Archive for November, 2020

UPDATE: The paper went on-line on February 2, 2021,

The paper “Stochastic frontier models using the Generalized Exponential distribution” has just been approved for publication in the Journal of Productivity Analysis.

Abstract: We present a new, single-parameter distributional specification for the one-sided error components in single-tier and two-tier stochastic frontier models. The distribution has its mode away from zero, and can represent cases where the most likely outcome is non-zero inefficiency. We present the necessary formulas for estimating production, cost and two-tier stochastic frontier models in logarithmic form. We pay particular attention to the use of the conditional mode as a predictor of individual inefficiency. We use simulations to assess the performance of existing models when the data include an inefficiency term with non-zero mode, and we also contrast the conditional mode to the conditional expectation as measures of individual (in)efficiency.

Download the pre-print here.

This survey has just been published in the collection Parmeter, C. F., & Sickles, R. C. (2020) Advances in Efficiency and Productivity Analysis. Springer. Naturally, it is based on my PhD, and it is a comprehensive survey of the state-of-the-art of the Two-tier Stochastic Frontier Framework, surveying theoretical foundations, estimation tools, and the large variety of application this modeling framework has been used for. Indicatively, it has been used to measure the impact of informational asymmetry in wage negotiations, in the house market, in the Health Services market, the impact of asymmetric bargaining power in International donors-recipients relationship but also in Tourist shopping, or to measure the effects of “optimism” and “pessimism” in self-reported quality of life. And may more, economic and not-so-economic situations.

Anywhere where we can perceive of opposing latent forces operating on the outcome, this model can be applied. This is why I use as its pet name the “noisy Tug-of-War” model -“noisy” because there is also a “noise” component in the composed error specification.