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Modeling dependence in two-tier stochastic frontier models

Author

Listed:
  • Alecos Papadopoulos

    (Athens University of Economics and Business)

  • Christopher F. Parmeter

    (University of Miami)

  • Subal C. Kumbhakar

    (State University)

Abstract

The two-tier stochastic frontier model has seen widespread application across a range of social science domains. It is particularly useful in examining bilateral exchanges where unobserved side-specific information exists on both sides of the transaction. These buyer and seller specific informational aspects offer opportunities to extract surplus from the other side of the market, in combination also with uneven relative bargaining power. Currently, this model is hindered by the fact that identification and estimation relies on the potentially restrictive assumption that these factors are statistically independent. We present three different models for empirical application that allow for varying degrees of dependence across these latent informational/bargaining factors.

Suggested Citation

  • Alecos Papadopoulos & Christopher F. Parmeter & Subal C. Kumbhakar, 2021. "Modeling dependence in two-tier stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 56(2), pages 85-101, December.
    • Handle: RePEc:kap:jproda:v:56:y:2021:i:2:d:10.1007_s11123-021-00611-2
    DOI: 10.1007/s11123-021-00611-2
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    Cited by:

    1. Schmidt, Rouven & Kneib, Thomas, 2023. "Multivariate distributional stochastic frontier models," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    2. Alecos Papadopoulos, 2024. "The Nash Bargaining Two-tier Stochastic Frontier Model," Advances in Econometrics, in: Essays in Honor of Subal Kumbhakar, volume 46, pages 439-476, Emerald Group Publishing Limited.
    3. Alecos Papadopoulos, 2021. "Measuring the effect of management on production: a two-tier stochastic frontier approach," Empirical Economics, Springer, vol. 60(6), pages 3011-3041, June.
    4. Shirong Zhao & Jeremy Losak, 2024. "Two-tiered stochastic frontier models: a Bayesian perspective," Journal of Productivity Analysis, Springer, vol. 61(2), pages 85-106, April.

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