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An Econometric Analysis of Some Models for Constructed Binary Time Series

Author

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  • Don Harding

    (La Trobe University)

  • Adrian Pagan

    (QUT)

Abstract

Macroeconometric and financial researchers often use secondary or constructed binary random variables that differ in terms of their statistical properties from the primary random variables used in micro-econometric studies. One important difference between primary and secondary binary variables is that, while the former are, in many instances, independently distributed (i.d.), the latter are rarely i.d. We show how popular rules for constructing the binary states interact with the stochastic processes for of the variables they are constructed from, so that the binary states need to be treated as Markov processes. Consequently, one needs to recognize this when performing analyses with the binary variables, and it is not valid to adopt a model like static Probit which fails to recognize such dependence. Moreover, these binary variables are often censored, in that they are constructed in such a way as to result in sequences of them possessing the same sign. Such censoring imposes restrictions upon the DGP of the binary states and it creates difficulties if one tries to utilize a dynamic Probit model with them. Given this we describe methods for modeling with these variables that both respects their Markov process nature and which explicitly deals with any censoring constraints. An application is provided that investigates the relation between the business cycle and the yield spread.

Suggested Citation

  • Don Harding & Adrian Pagan, 2009. "An Econometric Analysis of Some Models for Constructed Binary Time Series," NCER Working Paper Series 39, National Centre for Econometric Research, revised 02 Jul 2009.
  • Handle: RePEc:qut:auncer:2009_39
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    2. Makram El-Shagi & Gregor Von Schweinitz, 2016. "Qual Var Revisited: Good Forecast, Bad Story," Journal of Applied Economics, Taylor & Francis Journals, vol. 19(2), pages 293-321, November.
    3. 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.
    4. 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.
    5. Meller, Barbara & Metiu, Norbert, 2015. "The synchronization of European credit cycles," Discussion Papers 20/2015, Deutsche Bundesbank.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Meller, Barbara & Metiu, Norbert, 2017. "The synchronization of credit cycles," Journal of Banking & Finance, Elsevier, vol. 82(C), pages 98-111.
    11. 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.
    12. Ms. Natasha X Che & Yoko Shinagawa, 2014. "Financial Soundness Indicators and the Characteristics of Financial Cycles," IMF Working Papers 2014/014, International Monetary Fund.
    13. 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.
    14. 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.
    15. 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.
    16. Francis Bismans & Reynald Majetti, 2013. "Forecasting recessions using financial variables: the French case," Empirical Economics, Springer, vol. 44(2), pages 419-433, April.
    17. Elena-Ivona Dumitrescu & Bertrand Candelon & Christophe Hurlin & Franz C. Palm, 2012. "Multivariate Dynamic Probit Models: An Application to Financial Crises Mutation," Working Papers halshs-00630036, HAL.
    18. 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.
    19. Gatfaoui, Jamel & Girardin, Eric, 2015. "Comovement of Chinese provincial business cycles," Economic Modelling, Elsevier, vol. 44(C), pages 294-306.
    20. 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).
    21. 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.

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    More about this item

    Keywords

    Business cycle; binary variable; Markov process; Probit model; yield curve;
    All these keywords.

    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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