The Holstein beef sector is a fascinating and integral part of the United States beefsystem; however, it has been largely overlooked in academic research. Holstein beef has longsuffered from perceptions that it is of poor quality. Recent changes in slaughter industrystructure, marketing systems, and production models have made the Holstein systemunbelievably complex. Coupled with econometric modeling, this sector analysis uses a semistructuredinterview approach to evaluate the reality of these perceptions, the impact of thesechanges, and to determine what truly drives the Holstein beef market. Results suggest that manyof the perceptions of Holstein beef are inaccurate; the market for Holstein steers was found to bequite similar to the market for native steers. Recent changes in production systems appear tohave been driven by changes in market preferences. Finally, the driving forces behind theHolstein market are not that different from the driving factors in the native cattle market,although some of the impacts were found to be different.

Cattle producers are faced with difficult decisions on how they market theircalves. This study examines the different characteristics that play a role in determiningthe price of a group of animals. Identifying characteristics that determine pricedifferentials relative to the price premium given to producers participating in CPH salesis important information when producers are making a marketing decision. The modeldeveloped in this study provides producers with evidence of what characteristics generatethe highest price, as well as relative differences between sales locations and types ofsales. The more information available to producers, the better equipped they are to makedecisions.

Air transportation demand forecasting is a core element in aviation planning and policy decision making. NASA Langley Research Center addressed the need of a global forecast model to be integrated into the Transportation Systems Analysis Model (TSAM) to fulfil the vision of the Aeronautics Research Mission Directorate (ARMD) at NASA Headquarters to develop a picture of future demand worldwide. Future forecasts can be performed using a range of techniques depending on the data available and the scope of the forecast. Causal models are widely used as a forecasting tool by looking for relationships between historical demand and variables such as economic and population growth. The Global Demand Model is an econometric regression model that predicts the number of air passenger seats worldwide using the Gross Domestic Product (GDP), population, and airlines market share as the explanatory variables. GDP and Population are converted to 2.5 arc minute individual cell resolution and calculated at the airport level in the geographic area 60 nautical miles around the airport. The global demand model consists of a family of models, each airport is assigned the model that best fits the historical data. The assignment of the model is conducted through an algorithm that uses the R2 as the measure of Goodness-of-Fit in addition to a sanity check for the generated forecasts. The output of the model is the projection of the number of seats offered at each airport for every year up to the year 2040. / Master of Science

Merit-based financial aid has long been utilized by college and university enrollment managers to attract the most academically qualified applicants for admission. Considerable research has been done to illustrate the impact of state-based merit aid programs and other scholarly pursuits have drawn attention to the consequences of merit aid on institutional investments in need-based aid. Less is known about the efficacy of merit aid to achieve college student enrollment objectives. The purpose of this study was to evaluate the relationship between merit aid values and the likelihood of undergraduate student enrollment yield on offers of admission. The primary research question to be answered was: What is the relationship between the amount of merit aid students receive from a college or university and their enrollment decisions? The sample comprised 2,770 students at three private higher education institutions in the United States. Binary logistic regression and a forward selection process were used to test a range of possible predictors (e.g., sex, race, ethnicity, in-state residency, distance from home, academic qualifications, merit aid awards, and information from the financial aid applications of those offered admission) to determine the relative strength of merit aid in the prediction of student enrollment yield on offers of admission. The amount of merit aid offered was positively related to the likelihood of a student to enroll, even when academic qualifications and other student characteristics were controlled.

This thesis investigates the relationship between mode choice and the complexity of trip chaining patterns. An understanding of the causality between these two choice behaviors may aid in the development of tour-based travel demand modeling systems that attempt to incorporate models of trip chaining and mode choice. The relationship between these two aspects of travel behavior is represented in this thesis by considering three different causal structures: one structure in which the trip chaining pattern is determined first and influences mode choice, another structure in which mode choice is determined first and influences the complexity of the trip chaining pattern, and a third structure in which neither is predetermined but both are determined simultaneously. The first two structures are estimated within a recursive bivariate probit modeling framework that accommodates error covariance. The simultaneous logit model is estimated for the third structure that allows a bidirectional simultaneous causality. The analysis and model estimation are performed separately for work tour and non-work tour samples drawn from the 2000 Swiss Microcensus travel survey. Model estimation results show that the causal structure in which trip chaining precedes mode choice performs best for the non-work tour sample. For the work-tour sample, the findings were less conclusive because two causal structures, one in which trip chaining affects mode choice and the other in which both are determined simultaneously, gave virtually identical goodness-of-fit measures. But the structure in which mode choice precedes trip chaining pattern choice gave significantly inferior goodness-of-fit measures for the work tour sample. These findings should be reflected in the development of activity-based and tour-based modeling systems.

Understanding joint and causal relationships among multiple endogenous variables has been of much interest to researchers in the field of activity and travel behavior modeling. Structural equation models have been widely developed for modeling and analyzing the causal relationships among travel time, activity duration, car ownership, trip frequency and activity frequency. In the model, travel time and activity duration are treated as continuous variables, while car ownership, trip frequency and activity frequency as ordered discrete variables. However, many endogenous variables of interest in travel behavior are not continuous or ordered discrete but unordered discrete in nature, such as mode choice, destination choice, trip chaining pattern and time-of-day choice (it can be classified into a few categories such as AM peak, midday, PM peak and off-peak). A modeling methodology with involvement of unordered discrete variables is highly desired for better understanding the causal relationships among these variables. Under this background, the proposed dissertation study will be dedicated into seeking an appropriate modeling methodology which aids in identifying the causal relationships among activity and travel variables including unordered discrete variables. In this dissertation, the proposed modeling methodologies are applied for modeling the causal relationship between three pairs of endogenous variables: trip chaining pattern vs. mode choice, activity timing vs. duration and trip departure time vs.mode choice. The data used for modeling analysis is extracted from Swiss Travel Microcensus 2000. Such models provide us with rigorous criteria in selecting a reasonable application sequence of sub-models in the activity-based travel demand model system.

This thesis develops a new structure that explicitly combines two CGE models, a national and a regional, in an integrated structure that gives the thesis model the name IRES, in short for the Integrated Regional Equation System. The typical features of the integrated structure are the adding-up conditions and the two-way linkages between the national and the regional modules facilitated by the interface shifters. The adding-up conditions ensure the two modules produce consistent results and updated databases. The inclusion of the interface shifters on the one hand plays a role in ensuring compatibility of results of the two modules, i.e. no distortion occurs because technical or taste changes are transferred across modules. On the other hand, the interface shifters assist the operation of IRES in different modes: the model can be used as a top-down model, a bottom-up model or an integrated model where national and regional shocks can be introduced at the same time. Hence, IRES has more flexibility in its application than a regional model or a national model alone, as IRES can make use of availability of data at any levels in the economy. IRES has a new labour market in which regional migration is no longer the only factor that settles the labour market as in the original setting of the MMRF model. Regional unemployment and regional participation rates are modelled to response to changes in regional employment growth using elasticities estimated econometrically in this thesis. IRES implements historical patterns of regional migration so that results of regional migration are consistent with observed patterns. Altogether, regional migration, regional unemployment and participation rates determine the equilibrium of the labour market. IRES adopts new approaches to modelling margin demands and indirect taxes. These new approaches are very effective in reducing the size of IRES but they do not compromise the use of the model. These approaches are readily applicable to any other regional CGE models.

It has long been accepted that changes in monetary policy have real economic effects; however, the mechanism by which these policy changes are transmitted to the real economy has been the subject of much debate. Traditionally the transmission mechanism of monetary policy has consisted of various channels which include the money channel, the asset price channel and the exchange rate channel. Recent developments in economic theory have led to a relatively new channel of policy transmission, termed the credit channel. The credit channel consists of the bank lending channel as well as the balance sheet channel, and focuses on the demand for credit as the variable of interest. The credit channel is based on the notion that demanders and suppliers of credit face asymmetric information problems which create a gap between the cost of external funds and the cost of internally generated funds, referred to as the wedge. The aim here is to determine the size and lag length effects of changes in credit demand, by both firms as well as households, as a result of changes in interest rates. A secondary, but subordinate, aim is to test for a balance sheet channel of monetary policy transmission. A vector autoregressive (VAR) model is used in conjunction with causality tests, impulse response functions and variance decompositions to achieve the stated objectives. Results indicate that the interest rate elasticity of credit demand, for both firms and households, is interest inelastic and therefore the monetary policy authorities have a limited ability to influence credit demand in the short as well as medium term. In light of the second aim, only weak evidence of a balance sheet channel of policy transmission is found.

La presente investigación tiene como objetivo estimar y analizar si la producción de agua embotellada en la Corporación Lindley se lleva a cabo bajo economías de escala y así, en niveles óptimos, con el fin de hacer un uso adecuado de los recursos de la empresa. En el primer capítulo, presentamos fundamentos teóricos y el marco institucional. Específicamente, presentamos información de la empresa, mercados y la industria bajo estudio, también los conceptos de la teoría económica, para propósitos de organización metodológica y análisis empírico. En el segundo capítulo, desarrollamos el problema de estudio, del que derivamos los objetivos y las hipótesis de esta investigación. Este capítulo establece el terreno, tanto para desarrollar el modelo micro-econométrico de los costos de producción, como para su estimación estadística usando el análisis de regresión clásica. En el tercer capítulo, presentamos el modelo estimado y sus principales estadísticas. Puntualmente, encontramos que la Corporación Lindley tendría cómo incrementar su producción de agua embotellada. Técnicamente, ello lo podría llevar a cabo sin tener que incrementar su capacidad instalada. En sus nuevas plantas, construidas en antelación a mayores mercados, parece existir capacidad instalada ociosa. La presencia de economías de escala, nos permiten inferir que la producción de corto plazo en esta empresa sería económicamente sub-óptima, toda vez que la existencia de dichas economías sugiere una producción inconsistente con la maximización de beneficios de largo plazo. Estos hallazgos se presentan en el capítulo cuarto, en el que concluimos nuestro estudio con algunas recomendaciones y una agenda de investigación futura. / The present research aims to estimate and analyze whether the production of bottled water in the Lindley Corporation is carried out under economies of scale and thus, at optimal levels, in order to make adequate use of the company's resources. In the first chapter, we present theoretical foundations and the institutional framework. Specifically, we present information on the company, markets and the industry under study, as well as concepts from economic theory, for purposes of methodological organization and empirical analysis. In the second chapter, we develop the study problem, from which we derive the objectives and hypotheses of this research. This chapter sets the stage, both for developing the micro-econometric model of production costs, and for its statistical estimation using classical regression analysis. In the third chapter, we present the estimated model and its main statistics. Specifically, we found that the Lindley Corporation would have a way to increase its production of bottled water. Technically, this could be done without having to increase its installed capacity. In its new plants, built in advance of larger markets, there seems to be idle installed capacity. The presence of economies of scale allows us to infer that the short-term production in this company would be economically suboptimal, since the existence of these economies suggests a production inconsistent with the maximization of long-term benefits. These findings are presented in the fourth chapter, in which we conclude our study with some recommendations and a future research agenda. / Tesis

In the last two decades, more than half of the countries in the world have introduced a reform process in their power industries and billions of dollars have been spent on liberalizing electricity markets around the world. This thesis presents a doctoral research concerned with the cross-country empirical analysis of the electricity market reforms. The thesis is in three-paper format; that is, we present three independent but related stand-alone papers. The first paper focuses on the impact of power market reforms on electricity price-cost margins and industrial/residential price ratios. It investigates this issue by looking at the impact of the electricity industry reforms on residential and industrial electricity price-cost margins and their effect on industrial/residential price ratios. Using panel data from 63 developed and developing countries covering the period 1982–2009, empirical models are developed and analysed. The results suggest that each individual reform step has different impact on price-cost margins and industrial/residential price ratios for each consumer and country group. That is to say, our findings imply that similar reform steps may have different impacts in different countries, which supports the idea that reform prescription for a specific country cannot easily be transferred to another one with similar success. The second paper explores whether the question of why some countries are able to implement more extensive reforms is closely related to the question of why some countries have better institutions than others. It analyses this question by using an empirical econometric model based on Poisson regression with cross-section data covering 51 states in US, 13 provinces in Canada and 51 other countries. The study concludes that both the background of the chairperson and the minister/governor and institutional endowments of a country are important determinants of how far reforms have gone in a country. Considering the fact that ideological considerations, political composition of governments and educational/professional background of leaders have played and will play a crucial role throughout the reform process; the third paper attempts to discover the impact of political economic variables on the liberalization process in electricity markets. It develops and analyses empirical models using panel data from 55 developed and developing countries covering the period 1975–2010. The results suggest that a portion of the differences in the reform experiences of reforming countries in the past three decades can be explained by differences in the political structure, in the ideology of the government and in the professional and educational backgrounds of the political leaders.