Economic Efficiency and Frontier Techniques

Abstract. Most of the literature related to the measurement of economic efficiency has based its analysis either on parametric or on non‐parametric frontier methods. The choice of estimation method has been an issue of debate, with some researchers preferring the parametric and others the non‐parametric approach. The aim of this paper is to provide a critical and detailed review of both core frontier methods. In our opinion, no approach is strictly preferable to any other. Moreover, a careful consideration of their main advantages and disadvantages, of the data set utilized, and of the intrinsic characteristics of the framework under analysis will help us in the correct implementation of these techniques. Recent developments in frontier techniques and economic efficiency measurement such as Bayesian techniques, bootstrapping, duality theory and the analysis of sampling asymptotic properties are also considered in this paper.     

Efficient frontier estimation: a maximum entropy approach

An alternative efficiency estimation approach is developed utilizing generalized maximum entropy (GME). GME combines the strengths of both SFA and DEA, allowing for the estimation of a frontier that is stochastic, without making an ad hoc assumption about the distribution of the efficiency component. GME results approach SFA results as the one-sided inefficiency bounds used by GME shrink. Results similar to DEA are achieved as the bounds increase. The GME results are distributed like DEA, but yield virtually the same rankings as SFA. The results suggest that GME may provide a link between various estimators of efficiency.

RSSR机构的ADAMS参数化建模及优化技术研究

为对空间RSSR四杆机构进行优化设计,以RSSR机构为研究对象,运用解析法对RSSR机构进行了运动学分析,得出RSSR机构的运动解析式,确定曲柄存在条件的表达式;利用ADAMS软件参数化编程技术,将空间四杆机构关键点参数化,构建三维参数化模型;运用ADAMS软件的二次开发技术,开发了空间RSSR四杆机构的参数化建模及分析优化系统,实现了RSSR机构运动类型的判定、快速建模、机构仿真、运动分析及优化,并进行了实例分析。结果表明,对于给定的RSSR机构,系统运动分析优化后,摇杆的角速度、角加速度的最大值和最小值的绝对值明显减小,摇杆摆动速度趋于平稳。研究结果对提高RSSR机构的设计效率、减少设计周期具有参考价值。     

Robust prediction of domain compositions from uncertain data using isometric logratio transformations in a penalized multivariate Fay–Herriot model

Assessing regional population compositions is an important task in many research fields. Small area estimation with generalized linear mixed models marks a powerful tool for this purpose. However, the method has limitations in practice. When the data are subject to measurement errors, small area models produce inefficient or biased results since they cannot account for data uncertainty. This is particularly problematic for composition prediction, since generalized linear mixed models often rely on approximate likelihood inference. Obtained predictions are not reliable. We propose a robust multivariate Fay–Herriot model to solve these issues. It combines compositional data analysis with robust optimization theory. The nonlinear estimation of compositions is restated as a linear problem through isometric logratio transformations. Robust model parameter estimation is performed via penalized maximum likelihood. A robust best predictor is derived. Simulations are conducted to demonstrate the effectiveness of the approach. An application to alcohol consumption in Germany is provided.     

Optimizing time-series forecasts for inflation and interest rates using simulation and model averaging

Motivated by economic-theory concepts – the Fisher hypothesis and the theory of the term structure – we consider a small set of simple bivariate closed-loop time-series models for the prediction of price inflation and of long- and short-term interest rates. The set includes vector autoregressions (VAR) in levels and in differences, a cointegrated VAR and a non-linear VAR with threshold cointegration based on data from Germany, Japan, UK and the US. Following a traditional comparative evaluation of predictive accuracy, we subject all structures to a mutual validation using parametric bootstrapping. Ultimately, we utilize the recently developed technique of Mallows model averaging to explore the potential of improving upon the predictions through combinations. While the simulations confirm the traded wisdom that VARs in differences optimize one-step prediction and that error correction helps at larger horizons, the model-averaging experiments point at problems in allotting an adequate penalty for the complexity of candidate models.     

Do Urban Neighborhoods Benefit from Homeowners? Evidence from Housing Prices

Homeownership is heavily subsidized in most countries. The adverse effects of this policy on economic efficiency and income distribution are well documented in the economics literature. The main argument in favor of subsidizing homeownership is that it creates positive externalities that offset these adverse effects. In this paper, we test whether homeowners create positive externalities that capitalize into housing prices in multi‐storey buildings. Using semi‐parametric hedonic regressions with and without instrumental variables, we find no evidence of positive externalities, although the results with instrumental variables are somewhat imprecise. This result is robust to several sensitivity checks and to a relaxation of the identification assumptions of our instrument using set identification.     

Diversification strategies and firm performance in Vietnam

This paper is based upon the assumption that a firm's profitability is determined by its degree of diversification which is, in turn, strongly related to the antecedent decision to carry out diversification activities. This calls for an empirical approach that permits the joint analysis of the three interrelated and consecutive stages of the overall diversification process: diversification decision, degree of diversification and outcome of diversification. We apply parametric and semi‐parametric approaches to control for sample selection and the endogeneity of the diversification decision in both static and dynamic models. For the analysis, we use the census dataset on the whole firm population in Vietnam, as a representative of transition countries. After controlling for industry fixed‐effects, the empirical evidence from the firm‐level data shows that diversification has a curvilinear effect on profitability: it improves firms’ profit up to a point, after which a further increase in diversification is associated with declining performance. This implies that firms should consider optimal levels of product diversification when they expand their product offerings beyond their core business. Other noteworthy findings include the following: (i) the factors that stimulate firms to diversify do not necessarily encourage them to extend their diversification strategy; (ii) firms that are endowed with highly technological resources and innovation investment are likely to successfully exploit diversification as an engine of growth; and (iii) while industry performance does not have a strong influence on the profitability of firms, it impacts their diversification decision as well as the degree of diversification.     

THE PREDICTABILITY OF STOCK MARKET RETURNS IN SOUTH AFRICA: PARAMETRIC VS. NON‐PARAMETRIC METHODS

This paper compares the forecasting performance of a sub‐class of univariate parametric and non‐parametric models in predicting stock market returns in South Africa. To account for conditional heteroskedasticity in stock returns data, the non‐parametric model is generated by the conditional heteroskedastic non‐linear autoregressive (NAR) model, while the parametric model is produced by the generalised autoregressive conditional heteroskedastic in mean (GARCH‐M) model. The results of the paper show that the NAR as a non‐parametric model performs better than the GARCH‐M model in short‐term forecasting horizon, and this indicates the importance of a distribution‐free model in predicting stock returns in South Africa.     

R&D subsidies and private R&D expenditures: evidence from Italian manufacturing data

This paper uses a comprehensive firm level data set for the manufacturing sector in Italy to investigate the effect of government support on privately financed R&D expenditure. Estimates from a non‐parametric matching procedure suggest that public assistance has a positive effect on private R&D investment in the sense that the recipient firms achieve more private R&D than they would have without public support. This indicates that the possibility of perfect crowding out between private and public funds can be rejected. Furthermore, in this sample of Italian firms, tax incentives appear to be more effective than direct grants. The paper also examines whether public funding affects the financial sources available for R&D and finds that grants encourage the use of internal sources. The results also show some evidence of positive effects on credit financing for R&D.