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

Author

Listed:
  • 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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    2. Kaddour Hadri & Eiji Kurozumi & Yao Rao, 2015. "Novel panel cointegration tests emending for cross‐section dependence with N fixed," Econometrics Journal, Royal Economic Society, vol. 18(3), pages 363-411, October.
    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.
    4. Akama, Erick, 2016. "International tourism receipts and economic growth in Kenya 1980 -2013," MPRA Paper 78110, University Library of Munich, Germany.
    5. Adom, Philip Kofi, 2017. "The long-run price sensitivity dynamics of industrial and residential electricity demand: The impact of deregulating electricity prices," Energy Economics, Elsevier, vol. 62(C), pages 43-60.
    6. Risso, W. Adrián & Punzo, Lionello F. & Carrera, Edgar J. Sánchez, 2013. "Economic growth and income distribution in Mexico: A cointegration exercise," Economic Modelling, Elsevier, vol. 35(C), pages 708-714.
    7. Mayer, Alexander, 2020. "(Consistently) testing strict exogeneity against the alternative of predeterminedness in linear time-series models," Economics Letters, Elsevier, vol. 193(C).
    8. Scheiblecker, Marcus, 2013. "Between cointegration and multicointegration: Modelling time series dynamics by cumulative error correction models," Economic Modelling, Elsevier, vol. 31(C), pages 511-517.
    9. James J. Forest & Paul Turner, 2013. "Alternative estimators of cointegrating parameters in models with nonstationary data: an application to US export demand," Applied Economics, Taylor & Francis Journals, vol. 45(5), pages 629-636, February.
    10. Eiji Kurozumi & Anton Skrobotov, 2018. "Confidence Sets for the Break Date in Cointegrating Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(3), pages 514-535, June.
    11. Urgaia, Worku R., 2018. "The Role of Human Capital Resources in East African Economies," GLO Discussion Paper Series 218, Global Labor Organization (GLO).
    12. Hasanov, Fakhri J. & Bulut, Cihan & Suleymanov, Elchin, 2016. "Do population age groups matter in the energy use of the oil-exporting countries?," Economic Modelling, Elsevier, vol. 54(C), pages 82-99.
    13. Duc Hong Vo & Thang Cong Nguyen & Ngoc Phu Tran & Anh The Vo, 2019. "What Factors Affect Income Inequality and Economic Growth in Middle-Income Countries?," JRFM, MDPI, vol. 12(1), pages 1-12, March.
    14. Christos Pierros, 2020. "A Labor Market-Augmented Empirical Stock-Flow Consistent Model Applied to the Greek Economy," Economics Working Paper Archive wp_949, Levy Economics Institute.
    15. Wan-Jiun Chen, 2022. "Toward Sustainability: Dynamics of Total Carbon Dioxide Emissions, Aggregate Income, Non-Renewable Energy, and Renewable Power," Sustainability, MDPI, vol. 14(5), pages 1-27, February.
    16. Adom, Philip Kofi & Amuakwa-Mensah, Franklin & Agradi, Mawunyo Prosper & Nsabimana, Aimable, 2021. "Energy poverty, development outcomes, and transition to green energy," Renewable Energy, Elsevier, vol. 178(C), pages 1337-1352.

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    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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