Leiva-Leon, Danilo (2013): Real vs. Nominal Cycles: A Multistate Markov-Switching Bi-Factor Approach. Forthcoming in: Studies in Nonlinear Dynamics & Econometrics
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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.
Item Type: | MPRA Paper |
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Original Title: | Real vs. Nominal Cycles: A Multistate Markov-Switching Bi-Factor Approach |
Language: | English |
Keywords: | Business Cycles, Inflation Cycles, Housing Price Cycles, Dynamics Factors, Markov-Switching. |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E27 - Forecasting and Simulation: Models and Applications E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations ; Cycles |
Item ID: | 54456 |
Depositing User: | Dr. Danilo Leiva-Leon |
Date Deposited: | 19 Mar 2014 07:29 |
Last Modified: | 27 Sep 2019 14:55 |
References: | [1] Aruoba, B., and F. Diebold (2010). "Real-time macroeconomic monitoring: Real activity, inflation, and interactions." American Economic Review, 100,2, 20-24. [2] 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. [3] bibitem : 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. [4] Blanchard, J., and D. Quah (1989). "The Dynamic Effects of Aggregate and Supply Disturbances." American Economic Review, 79, 4, 655-673. [5] Blanchard, J., D. Quah (1993). "The Dynamic Effects of Aggregate and Supply Disturbances: Reply." American Economic Review, 83, 3, 653-658. [6] Burns, A., and W. Mitchell (1946). "Measuring business cycles." National Bureau of Economic Research. New york. [7] Camacho, M., and G. Perez-Quiros (2007). "Jump-and-rest effect of U.S. business cycles." Studies in Nonlinear Dynamics and Econometrics, 11, 4. [8] 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. [9] Camacho, M., G. Perez-Quiros, and P. Poncela (2010). "Green shoots in the Euro area. A real time measure." International Journal of Forecasting, forthcoming. [10] Chauvet, M. (1998). "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switches." International Economic Review, 39, 4, 969-96. [11] 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. [12] Diebold, F., and G. Rudebusch (1996). "Measuring business cycles: A modern perspective." Review of Economics and Statistics, 78,1, 67-77. [13] 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. [14] Galí, J. (1989). "The Dynamic Effects of Aggregate Demand and Supply Disturbances." American Economic Review. 79, 4, 655-673. [15] Galí, J. (1992). "How well does the IS-LM model fit postwar U.S. data?" Quarterly Journal of Economics,107, 2, 709-738. [16] Galí, J. (1999). "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?" American Economic Review, 89, 1, 249-271. [17] Hamilton, J. (1989). "A new approach to the economic analysis of nonstationary time series and the business cycle." Econometrica, 57, 2, 357-384. [18] Hamilton, J. (1983). "Oil and the macroeconomy since world war II." Journal of Political Economy, 91, 2, 228-248. [19] Harrison, P., and C. Steven (1976). "Bayesian Forecasting." Journal of the Royal Statistical Society, B, 38, 205-247. [20] Ireland, P. (2010). "A New Keynesian Perspective on the Great Recession." Journal of Money, Credit and Banking, Blackwell Publishing, 43, 1, 31-54. [21] 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. [22] Kim, C. (1994). "Dynamic linear models with Markov-switching." Journal of Econometrics, 60, 1-22, 1-22. [23] 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. [24] Kim, C., and C. Nelson (1999). "State-space models with regime switching: Classical and gibbs-sampling approaches with applications." MIT Press. [25] 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. [26] Lippi M., and L. Reichlin (1993). "The Dynamic Effects of Aggregate Demand and Supply Disturbances: Comment." American Economic Review, 83, 3, 644-652. [27] 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. [28] Ng, S., and E. Moench (2011). "A Hierarchical Factor Analysis of US Housing Market Dynamics." Econometrics Journal, 14, 1, C1-C24. [29] 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. [30] Stock, J., and M. Watson, M (1999). "Forecasting inflation." Journal of Monetary Economics, 44, 2, 293-335. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/54456 |