The Uses of Tobit Analysis
TL;DR: The authors showed that the coefficients obtained from using Tobit-here called "beta" coefficients -provide more information than is commonly realized and showed that this decomposition can be quantified in rather useful and insightful ways.
Abstract: In this paper authors point out that the coefficients obtained from using Tobit-here called "beta" coefficients - provide more information than is commonly realized. In particular, authors show that Tobit can be used to determine both changes in the probability of being above the limit and changes in the value of the dependent variable if it is already above the limit$ and authors show that this decomposition can be quantified in rather useful and insightful ways.
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Book•
01 Jan 2001
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).
Abstract: 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.
28,298 citations
TL;DR: In this article, a manager's motivation for a conglomerate merger is investigated. And the authors show that managers engage in conglomerate mergers to decrease their largely undiversifiable "employment risk" (i.e., risk of losing job, professional reputation, etc.).
Abstract: A conglomerate merger generally leads, through the diversification effect, to reduced risk for the combined entity. As is well known, in perfect capital markets such risk reduction will not be beneficial to stockholders, since they can achieve on their own the preferred degree of risk in their "homemade" portfolios. What, then, is the motive for the widespread and persisting phenomenon of conglomerate mergers? In this study a "managerial" motive for conglomerate merger is advanced and tested. Specifically, managers, as opposed to investors, are hypothesized to engage in conglomerate mergers to decrease their largely undiversifiable "employment risk" (i.e., risk of losing job, professional reputation, etc.). Such risk-reduction activities are considered here as managerial perquisites in the context of the agency cost model. This hypothesis about conglomerate merger motivation is empirically examined in two different tests and found to be consistent with the data.
2,876 citations
Book•
15 Dec 2008
TL;DR: The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes as mentioned in this paper.
Abstract: The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes. In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages? Mostly Harmless Econometrics shows how the basic tools of applied econometrics allow the data to speak.In addition to econometric essentials, Mostly Harmless Econometrics covers important new extensions--regression-discontinuity designs and quantile regression--as well as how to get standard errors right. Joshua Angrist and Jorn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science.
An irreverent review of econometric essentials
A focus on tools that applied researchers use most
Chapters on regression-discontinuity designs, quantile regression, and standard errors
Many empirical examples
A clear and concise resource with wide applications
2,379 citations
TL;DR: In this article, the authors investigated the dependence of transport costs on geography and infrastructure and found that poor infrastructure is an important determinant of transportation costs, especially for landlocked countries.
Abstract: The authors use different data sets to investigate the dependence of transport costs on geography and infrastructure. Infrastructure is an important determinant of transport costs, especially for landlocked countries. Analysis of bilateral trade data confirms the importance of infrastructure and gives an estimate of the elasticity of trade flows with respect to the trade cost factor of around-3. A deterioration of infrastructure from the median to the 75th percentile raises transport costs by 12 percentage points and reduces trade volumes by 28 percent. Analysis of African trade flows indicates that their relatively low level is largely due to poor infrastructure.
1,707 citations
TL;DR: This paper examined a new database that details corporate risk management activity in the North American gold mining industry and found little empirical support for the predictive power of theories that view risk management as a means to maximize shareholder value.
Abstract: This article examines a new database that details corporate risk management activity in the North American gold mining industry. I find little empirical support for the predictive power of theories that view risk management as a means to maximize shareholder value. However, firms whose managers hold more options manage less gold price risk, and firms whose managers hold more stock manage more gold price risk, suggesting that managerial risk aversion may affect corporate risk management policy. Further, risk management is negatively associated with the tenure of firms' CFOs, perhaps reflecting managerial interests, skills, or preferences.
1,507 citations
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1,055 citations
Posted Content•
232 citations
TL;DR: In this paper, the authors present some econometric evidence on the effects of taxes on married women, a group of growing importance in the American labor force, and present a testable model of labor supply which permits statistical estimation of a "coefficient of tax perception." Unlike previous models of labour supply, it allows for the possibility that the wage may depend on the number of hours worked.
Abstract: PAYROLL AND PROGRESSIVE INCOME taxes play an enormous role in the American fiscal system. It is therefore of some importance to know the extent to which they influence work incentives. The purpose of this study is to present some econometric evidence on the effects of taxes on married women, a group of growing importance in the American labor force.2 A testable model of labor supply is developed which permits statistical estimation of a "coefficient of tax perception." Unlike previous models of labor supply, it allows for the possibility that the wage may depend on the number of hours worked. Contrary to much of the literature, the results of this paper strongly suggest that marginal tax rates do have an important impact on labor force behavior. This section reviews briefly the past thought on this problem. Section 2 develops a model to explain work decisions when an individual faces a whole set of wagehour combinations, rather than a given wage independent of the number of hours he works. In Section 3 this model is modified to permit an explicit test of whether or not taxes affect individuals' labor supply decisions. Estimation problems are discussed at length, and the empirical results are presented. A concluding section contains a summary and suggestions for future research.
232 citations
TL;DR: In this article, the limited dependent variable technique is extended to provide for cases in which the dependent variable in a regression is subject to both an upper and a lower limit, and a surprising property of the statistical model emerges.
Abstract: Some economic variables are restricted by an upper and lower limit but are continuous between the two limits. Measurements of such variables are sometimes available in their natural form and sometimes only in the form of three categories where information concerning the middle category is suppressed (unemployed, employed part time, employed full time, for example). Where such a variable is a continuous function of other variables between the two limits, the function can be estimated from data of either sort provided the function and the distribution of errors can be specified. WHEN THE LIMITED dependent variable technique developed by Tobin [3] is extended to provide for cases in which the dependent variable in a regression is subject to both an upper limit and a lower limit, a surprising property of the statistical model emerges.1 Estimates of the regression function can be obtained whether or not the exact values of the dependent variable are known for the nonlimit cases. Provided the functional form can be specified correctly, classification of the dependent variable into upper limit, lower limit, and non-limit observations provides enough information, along with observed values of the independent
195 citations