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What Can We Learn From Benefit Transfer Errors? Evidence From 20 Years Of Research On Convergent Validity, Sapna Kaul, Kevin Boyle, Nicolai Kuminoff, Christopher Parmeter, Jaren Pope
What Can We Learn From Benefit Transfer Errors? Evidence From 20 Years Of Research On Convergent Validity, Sapna Kaul, Kevin Boyle, Nicolai Kuminoff, Christopher Parmeter, Jaren Pope
Christopher F. Parmeter
No abstract provided.
Due Diligence In Meta-Analysis To Support Benefit Transfer, Kevin Boyle, Christopher Parmeter, Brent Boehlert, Robert Paterson
Due Diligence In Meta-Analysis To Support Benefit Transfer, Kevin Boyle, Christopher Parmeter, Brent Boehlert, Robert Paterson
Christopher F. Parmeter
No abstract provided.
Data-Driven Model Evaluation: A Test For Revealed Performance, Jeffrey Racine, Christopher Parmeter
Data-Driven Model Evaluation: A Test For Revealed Performance, Jeffrey Racine, Christopher Parmeter
Christopher F. Parmeter
No abstract provided.
Regression And Inference Under Smoothness Restrictions, Christopher Parmeter, Kai Sun, Daniel Henderson, Subal Kumbhakar
Regression And Inference Under Smoothness Restrictions, Christopher Parmeter, Kai Sun, Daniel Henderson, Subal Kumbhakar
Christopher F. Parmeter
No abstract provided.
Does Education Matter For Economic Growth?, Michael Delgado, Daniel J. Henderson, Christopher Parmeter
Does Education Matter For Economic Growth?, Michael Delgado, Daniel J. Henderson, Christopher Parmeter
Christopher F. Parmeter
Empirical economic research typically uses education as a proxy for human capital. However, research aimed at validating the inclusion of education measures in growth regressions has yet to reach a consensus, often finding that the sign and significance of education depends on the sample of observations or the specification of the model. The goal of this paper is to reconcile the conflicting empirical evidence and validate (or invalidate) the inclusion of education in international growth regressions by providing a rigorous and systematic search for significance of education. Using methods which are largely immune to model misspecification, we examine six of …
A Zero Inefficiency Stochastic Frontier Estimator, Subal C. Kumbhakar, Christopher Parmeter, Efthymios G. Tsionas
A Zero Inefficiency Stochastic Frontier Estimator, Subal C. Kumbhakar, Christopher Parmeter, Efthymios G. Tsionas
Christopher F. Parmeter
Traditional stochastic frontier models impose inefficient behavior on all firms in the sample of interest. If the data under investigation represent a mixture of both fully efficient and inefficient firms then off-the-shelf frontier models are statistically inadequate. We introduce a zero-inflated stochastic frontier model which can accommodate the presence of both efficient and inefficient firms in the sample. We derive the corresponding log-likelihood function, conditional mean of inefficiency to estimate observation-specific inefficiency and discuss testing for the presence of fully efficient firms. We provide both simulated evidence as well as an empirical example which demonstrates the applicability of the proposed …