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Full-Text Articles in Social and Behavioral Sciences
A Monte Carlo Study Of Ranked Efficiency Estimates From Frontier Models, William C. Horrace, Seth Richards-Shubik
A Monte Carlo Study Of Ranked Efficiency Estimates From Frontier Models, William C. Horrace, Seth Richards-Shubik
Economics - All Scholarship
Parametric stochastic frontier models yield firm-level conditional distributions of inefficiency that are truncated normal. Given these distributions, how should one assess and rank firm-level efficiency? This study compares the techniques of estimating (a) the conditional mean of inefficiency and (b) probabilities that firms are most or least efficient. Monte Carlo experiments suggest that the efficiency probabilities are easier to estimate (less noisy) in terms of mean absolute percent error when inefficiency has large variation across firms. Along the way we tackle some interesting problems associated with simulating and assessing estimator performance in the stochastic frontier model.
Estimating Heterogeneous Capacity And Capacity Utilization In A Multi-Species Fishery, Ronald G. Feltoven, William C. Horrace, Kurt E. Schnier
Estimating Heterogeneous Capacity And Capacity Utilization In A Multi-Species Fishery, Ronald G. Feltoven, William C. Horrace, Kurt E. Schnier
Economics - All Scholarship
We use a stochastic production frontier model to investigate the presence of heterogeneous production and its impact on fleet capacity and capacity utilization in a multi-species fishery. We propose a new fleet capacity estimate that incorporates complete information on the stochastic differences between vessel-specific technical efficiency distributions. Results indicate that ignoring heterogeneity in production technologies within a multispecies fishery as well as the complete distribution of a vessel’s technical efficiency score, may lead to erroneous fleet-wide production profiles and estimates of capacity. Our new estimate of capacity enables out-of-sample production predictions which may be useful to policy makers.
Fixed-Effect Estimation Of Technical Efficiency With Time-Invariant Dummies, Qu Feng, William C. Horrace
Fixed-Effect Estimation Of Technical Efficiency With Time-Invariant Dummies, Qu Feng, William C. Horrace
Economics - All Scholarship
“Within” estimation of the fixed-effect stochastic frontier model does not identify parameters on time-invariant explanatory variables. If time-invariant variables are important production inputs, then standard efficiency estimates are biased. This note details bias correction, when time-invariant inputs are dummy variables.
On Ranking And Selection From Independent Truncated Normal Distributions, William C. Horrace
On Ranking And Selection From Independent Truncated Normal Distributions, William C. Horrace
Economics - All Scholarship
This paper develops probability statements and ranking and selection rules for independent truncated normal populations. An application to a broad class of parametric stochastic frontier models is considered, where interest centers on making probability statements concerning unobserved firm-level technical inefficiency. In particular, probabilistic decision rules allow subsets of firms to be deemed relatively efficient or inefficient at prespecified probabilities. An empirical example is provided.
Technical Efficiency Of Australian Wool Production: Point And Confidence Interval Estimates, William C. Horrace
Technical Efficiency Of Australian Wool Production: Point And Confidence Interval Estimates, William C. Horrace
Economics - All Scholarship
A balanced panel of data is used to estimate technical efficiency, employing a fixed-effects stochastic frontier specification for wool producers in Australia. Both point estimates and confidence intervals for technical efficiency are reported. The confidence intervals are constructed using the multiple comparisons with the best (MCB) procedure of Horrace and Schmidt (1996, 2000). The confidence intervals make explicit the precision of the technical efficiency estimates and underscore the dangers of drawing inferences based solely on point estimates. Additionally, they allow identification of wool producers that are statistically efficient and those that are statistically inefficient. The data reveal at the 95% …