 25 (4): 595–620. https://doi.org/10.1002/jae.1177.Search in Google Scholar

Giacomini, R., and H. White. 2006. “Tests of Conditional Predictive Ability.” Econometrica 74 (6): 1545–78. https://doi.org/10.1111/j.1468-0262.2006.00718.x.Search in Google Scholar

Giannone, D., M. Lenza, and G. Primiceri. 2015. “Prior Selection for Vector Autoregressions.” The Review of Economics and Statistics 97 (2): 436–51. https://doi.org/10.1162/rest_a_00483.Search in Google Scholar

Gómez-González, P., and D. M Rees. 2018. “Same Spain, Less Pain?” European Economic Review 110 (C): 78–2107. https://doi.org/10.1016/j.euroecorev.2018.08.006.Search in Google Scholar

Goodwin, P. 2000. “Correct or Combine? Mechanically Integrating Judgmental Forecasts with Statistical Methods.” International Journal of Forecasting 16: 261–75. https://doi.org/10.1016/s0169-2070(00)00038-8.Search in Google Scholar

Gürkaynak, R. S., B. Kısacıkoglu, and B. Rossi. 2013. “Do DSGE Models Forecast More Accurately Out-Of-Sample Than VAR Models?” CEPR Discussion Paper No. 9576.10.1108/S0731-9053(2013)0000031002Search in Google Scholar

Hall, S. G., and J. Mitchell. 2007. “Combining Density Forecasts.” International Journal of Forecasting 23 (1): 1–13. https://doi.org/10.1016/j.ijforecast.2006.08.001.Search in Google Scholar

Harrison, R., K. Nikolov, M. Quinn, G. Ramsay, A. Scott, and R. Thomas. 2005. The Bank of England Quarterly Model. London: Publications Group, Bank of England.Search in Google Scholar

Kilponen, J., and A. Ripatti. 2006. Introduction to AINO. Mimeo: Bank of Finland.Search in Google Scholar

King, R. G., and S. T. Rebelo. 1999. “Resuscitating Real Business Cycles.” In Handbook of Macroeconomics, 1, 927–1007. Amsterdam: Elsevier.10.1016/S1574-0048(99)10022-3Search in Google Scholar

Kortelainen, M. 2002. “EDGE: A Model of the Euro Area with Applications to Monetary Policy.” Bank of Finland Studies E23.Search in Google Scholar

Lees, K., T. Matheson, and C. Smith. 2011. “Open Economy Forecastsing with a DSGE–VAR: Head to Head with the RBNZ Published Forecasts.” International Journal of Forecasting 27 (1): 512–28. https://doi.org/10.1016/j.ijforecast.2010.01.008.Search in Google Scholar

Litterman, R. B. 1986a. “A Statistical Approach to Economic Forecasting.” Journal of Business & Economic Statistics 4 (1): 1–4. https://doi.org/10.2307/1391378.Search in Google Scholar

Litterman, R. B. 1986b. “Forecasting with Bayesian Vector Autoregressions—Five Years of Experience.” Journal of Business & Economic Statistics 4 (1): 25–38. https://doi.org/10.2307/1391384.Search in Google Scholar

Manzan, S., and D. Zerom. 2013. “Are Macroeconomic Variables Useful for Forecasting the Distribution of U.S. Inflation?” International Journal of Forecasting 29 (3): 469–78. https://doi.org/10.1016/j.ijforecast.2013.01.005.Search in Google Scholar

Mincer, J. A., and V. Zarnowitz. 1969. “The Evaluation of Economic Forecasts.” In Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, 3–46. New York: NBER.Search in Google Scholar

Murchison, S., and A. Rennison. 2006. “ToTEM: The Bank of Canada’s New Quarterly Projection Model.” Bank of Canada Technical Report No. 97.Search in Google Scholar

Rossi, B., and T. Sekhposyan. 2010. “‘Have Economic Models’ Forecasting Performance for US Output Growth and Inflation Changed over Time, and when?” International Journal of Forecasting 26 (4): 808–35. https://doi.org/10.1016/j.ijforecast.2009.08.004.Search in Google Scholar

Rossi, B., and T. Sekhposyan. 2016. “Forecast Rationality Tests in the Presence of Instabilities, with Applications to Federal Reserve and Survey Forecasts.” Journal of Applied Econometrics 31 (3): 507–32. https://doi.org/10.1002/jae.2440.Search in Google Scholar

Sims, C. A. 1980. “Macroeconomics and Reality.” Econometrica 48 (1): 1–48. https://doi.org/10.2307/1912017.Search in Google Scholar

Smets, F., and R. Wouters. 2003. “An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area.” Journal of the European Economic Association 1 (5): 1123–75. https://doi.org/10.1162/154247603770383415.Search in Google Scholar

Stock, J. H., and M. W. Watson. 2003. “Forecasting Output and Inflation: The Role of Asset Prices.” Journal of Economic Literature 41 (3): 788–829. https://doi.org/10.1257/jel.41.3.788.Search in Google Scholar

Warne, A., G. Coenen, and K. Christoffel. 2008. “The New Area-wide Model of the Euro Area: A Micro-founded Open-Economy Model for Forecasting and Policy Analysis.” ECB Working Paper No. 944.Search in Google Scholar

West, K. D., and M. W. McCracken (1998), “Regression-based Tests of Predictive Ability.” NBER Working Paper t0226.10.3386/t0226Search in Google Scholar


Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/bejm-2022-0170).


Received: 2022-10-27
Accepted: 2023-12-30
Published Online: 2024-02-15

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 7.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/bejm-2022-0170/html
Scroll to top button