abstract: This dissertation applies the Bayesian approach as a method to improve the estimation efficiency of existing econometric tools. The first chapter suggests the Continuous Choice Bayesian (CCB) estimator which combines the Bayesian approach with the Continuous Choice (CC) estimator suggested by Imai and Keane (2004). Using simulation study, I provide two important findings. First, the CC estimator clearly has better finite sample properties compared to a frequently used Discrete Choice (DC) estimator. Second, the CCB estimator has better estimation efficiency when data size is relatively small and it still retains the advantage of the CC estimator over the DC estimator. The second chapter estimates baseball's managerial efficiency using a stochastic frontier function with the Bayesian approach. When I apply a stochastic frontier model to baseball panel data, the difficult part is that dataset often has a small number of periods, which result in large estimation variance. To overcome this problem, I apply the Bayesian approach to a stochastic frontier analysis. I compare the confidence interval of efficiencies from the Bayesian estimator with the classical frequentist confidence interval. Simulation results show that when I use the Bayesian approach, I achieve smaller estimation variance while I do not lose any reliability in a point estimation. Then, I apply the Bayesian stochastic frontier analysis to answer some interesting questions in baseball. / Dissertation/Thesis / Doctoral Dissertation Economics 2014

About 75% of human food in the 21st century consists of just 12 crops, though specific crops vary among nations. Modern technology has allowed development of innovative food and non-food uses for these commodities. For instance, corn (maize (Zea mays L.)) is produced for many purposes, including food, livestock feed, biofuels, fiber for clothing, etcetera. Scientists project the human population will reach 9.2 billion in next 20 years—an 18% increase from the 2020 population of 7.8 billion—resulting in increased demand for corn and other crops. Hence, farmers must increase total crop production to meet demand; however, local agricultural resource endowments such as climate, land and water availability, and soil attributes constrain production. Perhaps the quickest yield and efficiency improvements will result from farm management practices that tailor input applications to match accurate seasonal weather forecasts. Regional seasonal weather forecasts would enable farmers to optimize yields by reducing yield risk from extreme weather events, as well as from less extreme inter-annual weather variability. Improved productive efficiency is also critical to reducing environmental harms, e.g. contaminated runoff from excessive agricultural input use. The objective of this dissertation is to estimate the corn yield frontier and efficiency measures based on agricultural input management and weather. This research contributes to an enhanced understanding of how the corn yield frontier responds to inter-annual weather variations, and how it may shift with climate change. The first chapter summarizes three main topics—farm technology, climate change and weather variability, and methods for evaluating production efficiency. The second presents estimated corn yield frontiers and efficiency measures based on stochastic frontier and data envelopment analyses for nine North Dakota Agricultural Statistics Districts from 1994 to 2018. The third presents corn yield efficiency measures for five states: Minnesota, North Dakota, Nebraska, South Dakota, and Wisconsin from 1994 to 2018. The results reveal the major causes of inter-annual yield variation are variability of rainfall and temperature. Development of accurate growing-season weather forecasts is likely to result in high value-added for farmers and downstream agribusinesses. Federal, state, and private research funding in seasonal weather forecasting would probably be well invested.

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The thesis examines technical efficiency using frontier efficiency estimation techniques from parametric and non-parametric approaches. Five different frontier efficiency estimation techniques are considered which are SFA, DFA, DEA-CCR, DEA-BCC and DEA-RAM. These techniques are then used on an artificially generated panel dataset using a two-input two-output production function framework based on characteristics of German life-insurers. The key contribution of the thesis is firstly, a study that uses simulated panel dataset to estimate frontier efficiency techniques and secondly, a research framework that compares multiple frontier efficiency techniques across parametric and non-parametric approaches in the context of simulated panel data. The findings suggest that, as opposed to previous studies, parametric and non-parametric approaches can both generate comparable technical efficiency scores with simulated data. Moreover, techniques from parametric approaches, i.e. SFA and DFA are consistent with each other whereas the same applies to non-parametric approaches, i.e. DEA models. The research study also discusses some important theoretical and methodological implication of the findings and suggests some ways whereby future research can enable to overcome some of the restrictions associated with current approaches.

This dissertation examines multiple state and local expenditure categories in the United States to expand understanding of fiscal federalism and spatial interactions among governments. First, the author investigates the relationship between police expenditures and crime rates from a spatial perspective. Both police expenditures and crime rates in one state are found to exhibit a similar pattern to that in neighboring states. Spatial correlation is also detected between police expenditures and crime rates. As police of neighbors in fact deter crime at home, there are positive externalities present among the states. Second, the author conducts new tests on the Leviathan hypothesis, i.e., more competition, smaller government. While cost efficiency is used in place of government size to capture the idea that fiscal decentralization reduces wasteful expenditures, spatial interaction is taken as another measure for decentralization. The hypothesis is supported by some evidence from total, police, highway, and welfare expenditures.

Attainment of greater efficiency in hospital operations has become a goal highly sought after as a result of several factors including skyrocketing costs. The possibility that the different incentives associated with ownership type might affect efficiency has been covered thoroughly in the literature. There are numerous studies comparing for-profit to not-for-profit hospitals or public to private hospitals. Analysis of federal ownership, however, has been less studied. In particular, comparisons involving military hospitals are non-existent, attributed to data availability and an assumption that military hospitals are too different from civilian facilities. This dissertation employs a cross-sectional Stochastic Frontier Analysis (“SFA”) of 2006 data to compare the technical efficiency of military, for-profit, not-for-profit, and other government hospitals, controlling for differences in patients, scope of work, physicianhospital working arrangements, and other structural characteristics. Four model specifications are examined, varying the method of accounting for heterogeneity of case mix. One of the specifications uses a distance function technique to allow for specific inclusion of multiple outputs, namely inpatient and outpatient workload. Results obtained using SFA are validated using Data Envelopment Analysis (“DEA”) and compared with results produced through simple ratio analysis. Estimates of overall technical efficiency ranged from 76% to 80%. The analysis found no significant correlation between ownership category and technical efficiency. Factors found to be significantly correlated with greater technical efficiency include younger average patient age, more female patients, percentage of surgical inpatient work, percentage of circulatory system-based work, accreditation, and having all credentialed physicians (i.e. no physician employees). Pooled-vs.-partitioned analysis showed that military hospitals are indeed different, but not enough to render comparisons meaningless. Data Envelopment Analysis produced comparable individual hospital efficiency scores (correlations of approximately 0.6 between like specifications using SFA and DEA) and comparable average efficiency (~87%). Ratio analysis results were sensitive to the specific ratio analyzed. This dissertation adds to the body of literature on the relationship between ownership and hospital technical efficiency. It is the first comparison of military and civilian hospital technical efficiency.

Since the open door policy was embarked upon in 1979, China s banking sector has undergone gradual but notable reforms. A key objective of the reforms implemented by the Chinese government is to build an effective, competitive and stable banking system in order to improve its efficiency and reliability. This study employs both parametric stochastic frontier analysis (SFA) and non-parametric data envelopment analysis (DEA) methods to assess and evaluate the cost efficiency of Chinese banks over the period from 1994 until 2007, a period characterised by far-reaching changes brought about by the banking reforms. To this end, we first compare a number of specifications of stochastic cost frontier models to determine the preferred frontier model which are adopted in our efficiency analysis. The preferred model specification for our sample is the one stage SFA model that includes the traditional input prices, the outputs and the control variables (that is, equity, non-performing loans and the time trend) in the cost frontier and the environmental variables (that is, ownership structure, size, deregulation, market structure and market discipline) in the inefficiency term. Moreover, we also employ two cost DEA models (traditional DEA and New DEA) as a complement to the preferred SFA model for methodological cross-checking purposes. Similar to the previous empirical literature, we find that in most cases only moderate consistency across the different techniques. The cost efficiency of Chinese banks is found to be 91% on average, based on our SFA model, over the period from 1994 until 2007. Based on the results of the DEA and New DEA models, the average cost efficiency for Chinese banks over the sample period is about 89% and 87%, respectively. We find that Chinese banking efficiency has deteriorated after China s admission to the WTO, suggesting that the significant external environmental changes which arose from China s WTO entry may have had a negative impact on its banking efficiency. In addition, we find that the majority of Chinese banks reveal scale inefficiencies and as asset size increases, banks tend to pass from increasing, to constant, and then to decreasing returns to scale. Our findings also show that both state-owned banks and foreign banks are more efficient than domestic private banks and larger banks tend to be relatively more efficient than smaller banks. These and other results suggest that in order to enhance Chinese banking efficiency, the government needs to continue with the banking reform process and in particular, to open up banking markets, to improve risk management and corporate governance in Chinese banks and to encourage the expansion of banks.

This study analyzes the technical efficiencies of Turkish electricity distribution companies (21 in total) throughout 2002 and 2009. For this aim, we used six different model specifications, all of which are generated from two different Stochastic Frontier Analysis (SFA) models (Battese ve Coelli (1992&amp / 1995)). At the end of the estimations of the models, it has been seen that the signs and significance levels of the coefficient estimations are very consistent and satisfactory in all models. We also observed consistency between the coefficient estimations of the different models despite the differences in the magnitudes of the coefficient estimations. For example, all model specifications confirm the presence of increasing returns to scale and of a mild technological progress over time in the market. In addition, among the inputs, all inputs except the quality of the electricity delivered are important in enhancing technical efficiency of the electricity distribution companies, according to the all alternative specifications. Again, all models showed that inefficiency effects rather than random error effects are of crucial importance in Turkish electricity distribution market. As for the efficiency estimations of the alternative models, the main conclusion revealed by our study is that efficiency estimations of the Battese ve Coelli (1995) models are remarkably higher than those of the Battese ve Coelli (1992) models. The efficiency estimation differences between Battese and Coelli (1992&amp / 1995) models can be attributed to the environmental variables included into the Battese ve Coelli (1995) models, which are not generally controlled by electricity distribution companies.

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From the idea of efficiency raised by Koopmans in 1951, and the panel data first introduced into the efficiency analysis by Pitt and Lee (1981) and Schmidt and Sickles (1984), the techniques of stochastic frontier analysis are fast developed and the applications of stochastic frontier are widely used in different areas, such as education, industry and hospital. But most researchers focus on only one aspect, either the development of new models or empirical applications. This thesis attempts to fill the gap to get a general idea of the properties of different panel data stochastic frontier models, on both statistical aspects and economic aspects, by the comparison of different models applied to different production applications. The thesis is also attempt to shed light on whether particular panel data stochastic frontier models are better suited to different data sets. The models selected capture the simplest situation, with no heterogeneity or heteroscedasticity, and complicated ones, with exogenous variables included in the models. Not only the classical models, such as the Pitt and Lee (1981) and Battese and Coelli (1992.1995), but also the new developed models, such as the latent class model and fixed management model are detected in the thesis. On the economic aspect, the data selected captures both microeconomic and macroeconomic, with the application to the World GDP and the Italian manufacturing industry. The results show that: the panel data stochastic frontier models perform better on the microeconomic level than on the macroeconomic level; the classical models perform better than the new developed ones; some panel data stochastic frontier models make ideal assumptions but the requirements to the dataset are hard to achieve; that the influence from the exogenous variables is quite strong.