Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

In this paper recent developments in the estimation of frontier functions and the measurement of efficiency are surveyed, and the potential applicability of these methods in agricultural economics is discussed. Frontier production, cost and profit functions are discussed, along with the construction of technical, allocative, scale and overall efficiency measures relative to these estimated frontiers. The two primary methods of frontier estimation, econometric and linear programming, are compared. A survey of recent applications of frontier methods in agriculture is also provided. (This abstract was borrowed from another version of this item.)

Recent Developments in Frontier Modelling and Efficiency Measurement

The efficiency of the container port industry has been variously studied utilising either Data Envelopment Analysis (DEA) or Stochastic Frontier Analysis (SFA). Given the strengths and weaknesses associated with these two approaches, the efficiency estimates and scale properties derived from these analyses are not always convincing. This paper applies both approaches to the same set of container port data for the world’s largest container ports and compares the results obtained. A high degree of correlation is found between the efficiency estimates derived from all the models applied, suggesting that results are relatively robust to the DEA models applied or the distributional assumptions under SFA. High levels of technical efficiency are associated with scale, greater private-sector participation and with transhipment as opposed to gateway ports. In analysing the implications of the results for management and policy makers, a number of shortcomings of applying a cross-sectional approach to an industry characterised by significant, lumpy and risky investments are identified and the potential benefits of a dynamic analysis, based on panel data, are enumerated.

The technical efficiency of container ports: Comparing data envelopment analysis and stochastic frontier analysis

This study surveys the data envelopment analysis (DEA) literature by applying a citation-based approach. The main goals are to find a set of papers playing the central role in DEA development and to discover the latest active DEA subareas. A directional network is constructed based on citation relationships among academic papers. After assigning an importance index to each link in the citation network, main DEA development paths emerge. We examine various types of main paths, including local main path, global main path, and multiple main paths. The analysis result suggests, as expected, that Charnes et al. (1978) [Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research 1978; 2(6): 429–444] is the most influential DEA paper. The five most active DEA subareas in recent years are identified; among them the “two-stage contextual factor evaluation framework” is relatively more active. Aside from the main path analysis, we summarize basic statistics on DEA journals and researchers. A growth curve analysis hints that the DEA literature’s size will eventually grow to at least double the size of the existing literature.

Data envelopment analysis 1978-2010: A citation-based literature survey

Economic Theory and Operations Analysis.

Cost overruns in road construction--what are their sizes and determinants?

Toll financing in Norway: The success, the failures and perspectives for the future

This paper focuses on the performance of Norwegian bus companies subsidized by the government. The performance is evaluated from a productive efficiency point of view. The framework is that of a deterministic non-parametric Data Envelopment Analysis (DEA) approach to efficiency measurement. In this context several important issues are addressed: efficiency rankings, distribution and scale properties in the bus industry, potentials for efficiency improvements in the sector, the impact of ownership, area of operation and scope, and ways of improving efficiency in the sector. The findings show that the average bus company exhibits increasing return to scale in production of its services. The extent of such returns however vary, with size and is more prevalent among smaller companies. The average bus company is found to have a considerable input saving potential of about 28 percent. Neither economies of scope nor company ownership are found to have an influence on company performance. It is suggested that geographical factors need a closer attention in future research. The implications of DEA results are discussed and concluding remarks offered.

Evaluating efficiency in the Norwegian bus industry using data envelopment analysis

Previous applications of data envelopment analysis (DEA) and its subsequent Malmquist indices to efficiency and productivity measurements have been criticised for not providing statistical inferences regarding the significance of observed results. In this paper, DEA and a Malmquist index are combined with a bootstrap method in order to provide succinct statistical inferences that determine the performance of grain producers in Eastern Norway. The data cover the period between 1987 and 1997. Results reveal: (i) a significant degree of inefficiency (approximately 11%) and an average productivity progress of 38% over the period considered; (ii) the formidable productivity progress observed is primarily explained by technical efficiency changes that enabled producers to catch up with front runners; and (iii) environmental factors, such as weather conditions, impact both efficiency and productivity. Finally, the analysis reveals that using bootstrapping to make statistical inferences suggests that researchers should be careful in making performance comparisons based on conventional DEA methods, as any discovered differences may not be significant.

Statistical precision of DEA and Malmquist indices: A bootstrap application to Norwegian grain producers

Norway has been named the “capital” of Electric Vehicles (EVs) because the purchase and use of EVs in Norway has increased tremendously over the last few years. Currently, the fleet of EVs in Norway is the largest per capita in the world. From a transportation research perspective, the questions immediately asked are (i) what economic incentives make the purchase and use of EVs in Norway so attractive to road users; (ii) do these incentives have any adverse effects and, if so, how large are they; and (iii) how does the marginal external cost of EVs compare to that of conventional vehicles. We explore the above questions using available data, the literature and personal observations while relating to the city of Oslo as a case study. We find that the tremendous increase in the use of EVs is the result of multiple economic incentives, such as exemption from toll charges, exemption from purchase duties and permission to use transit lanes that induce road users to purchase and use EVs. The increase in EVs has led to a reduction in CO2 emissions. However, some of the EV incentives have adverse effects, the most serious of which is the exemption from toll charges, which has led to a sizable loss of toll revenue. We find that the marginal external cost of EVs’ road use is approximately the same as that for a conventional vehicle. The incentives for EVs should consider the adverse effects and how electricity is produced; the Norwegian approach should not be followed by other countries without due consideration of these factors.

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James Odeck

Papers

Cost overruns in road construction--what are their sizes and determinants?

Toll financing in Norway: The success, the failures and perspectives for the future

Evaluating efficiency in the Norwegian bus industry using data envelopment analysis

Statistical precision of DEA and Malmquist indices: A bootstrap application to Norwegian grain producers

The increase of electric vehicle usage in Norway—incentives and adverse effects