Forecasting national recessions using state-level data

Owyang, Michael T. and Piger, Jeremy and Wall, Howard J. (2012): Forecasting national recessions using state-level data.

Abstract

A large literature studies the information contained in national-level economic indicators, such as financial and aggregate economic activity variables, for forecasting and nowcasting U.S. business cycle phases (expansions and recessions.) In this paper, we investigate whether there is additional information useful for identifying business cycle phases contained in subnational measures of economic activity. Using a probit model to forecast the NBER expansion and recession classification, we assess the incremental information content of state-level employment growth over a commonly used set of national-level predictors. As state-level data adds a large number of predictors to the model, we employ a Bayesian model averaging procedure to construct forecasts. Based on a variety of forecast evaluation metrics, we find that including state-level employment growth substantially improves nowcasts and very short-horizon forecasts of the business cycle phase. The gains in forecast accuracy are concentrated during months of national recession.

• Item Type: MPRA Paper
• Language: English
• Keywords: turning points, Bayesian model averaging, nowcasting
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection; 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

Albert, J. H. and S. Chib (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association 88 (422), 669-679.

Brier, G. W. (1950). Verication of forecasts expressed in terms of probability. Monthly Weather Review 75 (January), 1-3.

Burns, A. F. and W. C. Mitchell (1946). Measuring business cycles. New York: National Bureau of Economic Research.

Carter, C. K. and R. Kohn (1994). On gibbs sampling for state-space models. Biometrika 81 (3), 541{553.

Casella, G. and E. I. George (1992). Explaining the Gibbs sampler. American Statistician 46 (3), 167{174.

Chauvet, M. and J. Piger (2008). A comparison of the real-time performance of business cycle dating methods. Journal of Business and Economic Statistics 26 (1), 42-49.

Estrella, A. (1997). A new measure of t for equations with dichotomous dependent variables. Journal of Business and Economic Statistics 16 (2), 198-205.

Estrella, A. and F. S. Mishkin (1998). Predicting U.S. recessions: Financial variables as leading indicators. The Review of Economics and Statistics 80 (1), 45-61.

Gelfand, A. E. and A. F. M. Smith (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 85 (410), 398-409.

Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57 (2), 357{384.

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

Hamilton, J. D. and M. T. Owyang (2011). The propagation of regional recessions. NBER working paper no. 16657.

Hernandez-Murillo, R. and M. T. Owyang (2006). The information content of regional employment data for forecasting aggregate conditions. Economic Letters 90 (3), 335-339.

Holmes, C. C. and L. Held (2006). Bayesian auxiliary variable models for binary and multinomial regression. Bayesian Analysis 1 (1), 145-168.

Katayama, M. (2008). Improving recession probability forecasts in the U.S. economy. Working Paper.

Kauppi, H. and P. Saikkonen (2008). Predicting U.S. recessions with dynamic binary response models. The Review of Economics and Statistics 90 (4), 777-791.

King, T. B., A. T. Levin, and R. Perli (2007). Financial market perceptions of recession risk. Federal Reserve Board Finance and Economics Discussion Series working paper no. 2007-57.

Mitchell, W. C. (1927). Business Cycles: The Problem and its Setting. New York: National Bureau of Economic Research.

Owyang, M. T., J. Piger, and H. J. Wall (2005). Business cycle phases in U.S. states. Review of Economics and Statistics 87 (4), 604-616.

Tanner, M. A. and W. H. Wong (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association 82 (398), 528-540.

Temin, P. (1998). The causes of american business cycles: An essay in economic historiography. In J. C. Fuhrer and S. Schuh (Eds.), Beyond Shocks: What Causes Business Cycles?, Conference Series No. 42, Boston, pp. 37-59. Federal Reserve Bank of Boston.

Wright, J. H. (2006). The yield curve and predicting recessions. Federal Reserve Board Finance and Economics Discussion Series working paper no. 2006-07.