An Analysis of Differences in Earnings Between Small and Large Commercial Banks

This paper introduces a simple, yet rich, measure of efficiency changes based on the revenue-generating-ability (RGA) principle. Using this principle, we explain the connections between efficiency changes and the variables, such as pretax profits, interest expense, non-interest expense, profit margins, loan loss provision, and asset quality. These connections are used to explain earnings differences between small and large commercial banks.     

A General Method of Deriving the Inefficiencies of Banks from a Profit Function

This article develops a new method of estimating inefficiencies in joint production and shows that unlike the approaches utilized in the previous studies of inefficiency, this method maintains a consistent relationship between the error term of a profit function and the error terms of its price derivatives. A useful by-product of the method is a proof of a Hotelling-like lemma that relates stochastic input demand and output supply functions to stochastic profit functions. While the previous studies fit a single frontier to data on all firms, this paper estimates a frontier unique to every observed firm to allow each one to have a different potential of achieving maximal levels of profit. The new method is applied in the analysis of annual data, 1984–1989, for U.S. commercial banks. Both the analytical and numerical results of the paper show that the residual that the previous studies attribute to inefficiency includes the effects of excluded variables and of inaccuracies in the specified functional forms. Once accurate estimates of these effects are subtracted from the residual, the distortions in the measured inefficiencies should be considerably reduced. Consequently, this article considers how such estimates might be obtained.     

Social Capital, Household Welfare and Poverty in Burkina Faso

This paper investigates empirically the importance of socialcapital, in the form of local associations and networks, forthe welfare of rural households in Burkina Faso. It draws ona unique database combining standard information on householdwelfare with multidimensional measures of social capital. Theanalysis finds that higher levels of social capital are associatedwith higher household per capita expenditures and better accessto credit. The distribution of social capital was found to bemore equal than that of other assets. Poor households and thosewho own little land obtain a higher return from social capitalthan other households.     

Comparing forecasts from fixed and variable coefficient models: The case of money demand

In this paper we introduce a class of tentatively plausible, fixed-coefficient models of money demand and evaluate their forecast performance. When these models are reestimated allowing all coefficients to vary over time, the forecasting performance improves dramatically. Aside from offering insights about improved methods of analyzing time series data, the most promising direct use for point estimates derived from time-varying coefficients is as an aid in calibrating proposed models of the kind discussed here.     

RANDOM COEFFICIENT MODELS: THEORY AND APPLICATIONS

Abstract. This paper provides an overview of the rationale behind, and the implementation, and uses of, the random coefficient approach to econometric modelling. A simple random coefficient model is presented, and methods for estimating, testing, and validating such a model are described. A more general model is then presented. The general model is shown to include several fixed-coefficient models as special cases and can be estimated incorporating a variety of judgements concerning simplification. Finally, the paper reviews recent applications of random coefficient estimation.     

On Bayesian estimation of seemingly unrelated regressions when some observations are missing

In this paper we have considered the problem of estimating the parameters of two seemingly unrelated regression equations when the number of available observations on all the variables in the first equation exceeds the number of available observations on all the variables in the second equation. We have shown how, within the Bayesian framework, extra observations on all the variables in the first equation can be readily and formally utilized in obtaining the exact or an approximate posterior distributions of all the coefficients including those in the second equation.