Pravin K. Trivedi

Regression Analysis of Count Data: Author Index

Students in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits to a doctor, auto accidents, or new patents awarded. This book provides the most comprehensive and up-to-date account of models and methods to interpret such data. The authors have conducted research in the field for more than 25 years. In this book, they combine theory and practice to make sophisticated methods of analysis accessible to researchers and practitioners working with widely different types of data and software in areas such as applied statistics, econometrics, marketing, operations research, actuarial studies, demography, biostatistics, and quantitative social sciences. The book may be used as a reference work on count models or by students seeking an authoritative overview. Complementary material in the form of data sets, template programs, and bibliographic resources can be accessed on the Internet through the authors’ homepages. This second edition is an expanded and updated version of the first, with new empirical examples and more than two hundred new references added. The new material includes new theoretical topics, an updated and expanded treatment of cross-section models, coverage of bootstrap-based and simulation-based inference, expanded treatment of time series, multivariate and panel data, expanded treatment of endogenous regressors, coverage of quantile count regression, and a new chapter on Bayesian methods.

Regression Analysis of Count Data: List of Tables

Students in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits to a doctor, auto accidents, or new patents awarded. This book provides the most comprehensive and up-to-date account of models and methods to interpret such data. The authors have conducted research in the field for more than 25 years. In this book, they combine theory and practice to make sophisticated methods of analysis accessible to researchers and practitioners working with widely different types of data and software in areas such as applied statistics, econometrics, marketing, operations research, actuarial studies, demography, biostatistics, and quantitative social sciences. The book may be used as a reference work on count models or by students seeking an authoritative overview. Complementary material in the form of data sets, template programs, and bibliographic resources can be accessed on the Internet through the authors’ homepages. This second edition is an expanded and updated version of the first, with new empirical examples and more than two hundred new references added. The new material includes new theoretical topics, an updated and expanded treatment of cross-section models, coverage of bootstrap-based and simulation-based inference, expanded treatment of time series, multivariate and panel data, expanded treatment of endogenous regressors, coverage of quantile count regression, and a new chapter on Bayesian methods.

Regression-based tests for overdispersion in the Poisson model

Abstract A property of the Poisson regression model is mean-variance equality, conditional on explanatory variables. ‘Regression-based’ tests for this property are proposed in a very general setting. Unlike classical statistical tests, these tests require specification of only the mean-variance relationship under the alternative, rather than the complete distribution whose choice is usually arbitrary. The optimal regression-based test is easily computed as the t-test from an auxiliary regression. If a distribution under the alternative hypothesis is in fact specified and is in the Katz system of distributions or is Coxs local approximation to the Poisson, the score test for the Poisson distribution is equivalent to the optimal regression-based test.

Copula Modeling: An Introduction for Practitioners

This article explores the copula approach for econometric modeling of joint parametric distributions. Although theoretical foundations of copulas are complex, this paper demonstrates that practical implementation and estimation are relatively straightforward. An attractive feature of parametrically specified copulas is that estimation and inference are based on standard maximum likelihood procedures, and thus copulas can be estimated using desktop econometric software. This represents a substantial advantage of copulas over recently proposed simulation-based approaches to joint modeling.

A Microeconometric Model of the Demand for Health Care and Health Insurance in Australia

This paper develops a model for interdependent demand for health insurance and health care under uncertainty to throw light on the issue of insurance-induced distortions in the demand for health care services. The model is used to empirically analyse the determinants of the choice of health insurance type and seven types of health care services using micro-level data from the 1977–78 Australian Health Survey. Econometric implementation of the model involves, simultaneously, issues of discreteness of choice, selectivity and stochastic dependence between health insurance and utilization. Health status appears to be more important in determining health care service use than health insurance choice, while income appears to be more important in determining health insurance choice than in determining health care service use. For a broad range of health care services both moral hazard and self selection are found to be important determinants of utilization of health care services.

The structure of demand for health care: latent class versus two-part models

We contrast the two-part model (TPM) that distinguishes between users and non-users of health care, with a latent class model (LCM) that distinguishes between infrequent and frequent users. In model comparisons using data on counts of utilization from the RAND Health Insurance Experiment (RHIE), we find strong evidence in favor of the LCM. We show that individuals in the infrequent and frequent user latent classes may be described as being healthy and ill, respectively. Although sample averages of price elasticities, conditional means and event probabilities are not statistically different, the estimates of these policy-relevant measures are substantively different when calculated for hypothetical individuals with specific characteristics.

Excess Zeros in Count Models for Recreational Trips

This article develops a modeling approach for a count dataset for recreational boating trips that shows a frequency of zero counts significantly higher than that expected for Poisson-distributed data. We consider several parametric and semiparametric mixed and modified Poisson models as alternatives to the Poisson regression. The analysis suggests that the negative binomial hurdles model, which allows for overdispersion and also accommodates the presence of excess zeros, is the most satisfactory of all those considered

Specification and Simulated Likelihood Estimation of a Non-Normal Treatment-Outcome Model With Selection: Application to Health Care Utilization

We develop a specification and estimation framework for a class of nonlinear, non-normal microeconometric models of treatment and outcome with selection. A latent factor structure is used to accommodate selection into treatment and a simulated likelihood method is used for estimation. The methodology is applied to examine the causal effect of managed care, a multinomial discrete choice process, on the utilization of health care services, measured as binary indicators and counts. The results indicate that there are significant unobserved self-selection effects and these effects substantially change the estimated effects of insurance on utilization. Copyright Royal Economic Society 2006

The excess co-movement of commodity prices reconsidered

This paper provides an empirical reconsideration of evidence for excess co-movement of commodity prices within the framework of univariate and multivariate GARCH(1,1) models. Alternative formulations of zero excess co-movement are provided, and corresponding score and likelihood ratio tests are developed. Monthly time series data for two sample periods, 1960-85 and 1974-92, on up to nine commodities are used. In contrast to earlier work, only weak evidence of excess co-movement is found. Copyright 1996 by John Wiley & Sons, Ltd.

Using Trivariate Copulas to Model Sample Selection and Treatment Effects: Application to Family Health Care Demand

Simultaneous nonlinear econometric models with discrete outcomes are often difficult to implement. This article considers the use of the copula approach for a model with three jointly determined outcomes. It also deals with the discrete case in which outcomes include a mixture of dichotomous choices and discrete count data. We apply this technique to study self-selection and interdependence between health insurance and health care demand among married couples. The full model consists of a dichotomous choice equation for family insurance and a separate negative binomial equation for each spouses health care use.