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Journal ArticleDOI

Econometric models based on count data. Comparisons and applications of some estimators and tests

TLDR
In this article, the authors deal with specification, estimation and tests of single equation reduced form type equations in which the dependent variable takes only non-negative integer values, and provide a detailed application of the estimators and tests to a model of the number of doctor consultations.
Abstract
This paper deals with specification, estimation and tests of single equation reduced form type equations in which the dependent variable takes only non-negative integer values. Beginning with Poisson and compound Poisson models, which involve strong assumptions, a variety of possible stochastic models and their implications are discussed. A number of estimators and their properties are considered in the light of uncertainty about the data generation process. The paper also considers the role of tests in sequential revision of the model specification beginr ing with the Poisson case and provides a detailed application of the estimators and tests to a model of the number of doctor consultations.

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Citations
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Book

Econometric Analysis of Cross Section and Panel Data

TL;DR: This is the essential companion to Jeffrey Wooldridge's widely-used graduate text Econometric Analysis of Cross Section and Panel Data (MIT Press, 2001).
Book

Negative Binomial Regression

TL;DR: In this article, the authors introduce the concept of risk in count response models and assess the performance of count models, including Poisson regression, negative binomial regression, and truncated count models.
Journal ArticleDOI

Analyzing developmental trajectories: A semiparametric, group-based approach

TL;DR: Agroup-based method for identifying distinctive groups of individual trajectories within the population and for profiling the characteristics of group members is demonstrated.
Journal ArticleDOI

Recombinant Uncertainty in Technological Search

TL;DR: It is proposed that purely technological uncertainty derives from inventors' search processes with unfamiliar components and component combinations, which leads to less useful inventions on average and implies an increase in the variability that can result in both failure and breakthrough.
References
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Journal ArticleDOI

A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity

Halbert White
- 01 May 1980 - 
TL;DR: In this article, a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic is presented, which does not depend on a formal model of the structure of the heteroSkewedness.
Journal ArticleDOI

Generalized Linear Models

TL;DR: In this paper, the authors used iterative weighted linear regression to obtain maximum likelihood estimates of the parameters with observations distributed according to some exponential family and systematic effects that can be made linear by a suitable transformation.

The behavior of maximum likelihood estimates under nonstandard conditions

TL;DR: In this paper, the authors prove consistency and asymptotic normality of maximum likelihood estimators under weaker conditions than usual, such that the true distribution underlying the observations belongs to the parametric family defining the estimator, and the regularity conditions do not involve the second and higher derivatives of the likelihood function.
Journal ArticleDOI

Maximum likelihood estimation of misspecified models

Halbert White
- 01 Jan 1982 - 
TL;DR: In this article, the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference are examined, and the properties of the quasi-maximum likelihood estimator and the information matrix are exploited to yield several useful tests.
Book

Statistical Models and Methods for Lifetime Data

TL;DR: Inference procedures for Log-Location-Scale Distributions as discussed by the authors have been used for estimating likelihood and estimating function methods. But they have not yet been applied to the estimation of likelihood.