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Bank-Specific, Industry-Specific and Macroeconomic Determinants of Bank Profitability

TLDR
In this article, the authors examined the effect of bank-specific, industry-specific and macroeconomic determinants of bank profitability, using an empirical framework that incorporates the traditional Structure-Conduct-Performance (SCP) hypothesis.
Abstract
The aim of this study is to examine the effect of bank-specific, industry-specific and macroeconomic determinants of bank profitability, using an empirical framework that incorporates the traditional Structure-Conduct-Performance (SCP) hypothesis. To account for profit persistence, we apply a GMM technique to a panel of Greek banks that covers the period 1985-2001. The estimation results show that profitability persists to a moderate extent, indicating that departures from perfectly competitive market structures may not be that large. All bank-specific determinants, with the exception of size, affect bank profitability significantly in the anticipated way. However, no evidence is found in support of the SCP hypothesis. Finally, the business cycle has a positive, albeit asymmetric effect on bank profitability, being significant only in the upper phase of the cycle.

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References
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Journal ArticleDOI

Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations.

TL;DR: In this article, the generalized method of moments (GMM) estimator optimally exploits all the linear moment restrictions that follow from the assumption of no serial correlation in the errors, in an equation which contains individual effects, lagged dependent variables and no strictly exogenous variables.
Report SeriesDOI

Initial conditions and moment restrictions in dynamic panel data models

TL;DR: In this paper, two alternative linear estimators that are designed to improve the properties of the standard first-differenced GMM estimator are presented. But both estimators require restrictions on the initial conditions process.
Journal ArticleDOI

Another look at the instrumental variable estimation of error-components models

TL;DR: In this paper, a framework for efficient IV estimators of random effects models with information in levels which can accommodate predetermined variables is presented. But the authors do not consider models with predetermined variables that have constant correlation with the effects.
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A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test

TL;DR: The Im-Pesaran-Shin (IPS) test as discussed by the authors relaxes the restrictive assumption of the LL test and is best viewed as a test for summarizing the evidence from independent tests of the sample hypothesis.
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