Topic
Tobit model
About: Tobit model is a research topic. Over the lifetime, 1918 publications have been published within this topic receiving 51399 citations. The topic is also known as: Tobit.
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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.
1,960 citations
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TL;DR: Tobin's model is also known as censored or truncated regression models as discussed by the authors, where the observations outside a specified range are totally lost and censored if one can at least observe the exogenous variables, and truncation occurs if a patient is still alive at the last observation date or if he or she cannot be located.
1,552 citations
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TL;DR: In this article, a test of weak exogeneity in the simultaneous equation Tobit model is proposed and illustrated using a female labour supply model estimated using cross-section data, which can be simply output from any standard Tobit maximum likelihood package, and is asymptotically efficient.
Abstract: A test of weak exogeneity in the simultaneous equation Tobit model is proposed and illustrated using a female labour supply model estimated using cross-section data. The test statistic can be simply output from any standard Tobit maximum likelihood package, and is asymptotically efficient. The procedure provides consistent estimators for the simultaneous Tobit model whose asymptotic covariance matrix is a simple extension of the usual Tobit formula. We also provide the Lagrange Multiplier test of weak exogeneity. (This abstract was borrowed from another version of this item.)
1,275 citations
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TL;DR: In this paper, a systematic analysis of several theoretic and statistical assumption s used in many empirical models of female labor supply is performed. But the two most important assumptions appear to be the Tobit assumption used to control for sel f-selection into the labor force and exogeneity assumptions on the worker's wage rate and her labor market experience.
Abstract: This study undertakes a systematic analysis of several theoretic and statistical assumption s used in many empirical models of female labor supply. Using a singl e data set (PSID 1975 labor supply data) the author is able to replic ate most of the range of estimated income and substitution effects fo und in previous studies in this field. He undertakes extensive specif ication tests and finds that most of this range should be rejected du e to statistical and model misspecifications. The two most important assumptions appear to be the Tobit assumption used to control for sel f-selection into the labor force and exogeneity assumptions on the wi fe's wage rate and her labor market experience. Copyright 1987 by The Econometric Society.
1,049 citations
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TL;DR: In this article, Monte Carlo methods were used to examine the small sample bias of the MLE in the tobit, truncated regression and Weibull survival models as well as the binary probit and logit and ordered probit discrete choice models.
Abstract: The nonlinear fixed-effects model has two shortcomings, one practical and one methodological. The practical obstacle relates to the difficulty of computing the MLE of the coefficients of non-linear models with possibly thousands of dummy variable coefficients. In fact, in many models of interest to practitioners, computing the MLE of the parameters of fixed effects model is feasible even in panels with very large numbers of groups. The result, though not new, appears not to be well known. The more difficult, methodological issue is the incidental parameters problem that raises questions about the statistical properties of the ML estimator. There is relatively little empirical evidence on the behaviour of the MLE in the presence of fixed effects, and that which has been obtained has focused almost exclusively on binary choice models. In this paper, we use Monte Carlo methods to examine the small sample bias of the MLE in the tobit, truncated regression and Weibull survival models as well as the binary probit and logit and ordered probit discrete choice models. We find that the estimator in the continuous response models behaves quite differently from the familiar and oft cited results. Among our findings are: first, a widely accepted result that suggests that the probit estimator is actually relatively well behaved appears to be incorrect; second, the estimators of the slopes in the tobit model, unlike the probit and logit models that have been studied previously, appear to be largely unaffected by the incidental parameters problem, but a surprising result related to the disturbance variance estimator arises instead; third, lest one jumps to a conclusion that the finite sample bias is restricted to discrete choice models, we submit evidence on the truncated regression, which is yet unlike the tobit in that regardit appears to be biased towards zero; fourth, we find in the Weibull model that the biases in a vector of coefficients need not be in the same direction; fifth, as apparently unexamined previously, the estimated asymptotic standard errors for the ML estimators appear uniformly to be downward biased when the model contains fixed effects. In sum, the finite sample behaviour of the fixed effects estimator is much more varied than the received literature would suggest.
879 citations