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The Econometric Analysis of Constructed Binary Time Series

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  • Don Harding
  • Adrian Pagan

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 microeconometric studies. One important di¤erence between primary and secondary binary variables is that while the former are, in many instances, independently distributed (i.d.) the later are rarely i.d. We show how popular rules for constructing binary states determine the degree and nature of the dependence in those states. When using constructed binary variables as regressands a common mistake is to ignore the dependence by using a probit model. We present an alternative non-parametric method that allows for dependence and apply that method to the issue of using the yield spread to predict recessions.

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Bibliographic Info

Paper provided by The University of Melbourne in its series Department of Economics - Working Papers Series with number 963.

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Length: 29 pages
Date of creation: 2006
Date of revision:
Handle: RePEc:mlb:wpaper:963

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Related research

Keywords: Business cycle; binary variable; Markov chain; probit model; yield curve;

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References

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Citations

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Cited by:
  1. Jes?s Crespo-Cuaresma & Octavio Fern?ndez-Amador, 2010. "Business cycle convergence in EMU: A first look at the second moment," Working Papers 2010-22, Faculty of Economics and Statistics, University of Innsbruck.
  2. T M Christensen & A S Hurn & K A Lindsay, 2008. "It never rains but it pours: Modelling the persistence of spikes in electricity prices," NCER Working Paper Series 25, National Centre for Econometric Research.
  3. Michael D. Bordo & Michael J. Dueker & David C. Wheelock, 2008. "Inflation, Monetary Policy and Stock Market Conditions," NBER Working Papers 14019, National Bureau of Economic Research, Inc.
  4. Michael D. Bordo & Michael J. Dueker & David C. Wheelock, 2009. "Inflation, monetary policy and stock market conditions: quantitative evidence from a hybrid latent-variable VAR," Working Papers 2008-012, Federal Reserve Bank of St. Louis.
  5. Kaufmann, Sylvia, 2008. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: A suggestion with an application to Austrian data," Working Papers 144, Oesterreichische Nationalbank (Austrian Central Bank).
  6. Harding, Don, 2008. "Detecting and forecasting business cycle turning points," MPRA Paper 33583, University Library of Munich, Germany.

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