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Habit, aggregation and long memory: evidence from television audience data

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  • D Byers
  • D Peel
  • D A Thomas

Abstract

Many economic outcomes appear to be influenced by habit or commitment, giving rise to persistence. In cases where the decision is binary and persistent, the aggregation of individual time series can result in a fractionally integrated process for the aggregate data. Certain television programmes appear to engender commitment on the part of viewers and the decision to watch or not is clearly binary. We report an empirical analysis of television audience data and show that these series can be modelled as I(d) processes. We also investigate the proposition that temporal aggregation of a fractionally-integrated series leaves the value of d unchanged.

Suggested Citation

  • D Byers & D Peel & D A Thomas, 2005. "Habit, aggregation and long memory: evidence from television audience data," Working Papers 567397, Lancaster University Management School, Economics Department.
  • Handle: RePEc:lan:wpaper:567397
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    References listed on IDEAS

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    1. Granger, Clive W. J. & Terasvirta, Timo, 1999. "A simple nonlinear time series model with misleading linear properties," Economics Letters, Elsevier, vol. 62(2), pages 161-165, February.
    2. David Byers & James Davidson & David Peel, 1997. "Modelling Political Popularity: an Analysis of Long‐range Dependence in Opinion Poll Series," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 471-490, September.
    3. Becker, Gary S & Grossman, Michael & Murphy, Kevin M, 1994. "An Empirical Analysis of Cigarette Addiction," American Economic Review, American Economic Association, vol. 84(3), pages 396-418, June.
    4. Chambers, Marcus J, 1998. "Long Memory and Aggregation in Macroeconomic Time Series," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 1053-1072, November.
    5. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    6. David Byers & James Davidson & David Peel, 2002. "Modelling political popularity: a correction," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(1), pages 187-189, February.
    7. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
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    Cited by:

    1. McHale, I.G. & Peel, D.A., 2010. "Habit and long memory in UK lottery sales," Economics Letters, Elsevier, vol. 109(1), pages 7-10, October.

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