Maximum Likelihood Estimation of Misspecified Models

  title={Maximum Likelihood Estimation of Misspecified Models},
  author={Halbert L. White},
  • H. White
  • Published 1982
  • Mathematics, Economics
  • Econometrica
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… 
Maximum likelihood estimation in misspecified generalized linear models
Summary, This paper deals with the asymptotic behaviour of the (quasi-)maximum likelihood estimator in misspecified generalized linear models. Misspecification may be due to incorrect densities,
Minimum ϕ-divergence estimation in misspecified multinomial models
Pseudo maximum likelihood estimation and a test for misspecification in mean and covariance structure models
Using the theory of pseudo maximum likelihood estimation the asymptotic covariance matrix of maximum likelihood estimates for mean and covariance structure models is given for the case where the
In this paper, the asymptotic distribution of corrected maximum likelihood estimators is formally derived when the misspecification present is local, or small, but unknown. Although these corrections
Maximum Likelihood Inference in Weakly Identified DSGE Models
This paper examines the problem of weak identification in maximum likelihood, motivated by problems with estimation and inference a multi-dimensional, non-linear DSGE model. We suggest a test for a
Asymptotic likelihood inference for nonhomogeneous observations
This paper surveys asymptotic theory of maximum likelihood estimation for not identically distributed, possibly dependent observations. Main results on consistency, asymptotic normality and
Robustness properties of marginal composite likelihood estimators
Composite likelihoods are a class of alternatives to the full likelihood which are widely used in many situations in which the likelihood itself is intractable. A composite likelihood may be computed


Specification tests in econometrics
Using the result that under the null hypothesis of no misspecification an asymptotically efficient estimator must have zero asymptotic covariance with its difference from a consistent but
The behavior of maximum likelihood estimates under nonstandard conditions
This paper proves consistency and asymptotic normality of maximum likelihood (ML) estimators under weaker conditions than usual. In particular, (i) it is not assumed that the true distribution
A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
This paper presents a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic. This estimator does not depend on a formal
This paper sets out a general criterion for the identifiability of a statistical system, based on Kullback's information integral. It is shown that the general identification problem is equivalent to
Omnibus test contours for departures from normality based on √b1 and b2
SUMMARY The test statistic X2(1bbl)+X2(b2), where X(1bl) and X(b2) are standardized normal equivalents to the sample skewness, Jbl, and kurtosis, b2, statistics, is considered in normal sampling.
Linear statistical inference and its applications
Algebra of Vectors and Matrices. Probability Theory, Tools and Techniques. Continuous Probability Models. The Theory of Least Squares and Analysis of Variance. Criteria and Methods of Estimation.
A nonparametric estimation of the entropy for absolutely continuous distributions (Corresp.)
  • I. Ahmad, P. Lin
  • Mathematics, Computer Science
    IEEE Trans. Inf. Theory
  • 1976
This correspondence proposes, based on a random sample X_{1, \cdots, X_{n} generated from F, a nonparametric estimate of H(f) given by -(l/n) \sum_{i = 1}^{n} \In \hat{f}(x) , where f is the kernel estimate of f due to Rosenblatt and Parzen.
On the Mathematical Foundations of Theoretical Statistics
Centre of Location. That abscissa of a frequency curve for which the sampling errors of optimum location are uncorrelated with those of optimum scaling. (9.)