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Sieve bootstrap t-tests on long-run average parameters

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  • Fuertes, Ana-Maria

Abstract

Panel estimators can provide consistent measures of a long-run average parameter even if the individual regressions are spurious. However, the t-test on this parameter is fraught with problems because the limit distribution of the test statistic is non-standard and rather complicated, particularly in panels with mixed (non-)stationary errors. A sieve bootstrap framework is suggested to approximate the distribution of the t-statistic. An extensive Monte Carlo study demonstrates that the bootstrap is quite useful in this context.

Suggested Citation

  • Fuertes, Ana-Maria, 2008. "Sieve bootstrap t-tests on long-run average parameters," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3354-3370, March.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:7:p:3354-3370
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    Cited by:

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    2. Trapani, Lorenzo, 2021. "Inferential theory for heterogeneity and cointegration in large panels," Journal of Econometrics, Elsevier, vol. 220(2), pages 474-503.
    3. Stephan Smeekes & Jean-Pierre Urbain, 2014. "On the Applicability of the Sieve Bootstrap in Time Series Panels," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 139-151, February.
    4. Urbain, J.R.Y.J. & Westerlund, J., 2008. "Spurious Regression in Nonstationary Panels time Series with Cross-Member Cointegration," Research Memorandum 044, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    5. Di Iorio, Francesca & Fachin, Stefano, 2012. "A note on the estimation of long-run relationships in panel equations with cross-section linkages," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 6, pages 1-18.
    6. Trapani, Lorenzo, 2012. "On the asymptotic t-test for large nonstationary panel models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3286-3306.

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