Abstract not found

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Abstract not found

We discuss the relative advantages and disadvantages of four types of convenient estimators of binary choice models when regressors may be endogenous or mismeasured, or when errors are likely to be heteroskedastic. For example, such models arise when treatment is not randomly assigned and outcomes are binary. The estimators we compare are the two stage least squares linear probability model, maximum likelihood estimation, control function estimators, and special regressor methods. We specifically focus on models and associated estimators that are easy to implement. Also, for calculating choice probabilities and regressor marginal effects, we propose the average index function (AIF), which, unlike the average structural function (ASF), is always easy to estimate.

In this paper we analyze the pattern of employment adjustment using a rich panel of Norwegian plants. The data suggest that the frequency of episodes of zero net employment changes is inversely related to plant size. We develop and estimate a simple “q ” model of labor demand, allowing for the presence of fixed, linear and convex components of adjustment costs. The econometric evidence supports the existence of purely fixed components, unrelated to plant size. As a result, the range of inaction is wider for smaller plants. The quadratic components of costs are also important. Finally, in most specifications both fixed and convex costs are higher for employment contractions. JEL classification: D21, C23, E24

This paper provides two main contributions to binary choice models with endogenous regressors. First, we propose some variants of special regressor based estimators that are numerically trivial to implement. These estimators provide consistent estimates of binary choice model coef cients when regressors (either discretely or continuously distributed) are endogenous, and when the latent errors have heteroskedasticity of unknown form. We also propose an alternative to the average structural function (ASF) measure of tted values for models having a latent index structure that is easier to calculate than ASF. We use this to provide simple estimators for choice probabilities and marginal effects of the regressors. We illustrate these methods with an empirical application to the estimation of migration probabilities within the US.

We consider estimation of means of functions that are scaled by an unknown density, or equivalently, integrals of conditional expectations. The ”ordered data ” estimator we provide is root n consistent, asymptotically normal, and is numerically extremely simple, involving little more than ordering the data and summing the results. No sample size dependent smoothing is required. A similarly simple estimator is provided for the limiting variance. The proofs include new limiting distribution results for functions of nearest neighbor spacings. Potential applications include endogeneous binary choice, willingness to pay, selection, and treatment models.

This chapter provides background for understanding and applying special regressor methods. This chapter is intended for inclusion in the "Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics, " Co-edited by Aman Ullah, Jeffrey Racine, and Liangjun Su, to be published by Oxford University Press

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Abstract. In this paper we study nonparametric estimation in a binary treatment model where the outcome equation is of unrestricted form, and the selection equation contains multiple unobservables that enter through a nonparametric random coefficients specification. This specification is flexible because it allows for complex unobserved heterogeneity of economic agents and non-monotone selection into treatment. We obtain conditions under which both the conditional distributions of Y0 and Y1, the outcome for the untreated, respectively treated, given first stage unobserved random coefficients, are identified. We can thus identify an average treatment effect, conditional on first stage unobservables called UCATE, which yields most treatment effects parameters that depend on averages, like ATE and TT. We provide sharp bounds on the variance, the joint distribution of (Y0,Y1) and the distribution of treatment effects. In the particular case where the outcomes are continuously distributed, we provide novel and weak conditions that allow to point identify the joint conditional distribution of Y0,Y1, given the unobservables. This allows to derive every treatment effect parameter, e.g. the distribution of treatment effects and the proportion of individuals who benefit from treatment. We present estimators for the marginals, average and distribution of treatment effects, both conditional on unobservables and unconditional, as well as total population effects. The estimators use all the data and discard