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How useful is yet another data-driven bandwidth in long-run variance estimation?: A simulation study on cointegrating regressions

  • Hirukawa, Masayuki
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    This paper investigates how bandwidth choice rules in long-run variance estimation affect finite-sample performance of efficient estimators for cointegrating regression models. Monte Carlo results indicate that Hirukawa's (2010) bandwidth choice rule contributes bias reduction in the estimators.

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    File URL: http://www.sciencedirect.com/science/article/B6V84-5259C11-4/2/7d44215ff6c755cfe396498dc290497f
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    Article provided by Elsevier in its journal Economics Letters.

    Volume (Year): 111 (2011)
    Issue (Month): 2 (May)
    Pages: 170-172

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    Handle: RePEc:eee:ecolet:v:111:y:2011:i:2:p:170-172
    Contact details of provider: Web page: http://www.elsevier.com/locate/ecolet

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    1. Park, Joon Y, 1992. "Canonical Cointegrating Regressions," Econometrica, Econometric Society, vol. 60(1), pages 119-43, January.
    2. Kurozumi, Eiji & Hayakawa, Kazuhiko, 2009. "Asymptotic properties of the efficient estimators for cointegrating regression models with serially dependent errors," Journal of Econometrics, Elsevier, vol. 149(2), pages 118-135, April.
    3. Hirukawa, Masayuki, 2010. "A Two-Stage Plug-In Bandwidth Selection And Its Implementation For Covariance Estimation," Econometric Theory, Cambridge University Press, vol. 26(03), pages 710-743, June.
    4. Newey, Whitney K & West, Kenneth D, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," Review of Economic Studies, Wiley Blackwell, vol. 61(4), pages 631-53, October.
    5. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-58, May.
    6. Phillips, Peter C B & Hansen, Bruce E, 1990. "Statistical Inference in Instrumental Variables Regression with I(1) Processes," Review of Economic Studies, Wiley Blackwell, vol. 57(1), pages 99-125, January.
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