The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.

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Econometric Analysis of Cross Section and Panel Data

This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum simulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. No other book incorporates all these fields, which have arisen in the past 20 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.

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Discrete Choice Methods with Simulation

This paper develops techniques for empirically analyzing demand and supply in differentiated products markets and then applies these techniques to analyze equilibrium in the U.S. automobile industry. Our primary goal is to present a framework which enables one to obtain estimates of demand and cost parameters for a class of oligopolistic differentiated products markets. These estimates can be obtained using only widely available product-level and aggregate consumer-level data, and they are consistent with a structural model of equilibrium in an oligopolistic industry. When we apply the tech- niques developed here to the U.S. automobile market, we obtain cost and demand parameters for (essentially) all models marketed over a twenty year period.

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Automobile prices in market equilibrium

Is the capital function distinct from the entrepreneurial function in modern economies? Or does a person have to be wealthy before he or she can start a business? Knight and Schumpeter held different views on the answer to this question. Our empirical findings side with Knight: Liquidity constraints bind, and a would-be entrepreneur must bear most of the risk inherent in his venture. The reasoning is roughly this: The data show that wealthier people are more inclined to become entrepreneurs. In principle, this could be so because the wealthy tend to make better entrepreneurs, but the data reject this explanation. Instead, the data point to liquidity constraints: capital is essential for starting a business, and liquidity constraints tend to exclude those with insufficient funds at their disposal.

https://www.edegan.com/pdfs/ERBC/Lusardi,%205.17.11/Evans_Jovanovic_1989.pdf

An Estimated Model of Entrepreneurial Choice under Liquidity Constraints

About 4.2 million men and women operate businesses on a full-time basis. Comprising more than a tenth of all workers, they run most of our nation’s firms and employ about a tenth of all wage workers. The fraction of the labor force that is self-employed has increased since the mid-1970s after a long period of decline.1 This paper examines the process of selection into self-employment over the life cycle and the determinants of self-employment earnings using data from the National Longitudinal Survey of Young Men (NLS) for 1966–1981 and the Current Population Surveys for 1968–1987.

Some Empirical Aspects of Entrepreneurship

This paper develops a new method for estimating the structural parameters of (discrete choice) dynamic programming problems. The method reduces the computational burden of estimating such models. We show the valuation functions characterizing the expected future utility associated with the choices often can be represented as an easily computed function of the state variables, structural parameters, and the probabilities of choosing alternative actions for states which are feasible in the future. Under certain conditions, nonparametric estimators of these probabilities can be formed from sample information on the relative frequencies of observed choices using observations with the same (or similar) state variables. Substituting the estimators for the true conditional choice probabilities in formulating optimal decision rules, we establish the consistency and asymptotic normality of the resulting structural parameter estimators. To illustrate our new method, we estimate a dynamic model of parental contraceptive choice and fertility using data from the National Fertility Survey.

Conditional Choice Probabilities and the Estimation of Dynamic Models

This paper presents a model of job matching the generalizes the existing literature by allowing for different jobs types, or occupations. Such differences affect the value of job-specific experience, inducing a career profile where certain types are sampled before others. More specifically, the analysis shows that it is optimal for the young and inexperienced to gravitate toward jobs exhibiting a certain kind of risk. Then, after deriving the equilibrium job turnover rate for an economy in which people do not switch occupations, panel data are used to estimate its underlying parameters. The hypothesis that people do not switch occupations is rejected against the alternative that they do, thus providing empirical support for the theoretical extension undertaken here.

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Job Matching and Occupational Choice

This paper examines US household fertility and female labor supply over the life cycle using data from the Panel Study of Income Dynamics. The authors investigate how maternal time inputs and market expenditures on offspring as well as the benefits they yield their parents vary with ages of offspring and influence female labor supply and contraceptive behavior. The econometric framework combines a female labor supply model and a contraceptive choice index function. It also accounts for the fact that conceptions are not perfectly controllable events. Using longitudinal data on married couples the authors estimate these equations and test alternative specifications of the technologies governing child care. The findings suggest that while parents cannot perfectly control conceptions variations in child care costs do affect life cycle spacing of births. Furthermore the results demonstrate the gains of modeling the linkages between female labor supply and fertility behavior at the household level. (authors modified)

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An Empirical Analysis of Life Cycle Fertility and Female Labor Supply.

We adapt the expectation–maximization algorithm to incorporate unobserved heterogeneity into conditional choice probability (CCP) estimators of dynamic discrete choice problems. The unobserved heterogeneity can be time-invariant or follow a Markov chain. By developing a class of problems where the difference in future value terms depends on a few conditional choice probabilities, we extend the class of dynamic optimization problems where CCP estimators provide a computationally cheap alternative to full solution methods. Monte Carlo results confirm that our algorithms perform quite well, both in terms of computational time and in the precision of the parameter estimates.

https://www.jstor.org/stable/pdfplus/41336537.pdf

Conditional choice probability estimation of dynamic discrete choice models with unobserved heterogeneity

This paper analyses a new estimator for the structural parameters of dynamic models of discrete choice. Based on an inversion theorem due to Hotz and Miller (1993), which establishes the existence of a one-to-one mapping between the conditional valuation functions for the dynamic problem and their associated conditional choice probabilities, we exploit simulation techniques to estimate models which do not possess terminal states. In this way our Conditional Choice Simulation (CCS) estimator complements the Conditional Choice Probability (CCP) estimator of Hotz and Miller (1993). Drawing on work in empirical process theory by Pakes and Pollard (1989), we establish its large sample properties, and then conduct a Monte Carlo study of Rust's (1987) model of bus engine replacement to compare its small sample properties with those of Maximum Likelihood (ML).

https://www.jstor.org/stable/pdfplus/2297981.pdf

Robert A. Miller

Papers

Conditional Choice Probabilities and the Estimation of Dynamic Models

Job Matching and Occupational Choice

An Empirical Analysis of Life Cycle Fertility and Female Labor Supply.

Conditional choice probability estimation of dynamic discrete choice models with unobserved heterogeneity

A Simulation Estimator for Dynamic Models of Discrete Choice