Abstract
This paper proposes a probabilistic model based on comovements and nonlinearities useful to assess the type of shock affecting each phase of the business cycle. By providing simultaneous inferences on the phases of real activity and inflation cycles, contractionary episodes are dated and categorized into demand, supply and mix recessions. The impact of shocks originated in the housing market over the business cycle is also assessed, finding that recessions are usually accompanied by housing deflationary pressures, while expansions are mainly influenced by housing demand shocks, with the only exception occurred during the period surrounding the “Great Recession,” affected by expansionary housing supply shocks.
Acknowledgment
Thanks to Maximo Camacho, Gabriel Perez-Quiros, the editor and an anonymous referee for valuable comments and suggestions. Thanks to the participants at 20th Symposium of the Society for Nonlinear Dynamics and Econometrics, 31st Annual International Symposium on Forecasting, Quantitative Economics Doctorate Jamboree and internal seminar series at the University of Alicante for discussions that improved the content of this paper. The views expressed in this paper are those of the author and do not represent the views of the Bank of Canada.
Appendix
It is assumed that m=2, and k=0. Focusing first on the real activity versus inflation model, according to the eleven-variable model used in the empirical application, the measurement equation, yt=Hβt+et, with et~N(0,R), is

where R is a matrix of zeroes. The notation of the variables is defined as: GDP is real GDP, IND is industrial production, PIN is real personal income less transfers, SAL is real manufacturing trading sales, PAY is total non-farm labor, DEF is deflator of GDP, CPI is consumer price index, PPI is producer price index, GSC is Standard and Poor’s GSCI non-energy commodities price index, OIL is spot oil price, and HCN hourly compensation in the non-farm business sector.
The transition equation,

where Q is a diagonal matrix in which the entries inside the main diagonal are collected in the vector

For the real activity versus housing prices model, the measurement and transition equations follow the same reasoning as in Equations (A.1) and (A.2), respectively. The main change, apart from adjusting the appropriate dimension of the matrices for 10 observed indicators, is that vector yt, as defined in Equation (A.1), is replaced by
The notation of the housing price indicators is defined as: PDI is Price deflator index of new single-family houses under construction, CMH is Conventional Mortgage Home Price Index, ATP is FHFA All-transactions Price Index, POI is FHFA Purchase-only Index, SPC is S&P Case-Shiller-10-cities Home Price Index.
References
Aruoba, B., and F. Diebold. 2010. “Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions.” American Economic Review 100 (2): 20–24.10.1257/aer.100.2.20Search in Google Scholar
Banbura, M., and G. Rustler. 2007. “A Look into the Model Factor Black Box. Publication Lags and the Role of Hard and Soft Data in Forecasting GDP.” ECB working paper No. 751.10.2139/ssrn.984265Search in Google Scholar
Bengoechea, P., M. Camacho, and G. Perez-Quiros. 2006. “A Useful Tool for Forecasting the Euro-Area Business Cycle Phases.” International Journal of Forecasting 22 (4): 735–749.10.1016/j.ijforecast.2006.01.002Search in Google Scholar
Blanchard, J., and D. Quah. 1989. “The Dynamic Effects of Aggregate and Supply Disturbances.” American Economic Review 79 (4): 655–673.Search in Google Scholar
Blanchard, J., and D. Quah. 1993. “The Dynamic Effects of Aggregate and Supply Disturbances: Reply.” American Economic Review 83 (3): 653–658.Search in Google Scholar
Burns, A., and W. Mitchell. 1946. “Measuring Business Cycles.” NBER Book Series Studies in Business Cycles. New York.Search in Google Scholar
Camacho, M., and G. Perez-Quiros. 2007. “Jump-and-Rest Effect of U.S. Business Cycles.” Studies in Nonlinear Dynamics and Econometrics 11 (4): 1558–3708.10.2202/1558-3708.1480Search in Google Scholar
Camacho, M., and G. Perez-Quiros. 2010. “Introducing the Euro-Sting: Short-Term Indicator of Euro Area Growth.” Journal of Applied Econometrics 25 (4): 663–694.10.1002/jae.1174Search in Google Scholar
Camacho, M., G. Perez-Quiros, and P. Poncela. 2010. “Green Shoots in the Euro Area. A Real Time Measure.” International Journal of Forecasting, http://www.sciencedirect.com/science/article/pii/S0169207013000320. In Press.10.2139/ssrn.1646848Search in Google Scholar
Chauvet, M. 1998. “An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switches.” International Economic Review 39 (4): 969–996.10.2307/2527348Search in Google Scholar
Del Negro, M., and C. Otrok. 2007. “99 Luftballons: Monetary Policy and the House Price Boom Across U.S. States.” Journal of Monetary Economics 54 (7): 1962–1985.10.1016/j.jmoneco.2006.11.003Search in Google Scholar
Forni, M., and L. Gambetti. 2010. “Macroeconomic Shocks and the Business Cycle: Evidence from a Structural Factor Model.” Recent, Center for Economic Research. Working paper series. No. 40.Search in Google Scholar
Galí, J. 1989. “The Dynamic Effects of Aggregate Demand and Supply Disturbances.” American Economic Review 79 (4): 655–673.Search in Google Scholar
Galí, J. 1992. “How Well Does the IS-LM Model Fit Postwar U.S. Data?” Quarterly Journal of Economics 107 (2): 709–738.10.2307/2118487Search in Google Scholar
Galí, J. 1999. “Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?” American Economic Review 89 (1): 249–271.Search in Google Scholar
Hamilton, J. 1989. “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica 57 (2): 357–384.10.2307/1912559Search in Google Scholar
Ireland, P. 2010. “A New Keynesian Perspective on the Great Recession.” Journal of Money, Credit and Banking, Blackwell Publishing 43 (1): 31–54.10.1111/j.1538-4616.2010.00364.xSearch in Google Scholar
Kholodilin, K., and V. Yao. 2005. “Measuring and Predicting Turning Points using A Dynamic bi-Factor Model.” International Journal of Forecasting 21 (3): 525–537.10.1016/j.ijforecast.2005.02.002Search in Google Scholar
Kim, C. 1994. “Dynamic Linear Models with Markov-Switching.” Journal of Econometrics 60 (1–2): 1–22.10.1016/0304-4076(94)90036-1Search in Google Scholar
Kim, C., and C. Nelson. 1998. “Business Cycle Turning Points, A New Coincident Index, and Tests of Duration Dependence Based on A Dynamic Factor Model with Regime Switching.” Review of Economics and Statistics 80 (2): 188–201.10.1162/003465398557447Search in Google Scholar
Kim, C., and C. Nelson. 1999. State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications, 1st edition, Vol. 1, No. 0262112388. MIT Press Books.Search in Google Scholar
Kim, C., and J. Yoo. 1995. “New Index of Coincident Indicators: A Multivariate Markov Switching Factor Model Approach.” Journal of Monetary Economics 36 (3): 607–630.10.1016/0304-3932(95)01229-XSearch in Google Scholar
Lippi M., and L. Reichlin. 1993. “The Dynamic Effects of Aggregate Demand and Supply Disturbances: Comment.” American Economic Review 83 (3): 644–652.Search in Google Scholar
Mariano, R., and Y. Murasawa. 2003. “A New Coincident Index of Business Cycles Based on Monthly and Quarterly Series.” Journal of Applied Econometrics 18 (4): 427–443.10.1002/jae.695Search in Google Scholar
Ng, S., and E. Moench. 2011. “A Hierarchical Factor Analysis of US Housing Market Dynamics.” Econometrics Journal 14 (1): C1–C24.10.1111/j.1368-423X.2010.00319.xSearch in Google Scholar
Stock, J., and M. Watson. 1991. “A Probability Model of the Coincident Economic Indicators.” Leading economic indicators: new approaches and forecasting records, edited by Lahiri, H., and Moore, G. Cambridge University Press. http://assets.cambridge.org/97805214/38582/excerpt/9780521438582_excerpt.pdfSearch in Google Scholar
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