Bootstrap Bartlett adjustment in seemingly unrelated regression

Journal of the American Statistical Association

Volumn 84, Issue 406, 1989, Pages 598-601

  Rocke, David M ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Likelihood ratio test; Maximum likelihood

Indexed keywords

EID: 84916541776     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1080/01621459.1989.10478809     Document Type: Article

References (26)

1. Avery, R. R. (1977), “Error Components and Seemingly Unrelated Regression,” Econometrica, 45, 199–209.
2. Baltagi, B. H. (1980), “On Seemingly Unrelated Regressions With Error Components,” Econometrics, 48, 1547–1551.
3. Barndorff-Nielson, O. E., and Cox, D. R. (1984), “Bartlett Adjustments to the Likelihood Ratio Statistic and the Distribution of the Maximum Likelihood Estimator,” Journal of the Royal Statistical Society, Ser. B, 46, 483–495.
4. Bartlett, M. S. (1937), “Properties of Sufficiency and Statistical Tests,” Proceedings of the Royal Society of London, Ser. A, 160, 268–282.
5. Bickel, P. J., and Freedman, D. A. (1981), “Some Asymptotic Theory for the Bootstrap,” The Annals of Statistics, 9, 1196–1217.
6. Binkley, J. K. (1982), “The Effect of Variable Correlation on the Efficiency of Seemingly Unrelated Regression in a Two-Equation Model,” Journal of the American Statistical Association, 77, 890–895.
7. Cox, D. R. (1984), “Effective Degrees of Freedom and the Likelihood Ratio Test,” Biometrika, 71, 487–493.
8. Cox, D. R., and Hinkley, D. V. (1974), Theoretical Statistics, London: Chapman & Hall.
9. Dennis, J. E., Jr., and Schnabel, R. B. (1983), Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Englewood Cliffs, NJ: Prentice-Hall.
10. Edlefsen, L. E., and Jones, S. D. (1986), GAUSS Programming Language, Kent, WA: Aptech Systems.
11. Efron, B. (1979), “Bootstrap Methods: Another Look at the Jackknife,” The Annals of Statistics, 7, 1–26.
12. —— (1982), The Jackknife, the Bootstrap, and Other Resampling Plans, Philadelphia: Society for Industrial and Applied Mathematics.
13. Evans, G. B. A., and Savin, N. E. (1982), “Conflict Among the Criteria Revisited: The W, LR, and LM Tests,” Econometrica, 50, 737–748.
14. Freedman, D. A. (1981), “Bootstrapping Regression Models,” The Annals of Statistics, 9, 1218–1228.
15. Freedman, D. A., and Peters, S. C. (1984a), “Bootstrapping a Regression Equation: Some Empirical Results,” Journal of the American Statistical Association, 79, 97–106.
16. —— (1984b), “Bootstrapping an Econometric Model: Some Empirical Results,” Journal of Business and Economic Statistics, 2, 150–158.
17. Gourieroux, C., Monfort, A., Renault, E., and Trognon, E. (1987), “Generalized Residuals,” Journal of Econometrics, 34, 5–32.
18. Kmenta, J., and Gilbert, R. F. (1968), “Small Sample Properties of Alternative Estimators of Seemingly Unrelated Regressions,” Journal of the American Statistical Association, 63, 1180–1200.
19. Peters, S. C., and Freedman, D. F. (1984), “Some Notes on the Bootstrap in Regression Problems,” Journal of Business and Economic Statistics, 2, 401–409.
20. Phillips, P. C. B. (1977), “An Approximation to the Finite Sample Distribution of Zellner’s Seemingly Unrelated Regression Estimator,” Journal of Econometrics, 6, 147–164.
21. —— (1985), “The Exact Distribution of the SUR Estimator,” Econometrica, 53, 745–756.
22. Rothenberg, T. J. (1984a), “Hypothesis Testing in Linear Models When the Error Covariance Matrix Is Nonscalar,” Econometrica, 52, 827–842.
23. —— (1984b), “Approximate Normality of Generalized Least Squares Estimates,” Econometrica, 52, 811–825.
24. Srivastava, V. K., and Dwivedi, T. D. (1979), “Estimation of Seemingly Unrelated Regression Equations: A Brief Survey,” Journal of Econometrics, 10, 15–32.
25. Telser, L. G. (1964), “Iterative Estimation of a Set of Linear Regression Equations,” Journal of the American Statistical Association, 59, 842–862.
26. Zellner, A. (1962), “An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias,” Journal of the American Statistical Association, 57, 348–368.