Dependence modeling in stochastic frontier analysis-Reference-Cited by-同舟云学术

Dependence modeling in stochastic frontier analysis

Published:2022-01-01 Issue:1 Volume:10 Page:123-144
ISSN:2300-2298
Container-title:Dependence Modeling
language:en
Short-container-title:

Author:

Mamonov Mikhail E.¹²,Parmeter Christopher F.³,Prokhorov Artem B.⁴⁵⁶

Affiliation:

1. Institute for International Studies, MGIMO-University, Prospekt Vernadskogo 76 , 119454 Moscow , Russia

2. CERGE-EI, a joint workplace of Charles University and the Economics Institute of the Czech Academy of Sciences, 111 21 Politickych veznu 7 , Prague , Czech Republic

3. Department of Economics, University of Miami, 517-E Jenkins Building , Coral Gables , Florida , USA

4. Discipline of Business Analytics, Business School, The University of Sydney, Room 4088 , Abercrombie Building (H70) , Australia

5. Center for Econometrics and Business Analytics (CEBA), Saint-Petersburg State University , St. Petersburg , Russia

6. Interuniversity Center for Research in Quantitative Economics (CIREQ), University of Montreal , Lionel-Groulx Pavilion , CP 6128 , Montreal , Canada

Abstract

Abstract This review covers several of the core methodological and empirical developments surrounding stochastic frontier models that incorporate various new forms of dependence. Such models apply naturally to panels where cross-sectional observations on firm productivity correlate over time, but also in situations where various components of the error structure correlate between each other and with input variables. Ignoring such dependence patterns is known to lead to severe biases in the estimates of production functions and to incorrect inference.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Modeling and Simulation,Statistics and Probability

Link

https://www.degruyter.com/document/doi/10.1515/demo-2022-0107/pdf

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