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An Econometric Analysis of Some Models for Constructed Binary Time Series

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  • Don Harding
  • Adrian Pagan

Abstract

Macroeconometric and financial researchers often use binary data constructed in a way that creates serial dependence. We show that this dependence can be allowed for if the binary states are treated as Markov processes. In addition, the methods of construction ensure that certain sequences are never observed in the constructed data. Together these features make it difficult to utilize static and dynamic Probit models. We develop modeling methods that respect the Markov-process nature of constructed binary data and explicitly deals with censoring constraints. An application is provided that investigates the relation between the business cycle and the yield spread.

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  • Don Harding & Adrian Pagan, 2011. "An Econometric Analysis of Some Models for Constructed Binary Time Series," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 86-95, January.
    • Handle: RePEc:taf:jnlbes:v:29:y:2011:i:1:p:86-95
    DOI: 10.1198/jbes.2009.08005
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    Cited by:

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    2. Dungey, Mardi & Jacobs, Jan P.A.M. & Lestano,, 2015. "The internationalisation of financial crises: Banking and currency crises 1883–2008," The North American Journal of Economics and Finance, Elsevier, vol. 32(C), pages 29-47.
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    4. Beladi, Hamid & Chao, Chi Chur & Hu, May, 2016. "A macro-analysis of financial decisions: An examination of special dividend announcements," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 162-181.
    5. Yongsung Chang & Sunoong Hwang, 2015. "Asymmetric Phase Shifts in U.S. Industrial Production Cycles," The Review of Economics and Statistics, MIT Press, vol. 97(1), pages 116-133, March.
    6. Raffaele Mattera, 2023. "Forecasting binary outcomes in soccer," Annals of Operations Research, Springer, vol. 325(1), pages 115-134, June.
    7. Erik Kole & Dick Dijk, 2017. "How to Identify and Forecast Bull and Bear Markets?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 120-139, January.
    8. Candelon, Bertrand & Dumitrescu, Elena-Ivona & Hurlin, Christophe, 2014. "Currency crisis early warning systems: Why they should be dynamic," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1016-1029.
    9. Bertrand Candelon & Elena-Ivona Dumitrescu & Christophe Hurlin & Franz C. Palm, 2013. "Multivariate Dynamic Probit Models: An Application to Financial Crises Mutation," Advances in Econometrics, in: VAR Models in Macroeconomics – New Developments and Applications: Essays in Honor of Christopher A. Sims, volume 32, pages 395-427, Emerald Group Publishing Limited.
    10. Don Harding, 2010. "Applying shape and phase restrictions in generalized dynamic categorical models of the business cycle," Working Papers 2010.05, School of Economics, La Trobe University.
    11. Francis Bismans & Reynald Majetti, 2013. "Forecasting recessions using financial variables: the French case," Empirical Economics, Springer, vol. 44(2), pages 419-433, April.
    12. Park, Byeong U. & Simar, Léopold & Zelenyuk, Valentin, 2017. "Nonparametric estimation of dynamic discrete choice models for time series data," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 97-120.
    13. Meller, Barbara & Metiu, Norbert, 2015. "The synchronization of European credit cycles," Discussion Papers 20/2015, Deutsche Bundesbank.
    14. Hu, May & Chao, Chi-Chur & Malone, Chris & Young, Martin, 2017. "Real determinants of stock split announcements," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 574-598.
    15. Gunnar Bårdsen & Stan Hurn & Kenneth Lindsay, 2019. "Modelling and forecasting wind drought," Working Paper Series 18219, Department of Economics, Norwegian University of Science and Technology.
    16. Meller, Barbara & Metiu, Norbert, 2017. "The synchronization of credit cycles," Journal of Banking & Finance, Elsevier, vol. 82(C), pages 98-111.
    17. Gatfaoui, Jamel & Girardin, Eric, 2015. "Comovement of Chinese provincial business cycles," Economic Modelling, Elsevier, vol. 44(C), pages 294-306.
    18. Park, Byeong U. & Simar, Leopold & Zelenyuk, Valentin, 2013. "Non-Parametric Approach to Dynamic Time Series Discrete Choice Models," LIDAM Discussion Papers ISBA 2013052, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    19. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    20. Ms. Natasha X Che & Yoko Shinagawa, 2014. "Financial Soundness Indicators and the Characteristics of Financial Cycles," IMF Working Papers 2014/014, International Monetary Fund.
    21. Adrian Pagan, 2013. "Patterns and Their Uses," NCER Working Paper Series 96, National Centre for Econometric Research.
    22. Chan, Felix & Pauwels, Laurent L. & Wongsosaputro, Johnathan, 2013. "The impact of serial correlation on testing for structural change in binary choice model: Monte Carlo evidence," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 93(C), pages 175-189.
    23. Bertrand Candelon & Elena-Ivona DUMITRESCU & Christophe HURLIN & Franz C. PALM, 2011. "Modelling Financial Crises Mutation," LEO Working Papers / DR LEO 1238, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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