Estimating point and density forecasts for the US economy with a factor-augmented vector autoregressive DSGE model

Stelios Bekiros and Alessia Paccagnini 3
  • 1 Department of Economics, European University Institute (EUI), Via della Piazzuola, 43, I-50133, Florence
  • 2 Rimini Centre for Economic Analysis (RCEA), Via Patara, 3, 47900, Rimini, Italy
  • 3 Department of Economics, Università degli Studi di Milano-Bicocca, Piazza Ateneo Nuovo 1, 20126 Milano, Italy
Stelios Bekiros and Alessia Paccagnini

Abstract

Although policymakers and practitioners are particularly interested in dynamic stochastic general equilibrium (DSGE) models, these are typically too stylized to be applied directly to the data and often yield weak prediction results. Very recently, hybrid DSGE models have become popular for dealing with some of the model misspecifications. Major advances in estimation methodology could allow these models to outperform well-known time series models and effectively deal with more complex real-world problems as richer sources of data become available. In this study we introduce a Bayesian approach to estimate a novel factor augmented DSGE model that extends the model of Consolo et al. [Consolo, A., Favero, C.A., and Paccagnini, A., 2009. On the Statistical Identification of DSGE Models. Journal of Econometrics, 150, 99–115]. We perform a comparative predictive evaluation of point and density forecasts for many different specifications of estimated DSGE models and various classes of VAR models, using datasets from the US economy including real-time data. Simple and hybrid DSGE models are implemented, such as DSGE-VAR and tested against standard, Bayesian and factor augmented VARs. The results can be useful for macro-forecasting and monetary policy analysis.

    • Supplemental_Data_Code
  • Adolfson, M., M. Andersson, J. Linde, M. Villani, and A. Vredin. 2007. “Modern Forecasting Models in Action: Improving Macroeconomic Analyses at Central Banks.” International Journal of Central Banking 3 (4): 111–144.

    • Crossref
  • Adolfson, M., L. Stefan, L. Jesper, and V. Mattias. 2008. “Evaluating an Estimated New Keynesian Small Open Economy Model.” Journal of Economic Dynamics and Control 32(8): 2690–2721.

  • Altug, S. 1989. “Time-to-Build and Aggregate Fluctuations: Some New Evidence.” International Economic Review 30 (4): 889–920.

    • Crossref
  • An. S., and F. Schorfheide. 2007. “Bayesian Analysis of DSGE Models.” Econometric Reviews 26(2–4): 113–172.

    • Crossref
  • Bai, J., and S. Ng. 2000. “Determining the Number of Factors in Approximate Factor Models.” Econometrica 70.

  • Banbura, M., D. Giannone, and L. Reichlin. 2010. “Large Bayesian vector Autoregressions.” Journal of Applied Econometrics 25 (1): 71–92.

    • Crossref
  • Berkowitz, J. 2001. “Testing Density Forecasts, with Applications To Risk Management.” Journal of Business and Economic Statistics 19 (4): 465–474.

    • Crossref
  • Bernanke, B. S., and J. Boivin. 2003. “Monetary Policy in a Data-Rich Environment.” Journal of Monetary Economics 50 (3): 525–546.

    • Crossref
  • Bernanke, B. S., J. Boivin, and P. Eliasz. 2005. “Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach.” The Quarterly Journal of Economics, MIT Press, 120 (1): 387–422.

  • Boivin, J., and M. P. Giannoni. 2006. “DSGE Models in a Data-Rich Environment.” NBER Working Papers 12772.

    • Crossref
  • Boivin, J., M. P. Giannoni, and I. Mihov. 2009. “Sticky Prices and Monetary Policy: Evidence from Disaggregated US Data.” American Economic Review, American Economic Association 99 (1): 350–384.

    • Crossref
  • Brüggemann, R., H. Lütkepohl, and M. Marcellino. 2008. “Forecasting Euro Area Variables with German Pre-EMU Data.” Journal of Forecasting 27 (6): 465–481.

    • Crossref
  • Christiano, L. J., and M. Eichenbaum. 1992. “Current Real Business Cycle Theories and Aggregate labor Market Fluctuation.” American Economic Review 82: 430–450.

  • Christiano, L. J., M. Eichenbaum, and C. Evans. 2005. “Nominal Rigidities and the Dynamic effects of a Shock to Monetary Policy.” Journal of Political Economy 113: 1–45.

    • Crossref
  • Christoffel, K., C. Günter, and W. Anders. 2008. “The New Area-Wide Model of the Euro Area – A Micro-Founded Open-Economy Model for Forecasting and Policy Analysis.” European Central Bank Working Paper Series n. 944.

  • Chudik, A., and M. H. Pesaran. 2011. “Infinite-Dimensional VARs and Factor Models.” Journal of Econometrics 163 (1): 4–22.

    • Crossref
  • Clarida, R., J. Gal, and M. Gertler. 2000. “Monetary Policy Rules And Macroeconomic Stability: Evidence And Some Theory.” The Quarterly Journal of Economics 115 (1): 147–180.

    • Crossref
  • Consolo, A., C. A. Favero, and A. Paccagnini. 2009. “On the Statistical Identification of DSGE Models.” Journal of Econometrics 150: 99–115.

    • Crossref
  • Croushore, D., and T. Stark. 2001. “A Real-Time Data Set for Macroeconomists.” Journal of Econometrics 105: 111–130.

    • Crossref
  • Dawid, A. 1984. “Statistical Theory: The Prequential Approach.” Journal of the Royal Statistical Society Series A 147 (2): 278–292.

    • Crossref
  • DeJong, D., B. Ingram, and C. Whiteman. 1996. “A Bayesian Approach to Calibration.” Journal of Business Economics and Statistics 14: 1–9.

  • Del Negro, M., and F. Schorfheide. 2004. “Priors from General equilibrium Models for VARs.” International Economic Review 45: 643–673.

    • Crossref
  • Del Negro, M., and F. Schorfheide. 2006. “How Good Is What You’ve Got? DSGE-VAR as a Toolkit for Evaluating DSGE Models.” Federal Reserve Bank of Atlanta Economic Review 91, Second Quarter.

  • Del Negro, M., and F. Schorfheide. 2009. “Monetary Policy Analysis with Potentially Misspecified Models.” American Economic Review 99: 1415–1450.

    • Crossref
  • Del Negro, M., and F. Schorfheide. 2012. “DSGE Model-Based Forecasting.” Handbook of Economic Forecasting 2.

    • Crossref
  • Del Negro, M., F. Schorfheide, F. Smets, and R. Wouters. 2007. “On the Fit of New-Keynesisan Models.” Journal of Business, Economics and Statistics 25 (2): 124–162.

  • Diebold, F. X., and R. S. Mariano. 1995. “Comparing Predictive Accuracy.” Journal of Business and Economic Statistics 13 (3): 253–263.

  • Diebold, F. X., A. G. Todd, and A. S. Tay. 1998. “Evaluating Density Forecasts with Applications to Financial Risk Management.” International Economic Review 39: 863–883.

    • Crossref
  • Diebold, F. X., T. A. Gunther, and A. S. Tay. 1998. “Evaluating Density Forecasts; with Applications to Financial Risk Management.” International Economic Review 39: 863–883.

    • Crossref
  • Doan, T., R. Litterman, and C. Sims. 1984. “Forecasting and Conditional Projections Using Realistic Prior Distributions.” Econometric Reviews 3: 1–100.

    • Crossref
  • Dua, P., and S. C. Ray. 1995. “A BVAR Model for the Connecticut Economy.” Journal of Forecasting 14 (3): 167–180.

    • Crossref
  • Edge, R., M. 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): 720–754.

    • Crossref
  • Elder, R., G. Kapetanios, T. Taylor and T. Yates. 2005. “Assessing the MPC’s fan charts.” Bank of England Quarterly Bulletin (Autumn): 326–348.

  • Fernández-de-Córdoba, G., and J. L. Torres. 2010. “Forecasting the Spanish Economy with a DSGE Model: An Augmented VAR Approach.” Journal of the Spanish Economic Association 2 (3): 379–399.

  • Forni, M., and Reichlin, L. 1996. “Dynamic Common Factors in Large Cross-Sections.” Empirical Economics 21: 27–42.

    • Crossref
  • Forni, M., and L. Reichlin. 1998. “Let’s Get Real: A Dynamic Factor Analytical Approach to Disaggregated Business Cycle.” Review of Economic Studies 65: 453–474.

    • Crossref
  • Forni, M., M. Hallin, M. Lippi, and L. Reichlin. 1999. Reference Cycles: The NBER Methodology Revisited. mimeo.

  • Forni, M., M. Hallin, M. Lippi, and L. Reichlin. 2000. “The Generalized Dynamic-Factor Model: Identification And Estimation.” The Review of Economic and Statistics MIT Press, 82 (4): 540–554.

    • Crossref
  • Gerard, H., and K. Nimark. 2008 “Combing Multivariate Density Forecasts Using Predictive Criteria.” Research Discussion Paper 2008-2, Reserve Bank of Australia.

  • Geweke, J. 1999. “Using Simulation Methods for Bayesian Econometric Models: Inference.” Development and Communication Econometric Reviews 18 (1): 1–126.

    • Crossref
  • Ghent, A. 2009. “Comparing DSGE-VAR Forecasting Models: How Big are the Differences?” Journal of Economic Dynamics and Control 33 (4): 864–882.

    • Crossref
  • Harvey, D., S. Leybourne and P. Newbold. 1997. “Testing the equality of prediction mean squared errors.” International Journal of Forecasting 13: 281–291.

    • Crossref
  • Herbst, E., and F. Schorfheide. 2012. “Evaluating DSGE Model Forecasts of Comovements.” Journal of Econometrics 171 (2): 152–166.

  • Ingram, B., and C. Whiteman. 1994. “Supplanting the Minnesota Prior – Forecasting Macroeconomics Time Series using Real Business Cycle Model Priors.” Journal of Monetary Economics 34: 497–510.

    • Crossref
  • Ireland, P. 2004. “A Method for Taking Models to the Data.” Journal of Economic Dynamics and Control 28: 1205–1226.

    • Crossref
  • Kim, J. 2000. “Constructing and Estimating A Realistic Optimizing Model of Monetary Policy.” Journal of Monetary Economics 45 (2): 329–359.

    • Crossref
  • King, R. G. 2000. “The New IS-LM Model: Language, Logic, and Limits.” Federal Reserve Bank of Richmond Economic Quarterly 86: 45–103.

  • Kolasa, M., M. Rubaszek, and P. Skrzypczynski. 2009. “Putting the New Keynesian DSGE Model to the Real-Time Forecasting Test.” European Central Bank Working Paper Series n. 1110.

  • Kolasa, M., M. Rubaszek and P. Skrzypczynski. 2012. “Putting the New Keynesian DSGE Model to the Real-Time Forecasting Test.” Journal of Money, Credit and Banking 44 (7): 1301–1324.

    • Crossref
  • Kydland, F. E., and E. C. Prescott. 1982. “Time to Build and Aggregate fluctuations.” Econometrica 50 (6): 1345–1370.

    • Crossref
  • Leeper, E. M., and C. A. Sims. 1994. “Toward a modern macroeconomic model usable for policy analysis.” In NBER MAcroeconomics Annual 1994, edited by F. Stanley and J. J. Rotemberg, 81–118. Cambridge, MA: MIT Press.

    • Crossref
  • Litterman, R. B. 1981. “A Bayesian Procedure for Forecasting with Vector Autoregressions.” Working Paper, Federal Reserve Bank of Minneapolis.

  • Litterman, R. B. 1986. “Forecasting with Bayesian Vector Autoregressions: Five Years of Experience.” Journal of Business and Statistics 4 (1): 25–38.

  • Marcellino, M. 2004. “Forecasting EMU Macroeconomic Variables.” International Journal of Forecasting 20: 359–372.

    • Crossref
  • McGrattan, E. R. 1994. “The Macroeconomic Effects of Distortionary Taxation.” Journal of Monetary Economics 33 (3): 573–601.

    • Crossref
  • Newey, W. K., and K. D. West. 1987. “A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix.” Econometrica 55 (3): 703–708.

    • Crossref
  • Newey, W. K., and K. D. West. 1994. “Automatic Lag Selection in Covariance Matrix Estimation.” Review of Economic Studies 61 (4): 631–654.

    • Crossref
  • Robertson, J. C., and E. W. Tallman. 1999. “Vector Autoregressions: Forecasting and Reality.” Economic Review 84: 4–18.

  • Rosenblatt, M. 1952. “Remarks on a Multivariate Transformation.” Annals Mathematical Statistics 23: 470–472.

    • Crossref
  • Rotemberg, J. J., and M. Woodford. 1997. “An Optimization-Based Econometric Framework For The Evaluation of Monetary Policy.” In NBER Macroeconomics Annual 1997, edited by B. S. Bernanke and J. J. Rotemberg, 297–346. Cambridge, MA: MIT Press.

    • Crossref
  • Rubaszek M, Skrzypczynski P (2008) On the Forecasting Perfomance of a Small-Scale DSGE Model, International Journal of Forecasting, 24, 498–512.

    • Crossref
  • Sargent, T. 1989. “Two Models of Measurements and the Investment Accelerator.” Journal of Political Economy 97 (2): 251–287.

    • Crossref
  • Schorfheide, F. 2000. “Loss Function-Based Evaluation of DSGE Models.” Journal of Applied Econometrics 15 (6): 645–670.

    • Crossref
  • Schorfheide, F. 2010. Estimation and Evaluation of DSGE Models: Progress and Challenges. University of Pennsylvania.

    • Crossref
  • Sims, C. A. 1980. “Macroeconomics and Reality.” Econometrica 48 (1): 1–48.

    • Crossref
  • Sims, C. A. 2002. “Solving Linear Rational Expectations Models.” Computational Economics 20 (1–2): 1–20.

  • Sims, C. A., and T. Zha. 1998. “Bayesian Methods for Dynamic Multivariate Models.” International Economic Review 39: 949–968.

    • Crossref
  • Smets, F., and R. Wouters. 2003. “An Estimated Stochastic Dynamic General Equilibrium Model of the Euro Area.” Journal of the European Economic Association 1: 1123–1175.

    • Crossref
  • Smets, F., and R. Wouters. 2004. “Forecasting with a Bayesian DSGE Model: An Application to the Euro area.” Working Paper No. 389, European Central Bank, Frankfurt.

    • Crossref
  • Smets, F., and R. Wouters. 2007. “Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach.” American Economic Review 97 (3): 586–606.

    • Crossref
  • Spencer, D. E. 1993. “Developing a Bayesian Vector Autoregression Forecasting Model.” International Journal of Forecasting 9 (3): 407–421.

    • Crossref
  • Stock, J. H., and W. M. Watson. 2001. “Vector Autoregressions.” Journal of Economic Perspectives 15: 101–115.

    • Crossref
  • Stock, J. H., and W. M. Watson. 2002. “Macroeconomic Forecasting Using Diffusion Indexe.” Journal of Business Economics and Statistics XX:II: 147–162.

    • Crossref
  • Theil, H., and A. S. Goldberg. 1961. “On Pure and Mixed Estimation in Economics.” International Economic Review 2: 65–78.

    • Crossref
  • Todd, R. M. 1984. “Improving Economic Forecasting with Bayesian Vector Autoregression.” Quarterly Review Federal Reserve Bank of Minneapolis.

    • Crossref
  • Wolters, M. H. 2013. “Evaluating Point and Density Forecasts of DSGE Models.” Journal of Applied Econometrics, DOI: 10.1002/jae.2363.

    • Crossref
  • Woodford, M. 2003. Interest and Prices. Princeton, NJ: Princeton University Press.

Purchase article
Get instant unlimited access to the article.
$42.00
Log in
Already have access? Please log in.


or
Log in with your institution

Journal + Issues

SNDE recognizes that advances in statistics and dynamical systems theory can increase our understanding of economic and financial markets. The journal seeks both theoretical and applied papers that characterize and motivate nonlinear phenomena. Researchers are required to assist replication of empirical results by providing copies of data and programs online. Algorithms and rapid communications are also published.

Search