On the choice of functional form in stochastic frontier modeling

By K. Giannakas, Kien C. Tran, Vangelis Tzouvelekas. 2003

113 citations recorded by the source corpus.

Subfields: Environmental Chemistry, General Agricultural and Biological Sciences, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/1ca421aebcca056cd63d3990d2cccda98dd6b058

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Chemistry

1. Timo Kuosmanen

In General Agricultural and Biological Sciences

1. Léopold Simar
2. Paul Wilson
3. Timo Kuosmanen

In Management Science and Operations Research

1. Timo Kuosmanen
2. Paul Wilson
3. Léopold Simar
4. Cinzia Daraio
5. Anne Vanhems
6. Mika Kortelainen
7. Laure Latruffe *
8. Abdelaati Daouia

Citations in the corpus

• Inferences from Cross-Sectional, Stochastic Frontier Models (2009, background): “Horrace and Schmidt as well as others (e.g., Greene, 1999; Giannakas et al., 2003) have noted that use of the conventional prediction intervals involves replacing unknown parameters with their estimates, and hence the resulting interval estimates understate the uncertainty that is involved.”
• Specification and Estimation of Production Functions Involving Damage Control Inputs: A Two-Stage, Semiparametric Approach (2006, background): “…over another, and typically the data do not reveal a strong preference for any particular functional form (e.g., Zhu, Ellinger, and Shumway, 1995; Giannakas, Tran, and Tzouvelekas, 2003).2 Although different functional forms offer almost equally good empirical fit, the estimated marginal…”