Abstract
This paper examines the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference. The quasi-maximum likelihood estimator (QMLE) converges to a well defined limit, and may or may not be consistent for particular parameters of interest. Standard tests (Wald, Lagrange Multiplier, or Likelihood Ratio) are invalid in the presence of misspecification, but more general statistics are given which allow inferences to be drawn robustly. The properties of the QMLE and the information matrix are exploited to yield several useful tests for model misspecification.
Keywords
Maximum likelihoodEstimationEconometricsEconomicsStatisticsMathematics
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Publication Info
- Year
- 1983
- Type
- article
- Volume
- 51
- Issue
- 2
- Pages
- 513-513
- Citations
- 3751
- Access
- Closed
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Cite This
Halbert White
(1983).
Maximum Likelihood Estimation of Misspecified Models.
Econometrica
, 51
(2)
, 513-513.
https://doi.org/10.2307/1912004
Identifiers
- DOI
- 10.2307/1912004