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Asymmetric Adjustment and Bias in Estimation of an Equilibrium Relationship from a Cointegrating Regression

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  • Sean Holly
  • Paul Turner
  • Melvyn Weeks

Abstract

This paper uses Monte Carlo methods to investigate the effects of asymmetricadjustment on estimates of the parameters of the equilibrium relationshipbetween a set of variables. We demonstrate that simple least squares estimatesand the implicit estimates from a symmetric error correction model both leadto biases in the constant term. This bias increases with the size of theasymmetry and shows no tendency to decline with the sample size. We also showthat if the biased estimates of the equilibrium relationship are then used todevide the sample into different regimes to test for assymmetric adjustment,then the resulting test has low power. The power of tests for asymmetry canbe increased significantly by using simultaneous estimation of the parametersof the equilibrium relationship and the asymmetric adjustment process. Copyright Kluwer Academic Publishers 2003

Suggested Citation

  • Sean Holly & Paul Turner & Melvyn Weeks, 2003. "Asymmetric Adjustment and Bias in Estimation of an Equilibrium Relationship from a Cointegrating Regression," Computational Economics, Springer;Society for Computational Economics, vol. 21(3), pages 195-202, June.
    • Handle: RePEc:kap:compec:v:21:y:2003:i:3:p:195-202
    DOI: 10.1023/A:1023954024420
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    References listed on IDEAS

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    1. Escribano, Alvaro & Pfann, Gerard A., 1998. "Non-linear error correction, asymmetric adjustment and cointegration," Economic Modelling, Elsevier, vol. 15(2), pages 197-216, April.
    2. Sean Holly & Paul Turner, 2001. "Asymmetric Adjustment Costs, Asymmetric Pricing and Employment: Evidence from the UK," Scottish Journal of Political Economy, Scottish Economic Society, vol. 48(1), pages 69-81, February.
    3. Cook, Steven & Holly, Sean & Turner, Paul, 1999. "The power of tests for non-linearity: the case of Granger-Lee asymmetry," Economics Letters, Elsevier, vol. 62(2), pages 155-159, February.
    4. Altuğ, Sumru & Ashley, Richard A. & Patterson, Douglas M., 1999. "Are Technology Shocks Nonlinear?," Macroeconomic Dynamics, Cambridge University Press, vol. 3(4), pages 506-533, December.
    5. Palm, Franz C. & Pfann, Gerard A., 1998. "Sources of asymmetry in production factor dynamics," Journal of Econometrics, Elsevier, vol. 82(2), pages 361-392, February.
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    Cited by:

    1. Cook, Steven & Fosten, Jack, 2019. "Replicating rockets and feathers," Energy Economics, Elsevier, vol. 82(C), pages 139-151.
    2. Afshin Honarvar, 2010. "Modeling of Asymmetry between Gasoline and Crude Oil Prices: A Monte Carlo Comparison," Computational Economics, Springer;Society for Computational Economics, vol. 36(3), pages 237-262, October.
    3. repec:asi:ajoerj:2013:p:420-432 is not listed on IDEAS
    4. Henry de-Graft Acquah, 2013. "The Role of Model Complexity and the Performance of the Selection Criteria in Asymmetric Price Transmission Models," Journal of Economics and Behavioral Studies, AMH International, vol. 5(3), pages 157-163.
    5. Ming-Hua Liu & Tianyun Liu & Keshab Shrestha & Yang Zhang, 2021. "The impact of financial regulation on the stickiness of credit card lending rate: evidence from the USA," Review of Quantitative Finance and Accounting, Springer, vol. 57(4), pages 1195-1213, November.
    6. Acquah, H. de-Graft, 2013. "On the Comparison of Akaike Information Criterion and Consistent Akaike Information Criterion in Selection of an Asymmetric Price Relationship: Bootstrap Simulation Results," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 5(1), pages 1-7, March.

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