Thirty years of heteroskedasticity-robust inference

Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis: Essays in Honor of Halbert L. White Jr

Volumn , Issue , 2013, Pages 437-461

  Mackinnon, James G ^a  

^a QUEEN S UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84876018307     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-1-4614-1653-1_17     Document Type: Chapter

References (49)

1. Andrews, D.W.K. (1991). “Heteroskedasticity and autocorrelation consistent covariance matrix estimation”, Econometrica, 59:817-858.
2. Andrews, D.W.K., and J. C. Monahan (1992). “An improved heteroskedasticity and autocorrelation consistent covariance matrix estimator”, Econometrica, 60, 953-966.
3. Cameron, A. C., J. B. Gelbach, and D. L. Miller (2008). “Bootstrap-based improvements for inference with clustered errors”, Review of Economics and Statistics, 90, 414-427.
4. Chesher, A. (1989). “Hajek inequalities, measures of leverage and the size of heteroskedasticity robust tests”, Journal of Econometrics, 57, 971-977.
5. Chesher, A., and G. Austin (1991). “The finite-sample distributions of heteroskedasticity robust Wald statistics”, Journal of Econometrics, 47, 153-173.
6. Chesher, A., and I. Jewitt (1987). “The bias of a heteroskedasticity consistent covariance matrix estimator”, Econometrica, 55, 1217-1222.
7. Cribaii-Neto, F. (2004). “Asymptotic inference under heteroskedasticity of unknown form”, Computational Statistics and Data Analysis, 45, 215-233.
8. Cribari-Neto, F., and M. G. A. Lima (2009). “Heteroskedasticity-consistent interval estimators”, Journal of Statistical Computation and Simulation, 79, 787-803.
9. Cribari-Neto, F., and M. G. A. Lima (2010). “Sequences of bias-adjusted covariance matrix estimators under heteroskedasticity of unknown form”, Annals of the Institute of Mathematical Statistics, 62, 1053-1082.
10. Cribari-Neto, F., T. C. Souza, and K. L. P. Vasconcellos (2007). “Inference under heteroskedasticity and leveraged data”, Communications in Statistics: Theory and Methods, 36, 1977-1988 [see also Erratum (2008), 37, 3329-3330.].
11. Davidson, R., andE. Flachaire (2008). “The wild bootstrap, tamed at last”, Journal of Econometrics, 146, 162-169.
12. Davidson, R. and J. G. MacKinnon (1993). Estimation and Inference in Econometrics, New York, Oxford University Press.
13. Davidson, R., and J. G. MacKinnon (1999). “The size distortion of bootstrap tests”, Econometric Theory, 15, 361-376.
14. Davidson, R., and J. G. MacKinnon (2000). “Bootstrap tests: How many bootstraps?” Econometric Reviews, 19, 55-68.
15. Davidson, R., and J. G. MacKinnon (2006). “The power of bootstrap and asymptotic tests”, Journal of Econometrics, 133, 421-441.
16. Davidson, R., and J. G. MacKinnon (2010). “Wild bootstrap tests for IV regression”, Journal of Business and Economic Statistics, 28, 128-144.
17. Davidson, R., and J. G. MacKinnon (2011). “Confidence sets based on inverting Anderson-Rubin tests”, Queen’s University, QED Working Paper No. 1257.
18. Driscoll, J. C., and A. C. Kraay (1998). “Consistent covariance matrix estimation with spatially dependent panel data”, Review of Economics and Statistics, 80, 549-560.
19. Efron, B. (1979). “Bootstrap methods: Another look at the jackknife”, Annals of Statistics, 7, 1-26.
20. Efron, B. (1982). The Jackknife, the Bootstrap and Other Resampling Plans. Philadelphia, Society for Industrial and Applied Mathematics.
21. Eicker, F. (1963). “Asymptotic normality and consistency of the least squares estimators for families of linear regressions”, Annals of Mathematical Statistics, 34, 447-456.
22. Eicker, F. (1967). “Limit theorems for regressions with unequal and dependent errors”, in Fifth Berkeley Symposium on Mathematical Statistics and Probability, ed. L. M. Le Cam and J. Ney- man, Berkeley, University of California Press, 1, 59-82.
23. Flachaire, E. (1999). “A better way to bootstrap pairs”, Economics Letters, 64, 257-262.
24. Flachaire, E. (2005). “Bootstrapping heteroskedastic regression models: Wild bootstrap vs pairs bootstrap”, Computational Statistics and Data Analysis, 49, 361-376.
25. Freedman, D. A. (1981). “Bootstrapping regression models”, Annals of Statistics, 9, 1218-1228.
26. Froot, K. A. (1989). “Consistent covariance matrix estimation with cross-sectional dependence and heteroskedasticity in financial data”, Journal of Financial and Quantitative Analysis, 24,333-355.
27. Furno, M. (1996). “Small sample behavior of a robust heteroskedasticity consistent covariance matrix estimator”, Journal of Statistical Computation and Simulation, 54, 115-128.
28. Hansen, L. P. (1982). “Large sample properties of generalized method of moments estimators”, Econometrica, 50, 1029-1054.
29. Hinkley, D.V. (1977). “Jackknifing in unbalanced situations”, Technometrics, 19, 285-292.
30. Horn, S. D., R. A. Horn, and D. B. Duncan (1975). “Estimating heteroskedastic variances in linear models”, Journal of the American Statistical Association, 70, 380-385.
31. Horowitz, J. L. (2001). “The bootstrap”, in Handbook of Econometrics, Vol. 5, ed. J. J. Heckman and E. E. Leamer. Amsterdam, North-Holland, 3159-3228.
32. Jockel, K.-H. (1986). “Finite sample properties and asymptotic efficiency of Monte Carlo tests”, Annals of Statistics, 14, 336-347.
33. Lancaster, T. (2006). “A note on bootstraps and robustness”, Brown University Working Paper 2006-6.
34. Liu, R.Y. (1988). “Bootstrap procedures under some non-I.I.D. models”, Annals of Statistics, 16, 1696-1708.
35. MacKinnon, J. G. (2002). “Bootstrap inference in econometrics”, Canadian Journal of Economics, 35,615-645.
36. MacKinnon, J. G., and H. White (1985). “Some heteroskedasticity consistent covariance matrix estimators with improved finite sample properties”, Journal of Econometrics, 29, 305-325.
37. Mammen, E. (1993). “Bootstrap and wild bootstrap for high dimensional linear models”, Annals of Statistics, 21, 255-285.
38. Newey, W.K., and K. D. West (1987). “A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix”, Econometrica, 55, 703-708.
39. Newey, W. K., and K. D. West (1994). “Automatic lag selection in covariance matrix estimation”, Review of Economic Studies, 61,631-653.
40. Paparoditis, E., and D. N. Politis (2005). “Bootstrap hypothesis testing in regression models”, Statistics and Probability Letters, 74, 356-365.
41. Poirier, D. J. (2010). “Bayesian interpretations of heteroskedastic consistent covariance estimators using the informed Bayesian bootstrap”, Econometric Reviews, 30, 457-468.
42. Politis, D.N. (2010). “Model-free model-fitting and predictive distributions”, Department of Economics, Univ. of California-San Diego.
43. Qian, L., and S. Wang (2001). “Bias-corrected heteroscedasticity robust covariance matrix (sandwich) estimators”, Journal of Statistical Computation and Simulation, 70, 161-174.
44. Racine, J. S., and J. G. MacKinnon (2007). “Simulation-based tests that can use any number of simulations”, Communications in Statistics: Simulation and Computation, 36, 357-365.
45. Rogers, W. H. (1993). “Regression standard errors in clustered samples”, STATA Technical Bulletin, 13, 19-23.
46. Stine, R. A. (1985). “Bootstrap prediction intervals for regression”, Journal of the American Statistical Association, 80, 1026-1031.
47. White, H. (1980). “A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity”, Econometrica, 48, 817-838.
48. White, H. (1982). “Maximum likelihood estimation of misspecified models”, Econometrica, 50, 1-25.
49. White, H., and I. Domowitz (1984). “Nonlinear regression with dependent observations”, Econometrica, 52, 143-161.