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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 fi?nancial researchers often use secondary or constructed binary random variables that differ in terms of their sta- tistical 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 in- stances, 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 sta- tic 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 explicitly deals with any censoring constraints. An application is provided that investigates the relation between the business cycle and the yield spread.

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Bibliographic Info

Paper provided by Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University in its series CAMA Working Papers with number 2009-08.

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Length: 31 pages
Date of creation: Jan 2009
Date of revision:
Handle: RePEc:een:camaaa:2009-08

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  1. Michael Dueker, 1997. "Strengthening the case for the yield curve as a predictor of U.S. recessions," Review, Federal Reserve Bank of St. Louis, issue Mar, pages 41-51.
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Cited by:
  1. 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.
  2. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2013. "Interactions between Eurozone and US Booms and Busts: A Bayesian Panel Markov-switching VAR Model," Tinbergen Institute Discussion Papers 13-142/III, Tinbergen Institute.
  3. Makram El-Shagi & Gregor von Schweinitz, 2012. "Qual VAR Revisited: Good Forecast, Bad Story," IWH Discussion Papers 12, Halle Institute for Economic Research.
  4. 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.
  5. Kole, H.J.W.G. & van Dijk, D.J.C., 2013. "How to Identify and Forecast Bull and Bear Markets?," ERIM Report Series Research in Management ERS-2013-016-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
  6. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2013. "Interactions between eurozone and US booms and busts: A Bayesian panel Markov-switching VAR model," Working Paper 2013/20, Norges Bank.
  7. Francis Bismans & Reynald Majetti, 2013. "Forecasting recessions using financial variables: the French case," Empirical Economics, Springer, vol. 44(2), pages 419-433, April.
  8. Sunoong Hwang & Yongsung Chang, 2011. "Asymmetric Phase Shifts in U.S. Industrial Production Cycles," 2011 Meeting Papers 31, Society for Economic Dynamics.
  9. Kajal Lahiri & Liu Yang, 2012. "Forecasting Binary Outcomes," Discussion Papers 12-09, University at Albany, SUNY, Department of Economics.
  10. Yongsung Chang & Sunoong Hwang, 2011. "Asymmetric Phase Shifts in the U.S. Industrial Production Cycles," RCER Working Papers 564, University of Rochester - Center for Economic Research (RCER).

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