Firm-Heterogeneity, Persistent and Transient Technical Inefficiency

This paper provides a new model that disentangles firm effects from persistent (time-invariant/long-term) and transient (time-varying/short-term) technical inefficiency.Bayesian analysis; Markov Chain Monte Carlo; Technical efficiency.

Are US regional incomes converging? A nonlinear perspective

This article deviates from the current practice of regional convergence by allowing output convergence to follow a non-linear process. In this scenario all standard linear unit root tests have low power, thus frequently leading to misguided conclusions. In light of this we adopt a unit root test based on a non-linear model which tests the null hypothesis of a unit root against a non-linear alternative. Our findings overwhelmingly support the tendency of US regions to converge over time

A Novel Model of Costly Technical Efficiency

This paper presents a novel model of measuring technical inefficiency based on the notion that higher efficiency requires a certain cost. First, we apply the “rational inefficiency hypothesis” of Bogetoft and Hougaard (2003) but we fail to find that it rationalizes our data set of large U.S banks with multiple inputs and outputs. In consequence, we adopt a novel model of profit maximization which explicitly incorporates the cost of technical inefficiency. The cost of inefficiency is treated as unknown and is parametrized as a function of inputs, outputs and decision-making-unit specific fixed effects. More importantly, by showing the model to be equivalent to one in which inefficiency is an arbitrary function of inputs, outputs and the inefficiency cost, we are able to determine optimal directions in the input-output space that would reduce inefficiency. Bayesian techniques organized around Markov Chain Monte Carlo are used to perform the computations and provide statistical inferences as well as useful policy measures to reduce inefficiencies in the U.S banking sector through an examination of different realistic scenarios

Adjustment costs in the technical efficiency: an application to global banking

This paper proposes a new framework of measuring technical efficiency that takes into account adjustment costs in variable inputs associated with changes in efficiency. We look closely at the implicit assumption in any model of technical efficiency that inputs could freely adjust. Yet, the technical efficiency is determined from the allocation of inputs by the firm to production on the one hand and to efficiency on the other. We show that technical efficiency depends on adjustment costs in variable inputs. Estimating the proposed model has certain complexities that we overcome by employing a non-parametric Local Linear Maximum Likelihood (LLML). In the empirical section, we employ a comprehensive global banking sample and estimate bank alternative profit efficiency across a plethora of countries with strong variability in the underlying adjustment costs. Moreover, given the observed heterogeneity across countries evidence shows that adjustment costs due to personnel expenses are the highest among advanced countries. Emerging economies show strong potential in terms of efficiency post-financial crisis, mainly due to lower labor adjustment costs. Alas, our findings show some persistence in adjustment costs post the financial crisis

Measuring hotel performance:Toward more rigorous evidence in both scope and methods

This paper extends the literature on hotel performance in both scope and methods. We introduce a model that accounts for heterogeneity in a flexible way and allows for the measurement of both efficiency and productivity. The model also accounts for the endogeneity problem in inputs and the issue of unobserved prices. We use a large sample of hotel companies that spreads across multiple geographical regions and locations, and accounts for some interesting and key determinants of hotel performance. We provide more validation to some contradictory findings in the literature. We show that large hotels do not necessarily outperform small hotels, and that hotel efficiency differs based on location, geographical region and type of service. The results further indicate that productivity growth is not a driving force in the industry

A note on the Gao et al. (2019) uniform mixture model in the case of regression

© 2019, The Author(s). We extend the uniform mixture model of Gao et al. (Ann Oper Res, 2019. https://doi.org/10.1007/s10479-019-03236-9) to the case of linear regression. Gao et al. (Ann Oper Res, 2019. https://doi.org/10.1007/s10479-019-03236-9) proposed that to characterize the probability distributions of multimodal and irregular data observed in engineering, a uniform mixture model can be used. This model is a weighted combination of multiple uniform distribution components. This case is of empirical interest since, in many instances, the distribution of the error term in a linear regression model cannot be assumed unimodal. Bayesian methods of inference organized around Markov chain Monte Carlo are proposed. In a Monte Carlo experiment, significant efficiency gains are found in comparison to least squares justifying the use of the uniform mixture model

Simple techniques for likelihood analysis of univariate and multivariate stable distributions: with extensions to multivariate stochastic volatility and dynamic factor models

In this paper we consider a variety of procedures for numerical statistical inference in the family of univariate and multivariate stable distributions. In connection with univariate distributions (i) we provide approximations by finite location-scale mixtures and (ii) versions of approximate Bayesian computation (ABC) using the characteristic function and the asymptotic form of the likelihood function. In the context of multivariate stable distributions we propose several ways to perform statistical inference and obtain the spectral measure associated with the distributions, a quantity that has been a major impediment in using them in applied work. We extend the techniques to handle univariate and multivariate stochastic volatility models, static and dynamic factor models with disturbances and factors from general stable distributions, a novel way to model multivariate stochastic volatility through time-varying spectral measures and a novel way to multivariate stable distributions through copulae. The new techniques are applied to artificial as well as real data (ten major currencies, SP100 and individual returns). In connection with ABC special attention is paid to crafting well-performing proposal distributions for MCMC and extensive numerical experiments are conducted to provide critical values of the “closeness” parameter that can be useful for further applied econometric work

Simple techniques for likelihood analysis of univariate and multivariate stable distributions: with extensions to multivariate stochastic volatility and dynamic factor models

In this paper we consider a variety of procedures for numerical statistical inference in the family of univariate and multivariate stable distributions. In connection with univariate distributions (i) we provide approximations by finite location-scale mixtures and (ii) versions of approximate Bayesian computation (ABC) using the characteristic function and the asymptotic form of the likelihood function. In the context of multivariate stable distributions we propose several ways to perform statistical inference and obtain the spectral measure associated with the distributions, a quantity that has been a major impediment in using them in applied work. We extend the techniques to handle univariate and multivariate stochastic volatility models, static and dynamic factor models with disturbances and factors from general stable distributions, a novel way to model multivariate stochastic volatility through time-varying spectral measures and a novel way to multivariate stable distributions through copulae. The new techniques are applied to artificial as well as real data (ten major currencies, SP100 and individual returns). In connection with ABC special attention is paid to crafting well-performing proposal distributions for MCMC and extensive numerical experiments are conducted to provide critical values of the “closeness” parameter that can be useful for further applied econometric work