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Using turning point information to study economic dynamics

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  • Don Harding

Abstract

Procedures are developed to compute the proportion of turning points located in the sample path of time series data. It is shown that the proportion of turning points can be directly related to the data generating process. Methods for estimating model parameters are developed using counts of turning points. It is shown that the proposed method has the advantages of tractability and robustness. The later feature arises as it does not require that any of the moments of the series Y(t) exist. Tests of model specification are developed using these counts of turning points. These tests are applied to several models one including the issue of whether GDP is better modelled as trend stationary or difference stationary. Monte carlo results are presented for both the estimation and testing procedures

Suggested Citation

  • Don Harding, 2004. "Using turning point information to study economic dynamics," Econometric Society 2004 Australasian Meetings 214, Econometric Society.
  • Handle: RePEc:ecm:ausm04:214
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    File URL: http://repec.org/esAUSM04/up.10680.1077839458.pdf
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    References listed on IDEAS

    as
    1. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, September.
    2. Harding, Don & Pagan, Adrian, 2006. "Synchronization of cycles," Journal of Econometrics, Elsevier, vol. 132(1), pages 59-79, May.
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    Keywords

    Keywords: Business cycle; turning points; elliptically symmetric distribution;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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