Abstract
The results of misspecification tests, based on Rao's score principle, are now routinely reported in applied econometric work. This paper draws together some important recent results which are designed to improve: (a) the robustness of standard score tests; and (b) the reliability of the asymptotic approximations used for inferential purposes. The discussion of robustness includes (i) parametric, (ii) distributional, and (iii) higher-order moment robustness. The issue of finite sample reliability focuses on controlling the size of the score test using (i) different variance estimators in conjunction with standard asymptotic theory, (ii) finite sample corrections obtainable from higher-order asymptotic analysis, and (iii) bootstrap procedures. (C) 2001 Elsevier Science B.V. All rights reserved.
| Original language | English |
|---|---|
| Pages (from-to) | 153-176 |
| Number of pages | 24 |
| Journal | Journal of Statistical Planning and Inference |
| Volume | 97 |
| Issue number | 1 |
| Publication status | Published - 1 Aug 2001 |
Keywords
- bootstrap
- finite sample corrections
- robustness
- score tests
- CONSISTENT COVARIANCE-MATRIX
- CONDITIONAL MOMENT TESTS
- LAGRANGE MULTIPLIER TEST
- MAXIMUM-LIKELIHOOD METHODS
- GENERALIZED LINEAR-MODELS
- SPECIFICATION TESTS
- HETEROSKEDASTICITY-CONSISTENT
- ARTIFICIAL REGRESSIONS
- TEST STATISTICS
- ASYMPTOTIC EXPANSIONS