Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data

Guérin, Pierre and Leiva-Leon, Danilo (2014): Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data.

Abstract

This paper estimates and forecasts U.S. business cycle turning points with state-level data. The probabilities of recession are obtained from univariate and multivariate regime-switching models based on a pairwise combination of national and state-level data. We use two classes of combination schemes to summarize the information from these models: Bayesian Model Averaging and Dynamic Model Averaging. In addition, we suggest the use of combination schemes based on the past predictive ability of a given model to estimate regimes. Both simulation and empirical exercises underline the utility of such combination schemes. Moreover, our best specification provides timely updates of the U.S. business cycles. In particular, the estimated turning points from this specification largely precede the announcements of business cycle turning points from the NBER business cycle dating committee, and compare favorably with competing models.

• Item Type: MPRA Paper
• Language: English
• Keywords: Markov-switching; Nowcasting; Forecasting; Business Cycles; Forecast combination.
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations ; Cycles; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications

References

Belmonte, M. A. G. and Koop, G. (2014). Model Switching and Model Averaging in Time-Varying Parameter Regression Models. Advances in Econometrics, forthcoming.

Berge, T. (2013). Predicting recessions with leading indicators: model averaging and selection over the business cycle. Federal Reserve Bank of Kansas City Working Paper 13-05.

Billio, M., Casarin, R., Ravazzolo, F., and van Dijk, H. K. (2012). Combination schemes for turning point predictions. The Quarterly Review of Economics and Finance, 52(4):402-412.

Billio, M., Casarin, R., Ravazzolo, F., and van Dijk, H. K. (2013). Time-varying combinations of predictive densities using nonlinear filtering. Journal of Econometrics, 177(2):213-232.

Cakmakli, C., Paap, R., and van Dijk, D. (2013). Measuring and predicting heterogeneous recessions. Journal of Economic Dynamics and Control, 37(11):2195-2216.

Camacho, M., Perez-Quiros, G., and Poncela, P. (2012). Markov-switching dynamic factor models in real time. CEPR Working Paper, February.

Camacho, M., Perez-Quiros, G., and Poncela, P. (2014). Extracting nonlinear signals from several economic indicators. Journal of Applied Econometrics, forthcoming.

Chan, J. C., Koop, G., Leon-Gonzalez, R., and Strachan, R. W. (2012). Time Varying Dimension Models. Journal of Business & Economic Statistics, 30(3):358-367.

Chauvet, M. (1998). An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching. International Economic Review, 39(4):969-96.

Chauvet, M. and Hamilton, J. D. (2006). Dating Business Cycle Turning Points in Real Time. Nonlinear Time Series Analysis of Business Cycles.

Chauvet, M. and Piger, J. (2008). A Comparison of the Real-Time Performance of Business Cycle Dating Methods. Journal of Business Economics and Statistics, 26(1):42-49.

Chib, S. (1995). Marginal Likelihood From the Gibbs Output. Journal of the American Statistical Association, 90:1313-1321.

Croushore, D. and Stark, T. (2001). A real-time data set for macroeconomists. Journal of Econometrics, 105(1):111-130.

Diebold, F. X. and Mariano, R. S. (1995). Comparing Predictive Accuracy. Journal of Business & Economic Statistics, 13(3):253-63.

Elliott, G. and Timmermann, A. (2005). Optimal Forecast Combination Under Regime Switching. International Economic Review, 46(4):1081-1102.

Fruhwirth-Schnatter, S. (2004). Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques. Econometrics Journal, 7(1):143-167.

Geweke, J. and Amisano, G. (2011). Optimal prediction pools. Journal of Econometrics, 164(1):130-141.

Granger, C. W. J. and Terasvirta, T. (1999). A simple nonlinear time series model with misleading linear properties. Economics Letters, 62(2):161-165.

Guerin, P. and Marcellino, M. (2013). Markov-Switching MIDAS Models. Journal of Business & Economic Statistics, 31(1):45-56.

Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2):357-84.

Hamilton, J. D. (2011). Calling recessions in real time. International Journal of Forecasting, 27(4):1006-1026.

Hamilton, J. D. and Owyang, M. T. (2012). The Propagation of Regional Recessions. The Review of Economics and Statistics, 94(4):935-947.

Hamilton, J. D. and Perez-Quiros, G. (1996). What Do the Leading Indicators Lead? The Journal of Business, 69(1):27-49.

Hoeting, J. A., Madigan, D., Raftery, A. E., and Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4):382-417.

Kholodilin, K. A. and Yao, V. W. (2005). Measuring and predicting turning points using a dynamic bi-factor model. International Journal of Forecasting, 21(3):525-537.

Kim, C. and Nelson, C. (1999). State-space models with regime switching: classical and Gibbs-sampling approaches with applications. The MIT press.

Kim, C.-J. (1994). Dynamic linear models with Markov-switching. Journal of Econometrics, 60(1-2):1-22.

Kim, C.-J. and Nelson, C. R. (1998). Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching. The Review of Economics and Statistics., 80(2):188-201.

Kim, M.-J. and Yooa, J.-S. (1995). New index of coincident indicators: A multivariate Markov switching factor model approach. Journal of Monetary Economics, 36(3):607-630.

Koop, G. (2014). Forecasting with dimension switching VARs. International Journal of Forecasting, 30(2):280-290.

Koop, G. and Korobilis, D. (2011). UK macroeconomic forecasting with many predictors: Which models forecast best and when do they do so? Economic Modelling, 28(5):2307- 2318.

Koop, G. and Korobilis, D. (2012). Forecasting In ation Using Dynamic Model Averaging. International Economic Review, 53(3):867-886.

Koop, G. and Korobilis, D. (2013). Large time-varying parameter VARs. Journal of Econometrics, 177(2):185-198.

Koop, G. and Onorante, L. (2012). Estimating Phillips curves in turbulent times using the ECB's survey of professional forecasters. Working Paper Series European Central Bank, (1422).

Koop, G. and Onorante, L. (2013). Macroeconomic Nowcasting Using Google Probabilities. mimeo.

Koop, G. and Tole, L. (2013). Forecasting the European Carbon Market. Journal of the Royal Statistical Society Series A, 176(3):723-741.

Leiva-Leon, D. (2014). A New Approach to Infer Changes in the Synchronization of Business Cycle Phases. Working Paper Series Bank of Canada, (2014-38).

Del Negro, M., Giannoni, M. P., and Schorfheide, F. (2013). Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance. mimeo.

Nalewaik, J. J. (2012). Estimating Probabilities of Recession in Real Time Using GDP and GDI. Journal of Money, Credit and Banking, 44(1):235-253.

Newton, M. A. and Raftery, A. E. (1994). Approximate Bayesian inference with the weighted likelihood boostrap. Journal of the Royal Statistical Society: Series B, 56:3-26.

Owyang, M. T., Piger, J., and Wall, H. J. (2005). Business Cycle Phases in U.S. States. The Review of Economics and Statistics, 87(4):604-616.

Owyang, M. T., Piger, J. M., and Wall, H. J. (2014). Forecasting national recessions using state level data. Journal of Money, Credit, and Banking, forthcoming.

Raftery, A., Karny, M., and Ettler, P. (2010). Online Predictions Under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill. Technometrics, 52:52-66.

Raftery, A. E., Madigan, D., and Hoeting, J. A. (1998). Bayesian Model Averaging for Linear Regression Models. Journal of the American Statistical Association, 92:179-191.

Timmermann, A. (2006). Forecast Combinations. Handbook of Economic Forecasting, 1:135-196.

West, K. D. (1996). Asymptotic Inference about Predictive Ability. Econometrica, 64(5):1067-84.

Wright, J. H. (2013). Unseasonal Seasonals? Brookings Papers on Economic Activity, 47(2(Fall)):65-126.