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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Addressing endogeneity when estimating stochastic ray production frontiers
T2 - a Bayesian approach
AU - Tsionas, Mike
AU - Izzeldin, Marwan
AU - Henningsen, Arne
AU - Paravalos, Evaggelos
N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/s00181-021-02060-0
PY - 2022/3/31
Y1 - 2022/3/31
N2 - We propose a Bayesian approach for inference in the stochastic ray production frontier (SRPF), which can model multiple-input–multiple-output production technologies even in case of zero output quantities, i.e., if some outputs are not produced by some of the firms. However, the econometric estimation of the SRPF—as the estimation of distance functions in general—is susceptible to endogeneity problems. To address these problems, we apply a profit-maximizing framework to derive a system of equations after incorporating technical inefficiency. As the latter enters non-trivially into the system of equations and as the Jacobian is highly complicated, we use Monte Carlo methods of inference. Using US banking data to illustrate our innovative approach, we also address the problems of missing prices and the dependence on the ordering of the outputs via model averaging.
AB - We propose a Bayesian approach for inference in the stochastic ray production frontier (SRPF), which can model multiple-input–multiple-output production technologies even in case of zero output quantities, i.e., if some outputs are not produced by some of the firms. However, the econometric estimation of the SRPF—as the estimation of distance functions in general—is susceptible to endogeneity problems. To address these problems, we apply a profit-maximizing framework to derive a system of equations after incorporating technical inefficiency. As the latter enters non-trivially into the system of equations and as the Jacobian is highly complicated, we use Monte Carlo methods of inference. Using US banking data to illustrate our innovative approach, we also address the problems of missing prices and the dependence on the ordering of the outputs via model averaging.
KW - Stochastic ray production frontier
KW - Technical inefficiency
KW - Endogeneity
KW - Bayesian inference
KW - Model averaging
U2 - 10.1007/s00181-021-02060-0
DO - 10.1007/s00181-021-02060-0
M3 - Journal article
VL - 62
SP - 1345
EP - 1363
JO - Empirical Economics
JF - Empirical Economics
SN - 0377-7332
IS - 3
ER -