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The Role of "Leads" in the Dynamic OLS Estimation of Cointegrating Regression Models

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  • Kazuhiko Hayakawa
  • Eiji Kurozumi

Abstract

In this paper, we consider the role of "leads" of the first difference of integrated variables in the dynamic OLS estimation of cointegrating regression models. We demonstrate that the role of leads is related to the concept of Granger causality and that in some cases leads are unnecessary in the dynamic OLS estimation of cointegrating regression models. Based on a Monte Carlo simulation, we find that the dynamic OLS estimator without leads substantially outperforms that with leads and lags; we therefore recommend testing for Granger noncausality before estimating models.

Suggested Citation

  • Kazuhiko Hayakawa & Eiji Kurozumi, 2006. "The Role of "Leads" in the Dynamic OLS Estimation of Cointegrating Regression Models," Hi-Stat Discussion Paper Series d06-194, Institute of Economic Research, Hitotsubashi University.
    • Handle: RePEc:hst:hstdps:d06-194
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    References listed on IDEAS

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    3. Worku, Urgaia R., 2020. "The Importance of Human Capital Resources for Sustainable Economic Growth in East African," Ethiopian Journal of Economics, Ethiopian Economics Association, vol. 29(01), April.
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    11. Urgaia, Worku R., 2018. "The Role of Human Capital Resources in East African Economies," GLO Discussion Paper Series 218, Global Labor Organization (GLO).
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    More about this item

    Keywords

    Cointegration; dynamic ordinary least squares estimator; Granger causality;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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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