TY - JOUR

T1 - An extension from network DEA to copula-based network SFA

T2 - Evidence from the U.S. commercial banks in 2009

AU - Huang, Tai Hsin

AU - Chen, Kuan Chen

AU - Lin, Chung I.

N1 - Publisher Copyright:
© 2017 Board of Trustees of the University of Illinois

PY - 2018/2

Y1 - 2018/2

N2 - The main contribution of network DEA deals with the dual role of deposits in the bank production process. Deposits are first viewed as an intermediate output, produced by, e.g., fractions of labor and capital. This intermediate output is next used as an input in the second process, together with the remaining labor and capital, to produce output combinations. A problem occurs in that network DEA suffers from the difficulty of determining the fractions of labor and capital used in the first process. This research thus develops an economic model to characterize the underlying multi-stage technologies and proposes a copula-based econometric model to identify parameters of the structural equations, including the fractional parameters, by the maximum likelihood. Our model also estimates technical efficiencies of the stochastic production and cost frontiers. We collect data from U.S. banks in 2009 to illustrate the feasibility and usefulness of our modeling, and the results are promising.

AB - The main contribution of network DEA deals with the dual role of deposits in the bank production process. Deposits are first viewed as an intermediate output, produced by, e.g., fractions of labor and capital. This intermediate output is next used as an input in the second process, together with the remaining labor and capital, to produce output combinations. A problem occurs in that network DEA suffers from the difficulty of determining the fractions of labor and capital used in the first process. This research thus develops an economic model to characterize the underlying multi-stage technologies and proposes a copula-based econometric model to identify parameters of the structural equations, including the fractional parameters, by the maximum likelihood. Our model also estimates technical efficiencies of the stochastic production and cost frontiers. We collect data from U.S. banks in 2009 to illustrate the feasibility and usefulness of our modeling, and the results are promising.

KW - Copula-based method

KW - Fractional parameters

KW - Multi-stage technologies

KW - Network DEA

KW - Stochastic production and cost frontiers

KW - Technical efficiency

UR - http://www.scopus.com/inward/record.url?scp=85019585371&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85019585371&partnerID=8YFLogxK

U2 - 10.1016/j.qref.2017.04.007

DO - 10.1016/j.qref.2017.04.007

M3 - Article

AN - SCOPUS:85019585371

SN - 1062-9769

VL - 67

SP - 51

EP - 62

JO - Quarterly Review of Economics and Finance

JF - Quarterly Review of Economics and Finance

ER -