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Ten Things We Should Know About Time Series

Author

Listed:
  • Michael McAleer

    (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, and Institute of Economic Research, Kyoto University)

  • Les Oxley

    (Department of Economics and Finance, University of Canterbury)

Abstract

Time series data affect many aspects of our lives. This paper highlights ten things we should all know about time series, namely: a good working knowledge of econometrics and statistics, an awareness of measurement errors, testing for zero frequency, seasonal and periodic unit roots, analysing fractionally integrated and long memory processes, estimating VARFIMA models, using and interpreting cointegrating models carefully, choosing sensibly among univariate conditional, stochastic and realized volatility models, not confusing thresholds, asymmetry and leverage, not underestimating the complexity of multivariate volatility models, and thinking carefully about forecasting models and expertise.

Suggested Citation

  • Michael McAleer & Les Oxley, 2010. "Ten Things We Should Know About Time Series," KIER Working Papers 710, Kyoto University, Institute of Economic Research.
    • Handle: RePEc:kyo:wpaper:710
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    File URL: http://www.kier.kyoto-u.ac.jp/DP/DP710.pdf
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    Cited by:

    1. is not listed on IDEAS
    2. Fermanian, Jean-David & Malongo, Hassan, 2017. "On The Stationarity Of Dynamic Conditional Correlation Models," Econometric Theory, Cambridge University Press, vol. 33(3), pages 636-663, June.
    3. Michael McAleer, 2014. "Discussion of “Principal Volatility Component Analysis” by Yu-Pin Hu and Ruey Tsay," Tinbergen Institute Discussion Papers 14-025/III, Tinbergen Institute.

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    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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