Empirical tools to assess the sensitivity of directional distance functions to direction selection

Directional Distance Functions (DDFs) are becoming a popular way of measuring efficiency as they encompass the Shephard output and input distance functions as special cases. However, the most critical and still unsolved issue related to DDF remains the selection of the direction along which to measure the distance from the efficient frontier. In this article, we propose some empirical tools which allow to quantify the sensitivity of the efficiency measurement to the selection of the direction. The proposed tools are applied on a dataset on the Italian agricultural sector.     

Monetary and fiscal policies' effect on agricultural growth: GMM estimation and simulation analysis

The concept of sustainable economic growth is closely linked with the agricultural growth. This is especially true in the context of under-developed countries. Pakistan is a typical under-developed country that has huge labor force employed in conventional rural economy and more than half of the population relies on agriculture for subsistence. The study examines the agricultural growth through developing a model using the data from agricultural sector of Pakistan for the period 1972–2010. The model is primarily based on input–output reduced form structural equations approach. It is then estimated by GMM, validated and used for deterministic simulation analyses. Finally the validated model is used to critically analyze the impact of fiscal, monetary and energy policies on the agricultural output. We conluded that recent fiscal and monetary policies should be continued, while the energy policy needs to be modified in order to improve the agricultural GDP and reduce the rural poverty situation in the country.     

Solution and estimation of RE macromodels with optimal policy

Macro models of monetary policy typically involve forward looking behavior. Except in rare circumstances, we have to apply some numerical method to find the optimal policy and the rational expectations equilibrium. This paper summarizes a few useful methods, and shows how they can be combined with a Kalman filter to estimate the deep model parameters with maximum likelihood. Simulations of a macro model with staggered price setting, interest rate elastic output, and optimal monetary policy illustrate the properties of this estimation approach.     

Comparing French and US hospital technologies: a directional input distance function approach

French and US hospital technologies are compared using directional input distance functions. The aggregation properties of the directional distance function allow comparison of hospital industry-level performance as well as standard firm-level performance with regard to productive efficiency. In addition, the underlying constituents of efficiency?–?in the short run, congestion and technical inefficiency, and in the long run, scale inefficiency?–?are analysed by decomposing the overall measure. By virtue of using the directional distance function, it is also possible to obtain an estimate of a lower bound on allocative inefficiency. It is found that French and US hospitals use quite different technologies. Long run scale inefficiencies cause most of the French hospitals’?inefficiency, while short run technical inefficiency is the main source of overall productive inefficiency in the US hospitals.     

Engineering robust instruments for GMM estimation of panel data regression models with errors in variables: a note

Econometricians have long recognized the need to account in some way for measurement errors, specification errors and endogeneity to ensure that the ordinary least squares estimator is consistent. This article introduces a new generalized method of moments estimator that relies on robust instruments to estimate panel data regression models containing errors in variables. We show how this GMM approach can be generalized for the panel data framework using higher moments and cumulants as instruments. The new instruments, engineered for greater robustness, are proposed to tackle the pervasive problem of weak instruments.     

Shadow prices of $$\hbox {CO}_{2}$$ emissions at US electric utilities: a random-coefficient,random-directional-vector directional output distance function approach

We estimate the shadow prices of \(\hbox {CO}_{2}\) emissions of electric utilities in the US over the period from 2001 to 2014, using a random-coefficient, random-directional-vector directional output distance function (DODF) model. The main feature of this model is that both its coefficients and directional vector are allowed to vary across firms, thus allowing different firms to have different production technologies and to follow different growth paths. Our Bayes factor analysis indicates that this model is strongly favored over the commonly used fixed-coefficient DODF model. Our results obtained from this model suggest that the average annual shadow price of \(\hbox {CO}_{2}\) emissions ranges from $61.62 to $105.72 (in 2001 dollars) with an average of $83.12. The results also suggest that the firm-specific average shadow price differs significantly across electric utilities. In addition, our estimates of the shadow price of \(\hbox {CO}_{2}\) emissions show an upward trend for both the sample electric utilities as a whole and the majority of the individual sample electric utilities.     

Semiparametric estimation of conditional mean functions with missing data

A new semiparametric estimator for estimating conditional expectation functions from incomplete data is proposed, which integrates parametric regression with nonparametric matching estimators. Besides its applicability to missing data situations due to non-response or attrition, the estimator can also be used for analyzing treatment effect heterogeneity and statistical treatment rules, where data on potential outcomes is missing by definition. By combining moments from a parametric specification with nonparametric estimates of mean outcomes in the non-responding population within a GMM framework, the estimator seeks to balance a good fit in the responding population with low bias in the non-responding population. The estimator is applied to analyzing treatment effect heterogeneity among Swedish rehabilitation programmes.

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Specification and estimation of nonstandard profit functions

This paper critically examines appropriateness of the nonstandard profit function (NSPF) as a tool for measuring profit efficiency. We show that there are some fundamental problems with the NSPF approach, the way it is currently specified and estimated. This is true irrespective of whether technical inefficiency is included or not. Instead of estimating profit efficiency directly from the NSPF, we suggest alternative specifications and recommend using the cost function approach and deriving profit efficiency therefrom. Data from US banks are used to estimate the cost function and the NSPF. Results from alternative specifications are then compared and analyzed.

Slack-based directional distance function in the presence of bad outputs: theory and application to Vietnamese banking

In this paper we extend the slack-based directional distance function introduced by Färe and Grosskopf (Eur J Oper Res 200(1):320–322, 2010) to measure efficiency in the presence of bad outputs and illustrate it through an application on data of Vietnamese commercial banks. We also compare results from the slack-based directional distance function relative to the directional distance function, the enhanced hyperbolic efficiency measure (Färe et al. in Rev Econ Stat 71(1):90–98, 1989) and the Farrell-type technical efficiency and confirm that it has greater discriminative power.     

GMM estimation of a structural demand model for yogurt and the effects of the introduction of new brands

The demand structure for yogurt is assumed to be properly described by a one level nested logit model that is applied to aggregate market data. Given the presence of endogenous regressors, suitably lagged endogenous variables (Arellano and Bover in J Econom 68:29–51, 1995; Blundell and Bond in J Econom 87:115–143, 1998) are proposed as instrumental variables. The validity of this set of instruments is discussed and price elasticities and marginal costs are recovered from the demand estimates. Total welfare gains associated to the introduction of two new brands by the same manufacturer are finally computed. Prices and profits decreased and total welfare increased.     

Trade distance functions and the derivation of inverse demand for imports

This article introduces a new representation of trade preferences termed as the trade distance function, which measures the maximum amount by which import quantities must be deflated or inflated to reach the indifference surface. The properties of this function are discussed and employed to derive systems of inverse demand for imported goods. We illustrate its usefulness by proposing two new parametric forms of trade distance functions. While the trade distance function directly yields Hicksian inverse demand functions of imports, they usually lack closed-form representations in terms of observable variables. This problem, however, need not hinder estimation and could be solved by using the numerical inversion estimation method. Results generally indicate that the suggested modelling and estimation methods are operationally, implying that the trade distance function approach is a promising tool of the empirical analysis of import demands subjected to tight theoretical conditions.     

Segmented labour markets and the estimation of wage functions

This paper uses a stratified random sample of private sector employees to estimate wage equations for both the male labour market as a whole and six ‘segments’ of the labour market, segement which are defined with respect to the industry and size characteristics of the employing establishmen. Highly conventional results are obtained when wage equations are estimated for the male labour market as a whole, or within two segements, comprising almost half the survey population. However, one can reject conclusively the hypothesis that the same estimating equation is appropriate for modelling wage determination in all segments and if a ‘human capital’ (HK) model is, within segements, tested against more appropriate alternative hypotheses for wage determination using the Davidson and Mackinnon (1981) methodology for the testing of non-nested hypotheses, the conclusion is that one should reject the HK specification and not reject the alternative.

It is therefore argued that the disaggregation of labour market data reveals differences in the wage determination process that are not consistent with the view that labour markets are ‘dualistic’ but are consistent with the view that are segmented.     

Measuring technical efficiency and shadow price of water pollutants for the leather industry in India: a directional distance function approach

Journal of Regulatory Economics - This paper measures the cost of reducing pollution from the Kanpur leather industry which is a prime source of pollution in India’s largest river basin of...     

Computation of non-linear continuous optimal growth models: experiments with optimal control algorithms and computer programs

In this paper we discuss the use of optimal control methods for computing non-linear continuous optimal growth models. We have discussed various recently developed algorithms for computing optimal control, involving step-function approximations, Runge–Kutta solutions of differential equations, and we suggest that the discretization approach is preferable to methods which solve first-order optimality conditions. We have surveyed some powerful computer programs by : , and for computing such models numerically. These programs have no substantial optimal growth modelling applications yet, although they have numerous engineering and scientific applications. A computer program named by is developed in this study. Results are reported for computing the Kendrick–Taylor optimal growth model using and programs based on the discretization approach. References are made to the computational experiments with and . The results are used to compare and evaluate mathematical and economic properties, and computing criteria. While several computer packages are available for optimal control problems, they are not always suitable for particular classes of control problems, including some economic growth models. The -based and , however, offer good opportunities for computing continuous optimal growth models. It is argued in this paper, that optimal growth modellers may find that these recently developed algorithms and computer programs are relatively preferable for a large variety of optimal growth modelling studies.     

The model and empirical estimation of the optimal flexibility of RMB exchange-rate regime: A study based on the price-stabilization

Under the Mundell-Fleming-Dornbusch (M-F-D) framework, the paper develops a stochastic model to study the optimal choice of RMB exchange rate regime based on two objectives, namely the exchange rate stabilization and price stabilization. The paper finds that different policy objectives will lead to different optimal choices of RMB exchange-rate regime. If the central bank aims to stabilize the price level, the optimal choice would be a certain type of intermediate regime, or the optimal choice would be a fixed one if it aims to stabilize the RMB exchange rate. Based on the model, the paper empirically estimates China’s open economy parameters and uses them to estimate the optimal flexibility of RMB exchange rate regime. The paper points out that China should allow more exchange rate changes to absorb its foreign exchange market pressure in order to stabilize the general price level, which indicates that China should move toward a more flexible exchange rate regime.     

Estimation of cost functions in a data poor environment: the case of capacity estimation in fisheries

Fisheries economic analysis is often handicapped by the lack of adequate data to undertake robust econometric analyses. In this study, a translog cost function was required to estimate the potential direction of adjustment in a UK fleet segment if a new regulatory regime was introduced. However, the available data were not appropriate for such estimation. Data envelopment analysis (DEA) was used to modify the data subsequently used in the estimation of the long-run cost function. The resulting model appears robust and is consistent with economic theory and the supporting evidence produced using DEA.     

Shadow pricing of labour and capital in an economy with unemployed labour

This paper derives the shadow prices of labour and capital to be used in the public sector in a situation of unemployment. The setting considered is that of a three-good, two-period general equilibrium model. Then shadow prices are compared to their corresponding market prices and shown to closely depend on own and cross-elasticities of supply and demand for labour and investment. In the first part, a rigid wage rate is the sole source of distortion; then, a tax on capital income is introduced so that our formula for the social rate of discount can be contrasted with that of Harberger, Sandmo and Drèze.