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Using Bootstrapped Confidence Intervals for Improved Inferences with Seemingly Unrelated Regression Equations

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  • Rilstone, Paul
  • Veall, Michael

Abstract

The usual standard errors for the regression coefficients in a seemingly unrelated regression model have a substantial downward bias. Bootstrapping the standard errors does not seem to improve inferences. In this paper, Monte Carlo evidence is reported which indicates that bootstrapping can result in substantially better inferences when applied to t-ratios rather than to standard errors.

Suggested Citation

  • Rilstone, Paul & Veall, Michael, 1996. "Using Bootstrapped Confidence Intervals for Improved Inferences with Seemingly Unrelated Regression Equations," Econometric Theory, Cambridge University Press, vol. 12(3), pages 569-580, August.
    • Handle: RePEc:cup:etheor:v:12:y:1996:i:03:p:569-580_00
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    Cited by:

    1. Moundigbaye, Mantobaye & Messemer, Clarisse & Parks, Richard W. & Reed, W. Robert, 2020. "Bootstrap methods for inference in the Parks model," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-18.
    2. Dufour, Jean-Marie & Khalaf, Lynda, 2002. "Simulation based finite and large sample tests in multivariate regressions," Journal of Econometrics, Elsevier, vol. 111(2), pages 303-322, December.
    3. Dufour, Jean-Marie & Khalaf, Lynda, 2001. "Finite-Sample Simulation-Based Tests in Seemingly Unrelated Regressions," Cahiers de recherche 0111, Université Laval - Département d'économique.
    4. Bhattacharjee, Arnab & Jensen-Butler, Chris, 2013. "Estimation of the spatial weights matrix under structural constraints," Regional Science and Urban Economics, Elsevier, vol. 43(4), pages 617-634.
    5. Wanat, Stanisław & Papież, Monika & Śmiech, Sławomir, 2016. "Insurance Market Development and Economic Growth in Transition Countries: Some new evidence based on bootstrap panel Granger causality test," MPRA Paper 69051, University Library of Munich, Germany.
    6. Fraser, D.A.S. & Rekkas, M. & Wong, A., 2005. "Highly accurate likelihood analysis for the seemingly unrelated regression problem," Journal of Econometrics, Elsevier, vol. 127(1), pages 17-33, July.
    7. Gustavo J. Bobonis, 2009. "Is the Allocation of Resources within the Household Efficient? New Evidence from a Randomized Experiment," Journal of Political Economy, University of Chicago Press, vol. 117(3), pages 453-503, June.
    8. Kim, Jae H., 1999. "Asymptotic and bootstrap prediction regions for vector autoregression," International Journal of Forecasting, Elsevier, vol. 15(4), pages 393-403, October.
    9. Bergström, Pål, 1999. "Bootstrap Methods and Applications in Econometrics - A Brief Survey," Working Paper Series 1999:2, Uppsala University, Department of Economics.
    10. Śmiech, Sławomir & Papież, Monika, 2014. "Energy consumption and economic growth in the light of meeting the targets of energy policy in the EU: The bootstrap panel Granger causality approach," Energy Policy, Elsevier, vol. 71(C), pages 118-129.
    11. Monika Papiez & Slawomir Smiech, 2013. "Economic Growth and Energy Consumption in Post-Communist Countries: a Bootstrap Panel Granger Causality Analysis," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 13, pages 51-68.

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