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

A finite sample correction for the variance of linear efficient two-step GMM estimators

This paper provides a model of firm and industry dynamics that allows for entry, exit, and firm-specific uncertainty generating variability in the fortunes of firms. It focuses on the impact of uncertainty arising from investment in research and exploration. It analyzes the behavior of individual firms in an evolving market place and derives optimal policies, including exit. Then it adds an entry process and aggregates the optimal behavior of all firms, including potential entrants, into a rational expectations Markov-perfect industry equilibrium and proves ergodicity of the equilibrium process. Numerical examples illustrate the detailed characteristics of the stochastic process generating industry structures.

Markov-Perfect Industry Dynamics: A Framework for Empirical Work

This paper proposes entropy balancing, a data preprocessing method to achieve covariate balance in observational studies with binary treatments. Entropy balancing relies on a maximum entropy reweighting scheme that calibrates unit weights so that the reweighted treatment and control group satisfy a potentially large set of prespecified balance conditions that incorporate information about known sample moments. Entropy balancing thereby exactly adjusts inequalities in representation with respect to the first, second, and possibly higher moments of the covariate distributions. These balance improvements can reduce model dependence for the subsequent estimation of treatment effects. The method assures that balance improves on all covariate moments included in the reweighting. It also obviates the need for continual balance checking and iterative searching over propensity score models that may stochastically balance the covariate moments. We demonstrate the use of entropy balancing with Monte Carlo simulations and empirical applications.

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Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies

We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is exogenous or unconfounded, that is, independent of the potential outcomes given covariates, biases associated with simple treatmentcontrol average comparisons can be removed by adjusting for differences in the covariates. Rosenbaum and Rubin (1983) show that adjusting solely for differences between treated and control units in the propensity score removes all biases associated with differences in covariates. Although adjusting for differences in the propensity score removes all the bias, this can come at the expense of efficiency, as shown by Hahn (1998), Heckman, Ichimura, and Todd (1998), and Robins, Mark, and Newey (1992). We show that weighting by the inverse of a nonparametric estimate of the propensity score, rather than the true propensity score, leads to an efficient estimate of the average treatment effect. We provide intuition for this result by showing that this estimator can be interpreted as an empirical likelihood estimator that efficiently incorporates the information about the propensity score.

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Efficient estimation of average treatment effects using the estimated propensity score

This paper proposes an estimator for discrete choice models that makes no assumption concerning the functional form of the choice probability function, where this function can be characterized by an index. The estimator is shown to be consistent, asymptotically normally distributed, and to achieve the semiparametric efficiency bound. Monte-Carlo evidence indicates that there may be only modest efficiency losses relative to maximum likelihood estimation when the distribution of the disturbances is known, and that the small-sample behavior of the estimator in other cases is good.

An efficient semiparametric estimator for binary response models

One-step efficient GMM estimation has been developed in the recent papers of Back and Brown (1990), Imbens (1993) and Qin and Lawless (1994). These papers emphasized methods that correspond to using Owen's (1988) method of empirical likelihood to reweight the data so that the reweighted sample obeys all the moment restrictions at the parameter estimates. In this paper we consider an alternative KLIC motivated weighting and show how it and similar discrete reweightings define a class of unconstrained optimization problems which includes GMM as a special case. Such KLIC-motivated reweightings introduce M auxiliary `tilting' parameters, where M is the number of moments; parameter and overidentification hypotheses can be recast in terms of these tilting parameters. Such tests, when appropriately conditioned on the estimates of the original parameters, are often startlingly more effective than their conventional counterparts. This is apparently due to the local ancillarity of the original parameters for the tilting parameters.

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Information Theoretic Approaches to Inference in Moment Condition Models

In industries where physical output varies with respect to attributes or qualities, it is important to take these differences into account in estimating cost functions. This paper presents a hedonic cost function that can be used to take output characteristics into account and applies it to the regulated trucking industry. It is found that failure to take output characteristics into account creates serious specification error and that inferences concerning economies of scale and factor demand differ substantially between the hedonic and nonhedonic formulation of the cost function.

Hedonic Cost Functions for the Regulated Trucking Industry

An attempt has been made to improve upon existing specifications and estimations of freight demand by treating transportation as an input in the production process and estimating the derived input demand equations for rail and trucking associated with a general translog cost function. This estimation method also recognizes that rates and shipment characteristics are jointly dependent and takes this inter dependence into account in estimating the demand functions. Two tentative conclusions emerge from the findings of this paper. First, at least with respect to less-than-truckload (LTL), rail and truck transportation are largely independent. Therefore, relatively little modal misallocation of resources between rail and LTL trucking should result from ICC policies that attempt to maintain the value of service pricing structure. Second, the estimated own price elasticities of demand for rail services were sufficiently high to indicate that the railroads might not benefit from blanket rate increases but could benefit instead from selective rate cutting. It is noted that the unique methodology employed and the empirical findings described in this paper need to be verified using different data and different time periods.

A derived demand function for freight transportation

Following the recent deregulation of the airlines, a chorus of politicians and consumers has been calling for the deregulation of other industries, especially those in the troubled transportation sector. By making use of a general equilibrium model, the authors of this study have evaluated the consequences of deregulation in the rail and trucking industries and, in particular, have quantified the efficiency and distributional effects of such a change.The book uses new and powerful econometric and analytical tools in its general equilibrium analysis, with explicit attention to the tradeoffs between the efficiency and distributional aspects of deregulation policies and the relationship between freight rates and regional incomes. The analysis recognizes the interdependence among the various rail and trucking modes and thus encompasses the full general-equilibrium effects of deregulation. In addition, by differentiating between short-run and long-run equilibrium, it takes changes in railroad infrastructure into account, balancing the huge costs of infrastructure maintenance against the savings of abandoning light-density routes. The book also tests a number of general hypotheses about the structure of cost and demand in the transport industries.The authors conclude that "with a few important caveats, the finding of this study indicate that the public interest would be served by deregulation. This is particularly true in the LTL [less-than-truckload] trucking market of general commodities where competition would appear to allocate resources efficiently and equitably and cause relatively few disruptions in the existing equilibrium."Although written from the viewpoint of the economist, transport and policy analysts will also find valuable and utilizable information in this volume.This book is the first in the series Regulation of Economic Activity, edited by Richard Schmalensee.

Richard H. Spady

Papers

An efficient semiparametric estimator for binary response models

Information Theoretic Approaches to Inference in Moment Condition Models

Hedonic Cost Functions for the Regulated Trucking Industry

A derived demand function for freight transportation

Freight Transport Regulation: Equity, Efficiency, and Competition in the Rail and Trucking Industries