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Strong assumptions needed to correctly specify parametric binary choice probability models make them particularly vulnerable to misspecification. Semiparametric models provide a less restrictive approach with estimators that exhibit desirable asymptotic properties. This paper discusses the...
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We consider the estimation of sample selection (type II Tobit) models that exhibit spatial error dependence or spatial autoregressive errors (SAE). The method considered is motivated by a two-step strategy analogous to the popular heckit model. The first step of estimation is based on a spatial...
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We propose a new asymptotic approximation for the sampling behavior of nonparametric estimates of the spectral density …
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