Functional Estimator Selection Techniques for Production Functions and Frontiers
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This dissertation provides frameworks to select production function estimators in both the state-contingent and the general monotonic and concave cases. It first presents a Birth-Death Markov Chain Monte Carlo (BDMCMC) Bayesian algorithm to endogenously estimate the number of previously unobserved states of nature for a state-contingent frontier. Secondly, it contains a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm to determine a parsimonious piecewise linear description of a multiplicative monotonic and concave production frontier. The RJMCMC based algorithm is the first computationally efficient one-stage estimator of production frontiers with potentially heteroscedastic inefficiency distribution and environmental variables. Thirdly, it provides general framework, based on machine learning concepts, repeated learning-testing and parametric bootstrapping techniques, to select the best monotonic and concave functional estimator for a production function from a pool of functional estimators. This framework is the first to test potentially nonlinear production function estimators on actual datasets, rather than extrapolation of Monte Carlo simulation results.
Preciado Arreola, Jose Luis (2016). Functional Estimator Selection Techniques for Production Functions and Frontiers. Doctoral dissertation, Texas A & M University. Available electronically from