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Full-Text Articles in Social and Behavioral Sciences
The Conditional Mode In Parametric Frontier Models, William C. Horrace, Hyunseok Jung, Yi Yang
The Conditional Mode In Parametric Frontier Models, William C. Horrace, Hyunseok Jung, Yi Yang
Center for Policy Research
We survey formulations of the conditional mode estimator for technical inefficiency in parametric stochastic frontier models with normal errors and introduce new formulations for models with Laplace errors. We prove the conditional mode estimator converges pointwise to the true inefficiency value as the noise variance goes to zero. We also prove that the conditional mode estimator in the normal-exponential model achieves near-minimax optimality. Our minimax theorem implies that the worst-case risk occurs when many firms are nearly efficient, and the conditional mode estimator minimizes estimation risk in this case by estimating these small inefficiency firms as efficient. Unlike the conditional …
Wrong Skewness And Finite Sample Correction, Qu Feng, William C. Horrace, Guiying Laura Wu
Wrong Skewness And Finite Sample Correction, Qu Feng, William C. Horrace, Guiying Laura Wu
Center for Policy Research
In parametric stochastic frontier models, the composed error is specified as the sum of a two-sided noise component and a one-sided inefficiency component, which is usually assumed to be half-normal, implying that the error distribution is skewed in one direction. In practice, however, estimation residuals may display skewness in the wrong direction. Model re-specification or pulling a new sample is often prescribed. Since wrong skewness is considered a finite sample problem, this paper proposes a finite sample adjustment to existing estimators to obtain the desired direction of residual skewness. This provides another empirical approach to dealing with the so-called wrong …
Endogenous Network Production Functions With Selectivity, William C. Horrace, Xiaodong Liu, Eleonora Patacchini
Endogenous Network Production Functions With Selectivity, William C. Horrace, Xiaodong Liu, Eleonora Patacchini
Center for Policy Research
We consider a production function model that transforms worker inputs into outputs through peer effect networks. The distinguishing features of this production model are that the network is formal and observable through worker scheduling, and selection into the network is done by a manager. We discuss identification and suggest a variety of estimation techniques. In particular, we tackle endogeneity issues arising from selection into groups and exposure to common group factors by employing a polychotomous Heckman-type selection correction. We illustrate our method using data from the Syracuse University Men’s Basketball team, where at any point in time the coach selects …