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Graphical Methods for Investigating the Size and Power of Hypothesis Tests

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  • Davidson, Russell
  • MacKinnon, James G

Abstract

Simple techniques for the graphical display of simulation evidence concerning the size and power of hypothesis tests are developed and illustrated. Three types of figures--called P value plots, P value discrepancy plots, and size-power curves--are discussed. Some Monte Carlo experiments on the properties of alternative forms of the information matrix test for linear regression models and probit models are used to illustrate these figures. Tests based on the outer-product-of-the-gradient regression generally perform much worse in terms of both size and power than efficient score tests. Copyright 1998 by Blackwell Publishers Ltd and The Victoria University of Manchester

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  • Davidson, Russell & MacKinnon, James G, 1998. "Graphical Methods for Investigating the Size and Power of Hypothesis Tests," The Manchester School of Economic & Social Studies, University of Manchester, vol. 66(1), pages 1-26, January.
    • Handle: RePEc:bla:manch2:v:66:y:1998:i:1:p:1-26
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    1. Davidson, Russell & MacKinnon, James G, 1984. "Model Specification Tests Based on Artificial Linear Regressions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 25(2), pages 485-502, June.
    2. Davidson, Russell & MacKinnon, James G., 1996. "The Size and Power of Bootstrap Tests," Queen's Economics Department Working Papers 273343, Queen's University - Department of Economics.
    3. Taylor, Larry W., 1987. "The size bias of White's information matrix test," Economics Letters, Elsevier, vol. 24(1), pages 63-67.
    4. Lancaster, Tony, 1984. "The Covariance Matrix of the Information Matrix Test," Econometrica, Econometric Society, vol. 52(4), pages 1051-1053, July.
    5. Horowitz, Joel L., 1994. "Bootstrap-based critical values for the information matrix test," Journal of Econometrics, Elsevier, vol. 61(2), pages 395-411, April.
    6. West, Kenneth D & Wilcox, David W, 1996. "A Comparison of Alternative Instrumental Variables Estimators of a Dynamic Linear Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 281-293, July.
    7. Chesher, Andrew, 1983. "The information matrix test : Simplified calculation via a score test interpretation," Economics Letters, Elsevier, vol. 13(1), pages 45-48.
    8. Hendry, David F., 1984. "Monte carlo experimentation in econometrics," Handbook of Econometrics,in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 16, pages 937-976 Elsevier.
    9. Davidson, Russell & MacKinnon, James G, 1992. "A New Form of the Information Matrix Test," Econometrica, Econometric Society, vol. 60(1), pages 145-157, January.
    10. Fischer, N. I. & Mammen, E. & Marron, J. S., 1994. "Testing for multimodality," Computational Statistics & Data Analysis, Elsevier, vol. 18(5), pages 499-512, December.
    11. Orme, Christopher, 1988. "The Calculation of the Information Matrix Test for Binary Data Models," The Manchester School of Economic & Social Studies, University of Manchester, vol. 56(4), pages 370-376, December.
    12. Alastair Hall, 1987. "The Information Matrix Test for the Linear Model," Review of Economic Studies, Oxford University Press, vol. 54(2), pages 257-263.
    13. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    14. Chesher, Andrew & Spady, Richard, 1991. "Asymptotic Expansions of the Information Matrix Test Statistic," Econometrica, Econometric Society, vol. 59(3), pages 787-815, May.
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