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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.

Suggested Citation

  • Don Harding & Adrian Pagan, 2006. "The Econometric Analysis of Constructed Binary Time Series," Department of Economics - Working Papers Series 963, The University of Melbourne.
    • Handle: RePEc:mlb:wpaper:963
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    File URL: http://www.economics.unimelb.edu.au/downloads/wpapers-06/963.pdf
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    References listed on IDEAS

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    Cited by:

    1. Bertrand Candelon & Elena-Ivona Dumitrescu & Christophe Hurlin, 2012. "How to Evaluate an Early-Warning System: Toward a Unified Statistical Framework for Assessing Financial Crises Forecasting Methods," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 60(1), pages 75-113, April.
    2. Harding, Don, 2008. "Detecting and forecasting business cycle turning points," MPRA Paper 33583, University Library of Munich, Germany.
    3. Timothy Christensen & Stan Hurn & Kenneth Lindsay, 2009. "It Never Rains but it Pours: Modeling the Persistence of Spikes in Electricity Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 25-48.
    4. Crespo-Cuaresma, Jesús & Fernández-Amador, Octavio, 2013. "Business cycle convergence in EMU: A first look at the second moment," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 265-284.
    5. Powell, John G. & Shi, Jing & Smith, Tom & Whaley, Robert E., 2009. "Political regimes, business cycles, seasonalities, and returns," Journal of Banking & Finance, Elsevier, vol. 33(6), pages 1112-1128, June.
    6. 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.
    7. Sylvia Kaufmann, 2010. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: a suggestion with an application to Austrian data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 309-344.
    8. 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.

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    More about this item

    Keywords

    Business cycle; binary variable; Markov chain; 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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