Examinando por Autor "Malikov, Emir"
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Ítem Are all U.S. credit unions alike?(Universidad EAFIT, 2013-04-11) Malikov, Emir; Restrepo, Diego A.; Kumbhakar, Subal C.This paper raises concerns about the econometric approach used in the literature to estimate credit unions’ production technologies. We show that the existing studies did not recognize heterogeneity amongst credit unions’ technologies as captured by (endogenously selected) differing output mixes. Failure to account for the above leads to biased, inconsistent estimates and potentially misleading results. The estimates are also likely to be biased due to unobserved credit union specific effects that the literature broadly ignores. To address these concerns, we develop a generalized model of endogenous switching with polychotomous choice that is able to account for fixed effects in both the technology selection and the outcome equations. We use this model to estimate returns to scale for the U.S. retail credit unions from 1994 to 2011. Unlike recent studies, we find that not all credit unions enjoy increasing returns to scale. A nonnegligible number of large institutions operate at decreasing returns to scale, indicating that they should either cut back in size or switch to a more efficient technology by re-optimizing the output mix.Ítem Estimation of banking technology under credit uncertainty(Universidad EAFIT, 2013-05-15) Malikov, Emir; Restrepo, Diego A.; Kumbhakar, Subal C.Credit risk is crucial to understanding banks’ production technology and should be explicitly accounted for when modeling the latter. The banking literature has largely accounted for risk by using ex-post realizations of banks’ uncertain outputs and the variables intended to capture risk. This is equivalent to estimating an ex-post realization of bank’s production technology which, however, may not reflect optimality conditions that banks seek to satisfy under uncertainty. The ex-post estimates of technology are likely to be biased and inconsistent, and one thus may call into question the reliability of the results regarding banks’ technological characteristics broadly reported in the literature. However, the extent to which these concerns are relevant for policy analysis is an empirical question. In this paper, we offer an alternative methodology to estimate banks’ production technology based on the ex-ante cost function. We model credit uncertainty explicitly by recognizing that bank managers minimize costs subject to given expected outputs and credit risk. We estimate unobservable expected outputs and associated credit risk levels from banks’ supply functions via nonparametric kernel methods. We apply this framework to estimate production technology of U.S. commercial banks during the period from 2001 to 2010 and contrast the new estimates with those based on the ex-post models widely employed in the literature.Ítem Estimation of banking technology under credit uncertainty(Springer International Publishing, 2014) Malikov, Emir; Restrepo-Tobón, Diego; Kumbhakar, Subal C.; Department of Economics, State University of New York at Binghamton, Department of Economics, St. Lawrence University; Department of Finance, EAFIT University; Department of Economics, State University of New York at Binghamton; Economía y Finanzas; Finanzas; Grupo de Investigación Finanzas y BancaCredit risk is crucial to understanding banks’ production technology and should be explicitly accounted for when modeling the latter. The banking literature has largely accounted for risk usingex-post realizations of banks’ uncertain outputs and the variables intended to capture risk. This is equivalent to estimating an ex-post realization of bank’s production technology which, however, may not reflect optimality conditions that banks seek to satisfy under uncertainty. The ex-post estimates of technology are likely to be biased and inconsistent, and one thus may call into question the reliability of the results regarding banks’ technological characteristics broadly reported in the literature. However, the extent to which these concerns are relevant for policy analysis is an empirical question. In this paper, we offer an alternative methodology to estimate banks’ production technology based on the ex-ante cost function. We model credit uncertainty explicitly by recognizing that bank managers minimize costs subject to given expected outputs and credit risk. We estimate unobservable expected outputs and associated credit risk levels from banks’ supply functions via nonparametric kernel methods. We apply this framework to estimate production technology of U.S. commercial banks during the period from 2001 to 2010 and contrast the new estimates with those based on the ex-post models widely employed in the literature.