Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-26T21:34:33.380Z Has data issue: false hasContentIssue false
  • English
  • Français

MACROPRUDENTIAL POLICY AND FORECASTING USING HYBRID DSGE MODELS WITH FINANCIAL FRICTIONS AND STATE SPACE MARKOV-SWITCHING TVP-VARS

Published online by Cambridge University Press:  17 June 2014

Stelios D. Bekiros  and
Alessia Paccagnini
Stelios D. Bekiros*
Affiliation:
European University Institute, Rimini Centre for Economic Analysis and Athens University of Economics and Business
Alessia Paccagnini
Affiliation:
Università degli Studi di Milano-Bicocca
*
Address correspondence to: Stelios Bekiros, Department of Economics, Via della Piazzuola 43, I-50133, Florence, Italy; e-mail: stelios.bekiros@eui.eu.
Get access Rights & Permissions [Opens in a new window]

Abstract

We focus on the interaction of frictions both at the firm level and in the banking sector in order to examine the transmission mechanism of the shocks and to reflect on the response of the monetary policy to increases in interest rate spreads, using DSGE models with financial frictions. However, VAR models are linear and the solutions of DSGEs are often linear approximations; hence they do not consider time variation in parameters that could account for inherent nonlinearities and capture the adaptive underlying structure of the economy, especially in crisis periods. A novel method for time-varying VAR models is introduced. As an extension to the standard homoskedastic TVP-VAR, we employ a Markov-switching heteroskedastic error structure. Overall, we conduct a comparative empirical analysis of the out-of-sample performance of simple and hybrid DSGE models against standard VARs, BVARs, FAVARs, and TVP-VARs, using data sets from the U.S. economy. We apply advanced Bayesian and quasi-optimal filtering techniques in estimating and forecasting the models.

Keywords

Financial FrictionsTime-Varying CoefficientsQuasi-optimal Filtering
Type
Articles
Information
Macroeconomic Dynamics , Volume 19 , Issue 7 , October 2015 , pp. 1565 - 1592
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Adolfson, M., Stefan, L., Jesper, L., Linde, J., and Mattias, V. (2008a) Empirical properties of closed- and open-economy DSGE models of the euro area. Macroeconomic Dynamics 12, 2–19.CrossRefGoogle Scholar
Adolfson, M., Stefan, L., Jesper, L., and Mattias, V. (2008b) Evaluating an estimated New Keynesian small open economy model. Journal of Economic Dynamics and Control 32 (8), 2690–2721.CrossRefGoogle Scholar
Bekiros, S. and Paccagnini, A. (2013) On the predictability of time-varying VAR and DSGE models. Empirical Economics 45 (1), 635–664.CrossRefGoogle Scholar
Bernanke, B.S., Boivin, J., and Eliasz, P. (2005) Measuring the effects of monetary policy: A factor-augmented vector autoregressive (FAVAR) approach. Quarterly Journal of Economics 120 (1), 387–422.Google Scholar
Bernanke, B., Gertler, M., and Gilchrist, S. (1999) The financial accelerator in a quantitative business cycle framework. In Taylor, J.B. and Woodford, M. (eds.), Handbook of Macroeconomics, Vol. 1, pp. 1341–1393.CrossRefGoogle Scholar
Christiano, L., Eichenbaum, M., and Evans, C. (2005) Nominal rigidities and the dynamic effects of a shock to monetary policy. Journal of Political Economy 113, 1–45.CrossRefGoogle Scholar
Christiano, L., Motto, R., and Rostagno, M. (2010) Financial Factors in Economics Fluctuations. Working paper 1192, European Central Bank.CrossRefGoogle Scholar
Clarida, R., Galí, J., and Gertler, M. (2000) Monetary policy rules and macroeconomic stability: Evidence and some theory. Quarterly Journal of Economics 115 (1), 147–180.CrossRefGoogle Scholar
Consolo, A., Favero, C.A., and Paccagnini, A. (2009) On the statistical identification of DSGE models. Journal of Econometrics 150, 99–115.CrossRefGoogle Scholar
Del Negro, M. and Schorfheide, F. (2004) Priors from general equilibrium models for VARs. International Economic Review 45, 643–673.CrossRefGoogle Scholar
Del Negro, M. and Schorfheide, F. (2012) DSGE model-based forecasting. In G. Elliott and Timmermann, A. (eds.), Handbook of Economic Forecasting, Vol. 2.CrossRefGoogle Scholar
Del Negro, M., Schorfheide, F., Smets, F., and Wouters, R. (2007) On the fit of New-Keynesisan models. Journal of Business, Economics and Statistics 25 (2), 124–162.CrossRefGoogle Scholar
Diebold, F.X. and Mariano, R.S. (1995) Comparing predictive accuracy. Journal of Business and Economic Statistics 13 (3), 253–263.Google Scholar
Doan, T., Litterman, R., and Sims, C. (1984) Forecasting and conditional projections using realistic prior distributions. Econometric Reviews 3, 1–100.CrossRefGoogle Scholar
Durbin, J. and Koopman, S.J. (2002) Time Series Analysis by State Space Methods. Oxford, UK: Oxford University Press.Google Scholar
Forni, M., Hallin, M., Lippi, M., and Reichlin, L. (2000) The generalized dynamic-factor model: Identification and estimation. Review of Economic and Statistics 82 (4), 540–554.CrossRefGoogle Scholar
Forni, M. and Reichlin, L. (1998) Let's get real: A dynamic factor analytical approach to disaggregated business cycle. Review of Economic Studies 65, 453–474.CrossRefGoogle Scholar
Gerali, A., Neri, S., Sessa, L., and Signoretti, F. (2010) Credit and banking in a DSGE model of the euro area. Journal of Money, Credit and Banking 42, 107–141.CrossRefGoogle Scholar
Gertler, M. and Karadi, P. (2011) A model of unconventional monetary policy. Journal of Monetary Economics 58 (1), 17–34.CrossRefGoogle Scholar
Gertler, M. and Kiyotaki, N. (2010) Financial intermediation and credit policy in business cycle analysis. In Handbook of Monetary Economics, Vol. 3, pp. 547–599. Elsevier, North-Holland.Google Scholar
Geweke, J. (1999) Using simulation methods for Bayesian econometric models: Inference, development and communication. Econometric Reviews 18 (1), 1–126.CrossRefGoogle Scholar
Hamilton, J. (1988) Rational-expectation econometric analysis of changes in regime: An investigation of the term structure of interest rates. Journal of Economic Dynamics and Control 12, 385–423.CrossRefGoogle Scholar
Harvey, A.C. (1986) Analysis and generalization of a multivariate exponential smoothing model. Management Science 32, 374–380.CrossRefGoogle Scholar
Harvey, A.C. (1990) Forecasting, Structural Time Series and the Kalman Filter. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Harvey, D., Leybourne, S., and Newbold, P. (1997) Testing the equality of prediction mean squared errors. International Journal of Forecasting 13, 281–291.CrossRefGoogle Scholar
Iacoviello, M. (2005) House prices, borrowing constraints, and monetary policy in the business cycle. American Economic Review 95 (3), 739–764.CrossRefGoogle Scholar
Ireland, P. (2004) A method for taking models to the data. Journal of Economic Dynamics and Control 28, 1205–1226.CrossRefGoogle Scholar
Kadiyala, K.R. and Karlsson, S. (1997) Numerical methods for estimation and inference in Bayesian VAR-models. Journal of Applied Econometrics 12 (2), 99–132.3.0.CO;2-A>CrossRefGoogle Scholar
Kim, C.-J. (1993) Unobserved-component time series models with Markov-switching heteroscedasticity: Changes in regime and the link between inflation rates and inflation uncertainty. Journal of Business and Economic Statistics 11 (3), 341–349.Google Scholar
Kim, C.-J. and Nelson, C.R. (1999a) Has the U.S. economy become more stable? A Bayesian approach based on a Markov-switching model of the business cycle. Review of Economics and Statistics 81 (4), 608–616.CrossRefGoogle Scholar
Kim, C.-J. and Nelson, C.R. (1999b) State Space Models with Regime Switching. Cambridge, MA: MIT Press.Google Scholar
Kiyotaki, N. and Moore, J. (1997) Credit cycles. Journal of Political Economy 105, 211–248.CrossRefGoogle Scholar
Lamoureux, C.G. and Lastrapes, W. (1990) Persistence in variance, structural change, and the GARCH model. Journal of Business and Economic Statistics 8, 225–234.Google Scholar
Lansing, K. (2009) Time varying U.S. inflation dynamics and the New Keynesian Phillips curve. Review of Economic Dynamics 12 (2), 304–326.CrossRefGoogle Scholar
Litterman, R.B. (1981) A Bayesian Procedure for Forecasting with Vector Autoregressions. Working paper, Federal Reserve Bank of Minneapolis.Google Scholar
Orphanides, A. and Williams, J.C. (2008) Learning, expectations formation, and the pitfalls of optimal control monetary policy. Journal of Monetary Economics 55.CrossRefGoogle Scholar
Prado, G.H. and West, M. (2001) Bayesian time-varying autoregressions: Theory, methods and applications. Resenhas Journal 4, 405–422.Google Scholar
Primiceri, G.E. (2005) Time varying structural vector autoregressions and monetary policy. Review of Economic Studies 72, 821–852.CrossRefGoogle Scholar
Schorfheide, F. (2011) Estimation and Evaluation of DSGE Models: Progress and Challenges. NBER Working Paper 16781.Google Scholar
Sims, C.A. (1980) Macroeconomics and reality. Econometrica 48 (1), 1–48.CrossRefGoogle Scholar
Smets, F. and Wouters, R. (2003) An estimated stochastic dynamic general equilibrium model of the euro area. Journal of the European Economic Association 1, 1123–1175.CrossRefGoogle Scholar
Smets, F. and Wouters, R. (2004) Forecasting with a Bayesian DSGE Model: An Application to the Euro Area. Working paper 389, European Central Bank, Frankfurt, Germany.CrossRefGoogle Scholar
Todd, R.M. (1984) Improving economic forecasting with Bayesian vector autoregression. Federal Reserve Bank of Minneapolis, Quarterly Review 8 (4), 1–13.Google Scholar