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A BIVARIATE AUTOREGRESSIVE PROBIT MODEL: BUSINESS CYCLE LINKAGES AND TRANSMISSION OF RECESSION PROBABILITIES

Published online by Cambridge University Press:  18 March 2013

Henri Nyberg
Henri Nyberg*
Affiliation:
University of Helsinki and HECER
*
Address correspondence to: Henri Nyberg, Department of Political and Economic Studies, P.O. Box 17 (Arkadiankatu 7), 00014University of Helsinki, Finland; e-mail: [email protected].
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Abstract

I propose a new binary bivariate autoregressive probit model of the state of the business cycle. This model nests various special cases, such as two separate univariate probit models used extensively in the previous literature. The parameters are estimated by the method of maximum likelihood and forecasts can be computed by explicit formulae. The model is applied to predict the U.S. and German business cycle recession and expansion periods. Evidence of in-sample and out-of-sample predictability of recession periods by financial variables is obtained. The proposed bivariate autoregressive probit model allowing links between the recession probabilities in the United States and Germany turns out to outperform two univariate models.

Keywords

Bivariate Probit ModelAutoregressive ModelRecessionFinancial Variables
Type
Articles
Information
Macroeconomic Dynamics , Volume 18 , Issue 4 , June 2014 , pp. 838 - 862
Copyright
Copyright © Cambridge University Press 2013 

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References

REFERENCES

Ahrens, Ralf (2002) Predicting recessions with interest rate spreads: A multicountry regime-switching analysis. Journal of International Money and Finance 21, 519–537.Google Scholar
Anatolyev, Stanislav (2009) Multi-market direction-of-change modeling using dependence ratios. Studies in Nonlinear Dynamics and Econometrics 13, Article 5.Google Scholar
Anderson, Heather M. and Vahid, Farshid (2001) Predicting the probability of a recession with nonlinear autoregressive leading indicator models. Macroeconomic Dynamics 5, 482–505.Google Scholar
Artis, Michael, Galvao, Ana B., and Marcellino, Massimiliano (2007) The transmission mechanism in a changing world. Journal of Applied Econometrics 22, 39–61.Google Scholar
Ashford, J.R. and Sowden, R.R. (1970) Multivariate probit analysis. Biometrics 26, 535–546.Google Scholar
Benjamin, Michael A., Rigby, Robert A., and Stasinopoulos, D. Mikis (2003) Generalized autoregressive moving average models. Journal of the American Statistical Association 98, 214–223.Google Scholar
Bernard, Henri and Gerlach, Stefan (1998) Does the term structure predict recessions? The international evidence. International Journal of Finance and Economics 3, 195–215.Google Scholar
Canova, Fabio and Marriman, Jane (1998) Sources and propagation of international output cycles: Common shocks or transmission. Journal of International Economics 46, 133–166.CrossRefGoogle Scholar
Chauvet, Marcelle and Potter, Simon (2005) Forecasting recession using the yield curve. Journal of Forecasting 24, 77–103.Google Scholar
Chib, Siddhartha and Greenberg, Edward (1998) Analysis of multivariate probit models. Biometrika 85, 347–361.Google Scholar
Cox, David R. (1981) Statistical analysis of time series: Some recent developments. Scandinavian Journal of Statistics 8, 93–115.Google Scholar
Dees, Stephane, di Mauro, Filippo, Pesaran, Hashem M., and Smith, Vanessa L. (2007) Exploring the international linkages of the euro area: A global VAR analysis. Journal of Applied Econometrics 22, 1–38.CrossRefGoogle Scholar
de Jong, Robert M. and Woutersen, Tiemen (2011) Dynamic time series binary choice. Econometric Theory 27, 673–702.Google Scholar
Diebold, Francis X. and Rudebusch, Glenn D. (1989) Scoring the leading indicators. Journal of Business 62, 369–391.Google Scholar
Dueker, Michael J. (1997) Strengthening the case for the yield curve as a predictor of U.S. recessions. Federal Reserve Bank of St. Louis Review 79, 41–51.Google Scholar
Dueker, Michael J. (2005) Dynamic forecasts of qualitative variables: A qual VAR model of U.S. recessions. Journal of Business and Economic Statistics 23, 96–104.Google Scholar
Eickmeier, Sandra (2007) Business cycle transmission from the US to Germany: A structural factor approach. European Economic Review 51, 521–551.Google Scholar
Ekholm, Anders, Smith, Peter W.J., and McDonald, John W. (1995) Marginal regression analysis of a multivariate binary response. Biometrika 82, 847–854.Google Scholar
Engemann, Kristie M., Klisen, Kevin L., and Owyang, Michael T. (2011) Do oil shocks drive business cycles? Some U.S. and international evidence. Macroeconomic Dynamics 15, 498–517.CrossRefGoogle Scholar
Estrella, Arturo (2005a) The Yield Curve as a Leading Indicator: FrequentlyAsked Questions. Federal Reserve Bank of New York. Available at http://www.newyorkfed.org/research/capital_markets/ycfaq.pdf (accessed 25 March 2011).Google Scholar
Estrella, Arturo (2005b) Why does the yield curve predict output and inflation? Economic Journal 115, 722–744.CrossRefGoogle Scholar
Estrella, Arturo and Hardouvelis, Gikas A. (1991) The term structure as a predictor of real economic activity. Journal of Finance 46, 555–576.CrossRefGoogle Scholar
Estrella, Arturo and Mishkin, Frederic S. (1998) Predicting U.S. recessions: Financial variables as leading indicators. Review of Economics and Statistics 80, 45–61.CrossRefGoogle Scholar
Estrella, Arturo, Rodrigues, Anthony P., and Schich, Sebastian (2003) How stable is the predictive power of the yield curve? Evidence from Germany and the United States. Review of Economics and Statistics 85, 629–644.CrossRefGoogle Scholar
Fama, Eugene F. (1990) Stock returns, expected returns, and real activity. Journal of Finance 45, 1089–1108.Google Scholar
Greene, William H. (2000) Econometric Analysis, 4th ed. London: Prentice-Hall International.Google Scholar
Hamilton, James D. (2011) Nonlinearities and the macroeconomic effects of oil prices. Macroeconomic Dynamics 15, 364–378.CrossRefGoogle Scholar
Harding, Don and Pagan, Adrian R. (2011) An econometric analysis of some models for constructed binary time series. Journal of Business and Economic Statistics 29, 86–95.Google Scholar
Kauppi, Heikki and Saikkonen, Pentti (2008) Predicting U.S. recessions with dynamic binary response models. Review of Economics and Statistics 90, 777–791.CrossRefGoogle Scholar
Mosconi, Rocco and Seri, Raffaello (2006) Non-causality in bivariate binary time series. Journal of Econometrics 132, 379–407.CrossRefGoogle Scholar
Nyberg, Henri (2010) Dynamic probit models and financial variables in recession forecasting. Journal of Forecasting 29, 215–230.Google Scholar
Paap, Richard, Segers, Rene, and van Dijk, Dick (2009) Do leading indicators lead peaks more than troughs? Journal of Business and Economic Statistics 27, 528–543.Google Scholar
Rudebusch, Glenn D. and Williams, John C. (2009) Forecasting recessions: The puzzle of the enduring power of the yield curve. Journal of Business and Economic Statistics 27, 492–503.Google Scholar
Rydberg, Tina H. and Shephard, Neil (2003) Dynamics of trade-by-trade price movements: Decomposition and models. Journal of Financial Econometrics 1, 2–25.Google Scholar
Schwarz, Gideon (1978) Estimating the dimension of a model. Annals of Statistics 6, 461–464.Google Scholar
Sensier, Marianne, Artis, Michael, Osborn, Denise R., and Birchenhall, Chris (2004) Domestic and international influences on business cycle regimes in Europe. International Journal of Forecasting 20, 343–357.Google Scholar
Startz, Richard (2008) Binomial autoregressive moving average models with an application to U.S. recessions. Journal of Business and Economic Statistics 26, 1–8.Google Scholar
Stock, James H. and Watson, Mark W. (2003) Forecasting output and inflation: The role of asset prices. Journal of Economic Literature 41, 788–829.CrossRefGoogle Scholar