This dissertation looks at models in which agents make decisions under various economic frictions, and examines the role of their preferences. The first two chapters analytically characterize an infinite-horizon open economy under the friction of a stock collateral constraint, whereby borrowing is limited by the value of capital assets available. The model that is considered allows for general subjective discounting of households and fully variable productivity. The third chapter looks at a model of an ambiguity-averse benevolent mediator tasked with choosing a price contract at which a risk neutral buyer and seller transact an indivisible good under the friction of unquantifiable uncertainty of their reservation values. The first chapter establishes that it is possible for households to enjoy the allocation they would obtain absent a stock collateral constraint under a condition that relates to their patience; this condition requires a long-run depression when agents are impatient relative to the market, and allows for an economic expansion when agents are more patient relative to the market. When this condition is not met, households are tightly constrained at least once and experience debt deleveraging in all periods and deflation of asset prices in periods preceding the constrained period relative to their unconstrained allocation. Households also ration their consumption more when they expect to be more tightly constrained in the future. The second chapter is a sequel to the first chapter and shows that under constant output, agents who are impatient relative to the market can face two and three-period cycles in consumption, debt, and asset prices. Further, large initial debt can lead to multiple equilibria. The third chapter considers a mediator who plays a Stackelberg game against Nature to maximize the distributionally worst-case expected weighted Nash product subject to known mean and boundary constraints on buyer and seller reservation values. We study the role of bargaining power and show that relative to what the buyer and seller themselves would choose when equipped with the mediator's information, the mediator's price contract has a shallow dependency on bargaining power, which is only exacerbated by the possibility of dependent buyer and seller values. Comparative statics results are obtained.

Emerging insurance markets, have limited access to financial instruments that they can use to create common hedge(s) to manage foreign exchange risk. This is the first empirical study to focus on the limitations when modelling foreign exchange rate transaction exposure in emerging insurance markets. This work is based on the cash flow methodology proposed by Martin and Mauer (2003, 2005) in reference to banks, and employed by Li et al. (2009) when assessing US insurance companies. Some econometric methodological innovations have been introduced to study the limitations of modelling foreign exchange rate transaction exposure in emerging insurance markets. An extensive literature review is followed by a quantitative investigation, to answer the following research questions. 1) Is the foreign exchange transaction exposure, as measured by a fundamental (economic) method of modelling the interplay of foreign exchange rates with other economic variables, significant, for all Egyptian insurance companies? 2) Is the foreign exchange transaction exposure, as measured by a technical (statistical) way of modelling the interplay of foreign exchange rates with other economic variables, significant for all Egyptian insurance companies? 3) Is the exchange transaction exposure for the Egyptian insurance industry, as a whole, significant? Although the foreign exchange rate transaction exposure for the Egyptian insurance industry, as a whole, is insignificant (question3), the percentage of Egyptian insurers affected by foreign exchange rate transaction exposure in US dollars, estimated at the individual firm level, was found to be 22% (question 1) and 35% (question2) respectively.

This dissertation studies statistical decision making in various guises. I start by providing a general decision theoretic model of statistical behavior, and then analyze two particular instances which fit in that framework. Chapter 1 studies statistical decision theory (SDT), a class of models pioneered by Abraham Wald to analyze how agents use data when making decisions under uncertainty. Despite its prominence in information economics and econometrics, SDT has not been given formal choice-theoretic or behavioral foundations. This chapter axiomatizes preferences over decision rules and experiments for a broad class of SDT models. The axioms show how certain seemingly-natural decision rules are incompatible with this broad class of SDT models. Using those representation result, I then develop a methodology to translate axioms from classical decision-theory, a la Anscombe and Aumann (1963), to the SDT framework. The usefulness of this toolkit is then illustrated by translating various classical axioms, which serve to refine my baseline framework into more specific statistical decision theoretic models, some of which are novel to SDT. I also discuss foundations for SDT under other kinds of choice data. Chapter 2 studies statistical identifiability of finite mixture models. If a model is not identifiable, multiple combinations of its parameters can lead to the same observed distribution of the data, which greatly complicates, if not invalidates, causal inference based on the model. High-dimensional latent parameter models, which include finite mixtures, are widely used in economics, but are only guaranteed to be identifiable under specific conditions. Since these conditions are usually stated in terms of the hidden parameters of the model, they are seldom testable using noisy data. This chapter provides a condition which, when imposed on the directly observable mixture distribution, guarantees that a finite mixture model is non-parametrically identifiable. Since the condition relates to an observable quantity, it can be used to devise a statistical test of identification for the model. Thus I propose a Bayesian test of whether the model is close to being identified, which the econometrician may apply before estimating the parameters of the model. I also show that, when the model is identifiable, approximate non-negative matrix factorization provides a consistent, likelihood-free estimator of mixture weights. Chapter 3 studies the robustness of pricing strategies when a firm is uncertain about the distribution of consumers' willingness-to-pay. When the firm has access to data to estimate this distribution, a simple strategy is to implement the mechanism that is optimal for the estimated distribution. We find that such an empirically optimal mechanism boasts strong profit and regret guarantees. Moreover, we provide a toolkit to evaluate the robustness properties of different mechanisms, showing how to consistently estimate and conduct valid inference on the profit generated by any one mechanism, which enables one to evaluate and compare their probabilistic revenue guarantees.

This dissertation conducted an extensive examination of dockless e-scooter dynamics using high-resolution trip data from Austin, Texas. Four studies were conducted to capture the multifaceted nature of e-scooter operations and demand. The first study aimed to identify and quantify the influence of contributing factors affecting e-scooter demand by partitioning the data by time period for weekdays and weekends. Utilizing a joint panel linear regression (JPLR) model, significant associations were observed between e-scooter demand and variables such as sociodemographic attributes, transportation infrastructure, land use, meteorological attributes, and situational factors. The second study shifted focus to shared e-scooter origin-destination (OD) flows in the urban region. By employing a joint binary logit-fractional split model, e-scooter OD flows were analyzed, emphasizing variations across distinct time periods and the subsequent implications for e-scooter deployment and rebalancing strategies. The third study delved into e-scooter utilization efficiency, introducing a time-to-book (TtB) measure. Through a Mixed Grouped Ordered Logit (MGOL) model, the study highlighted variations between regular and peak weeks, offering operators a chance to enhance fleet utilization. The final study addressed the broader context of the e-scooter industry, investigating the impact of the COVID-19 pandemic. By analyzing datasets spanning January 2019 through December 2021, a spatial approach illuminated changes in e-scooter demand patterns before, during, and after the pandemic, highlighting the effects of COVID-19-related factors and vaccine attributes on e-scooter trends. These collective insights from the four studies provide valuable contributions to understanding and enhancing e-scooter operations in urban landscapes

Internet Service Providers, or ISPs, like any other rational entity make decisions to maximize their profit. While some of their decisions are on how to attract customers, they inevitably need to control how much resources consumers utilize. In this dissertation, we focus on two different aspects of ISP's decisions, including bandwidth allocation and pricing techniques through which ISPs manage allotting their limited capacity to users with high demand, and zero-rating, which can be one of the tools through which the ISP can attract customers. For bandwidth allocation, this dissertation discusses the data plans available for each user's monthly billing cycle. Within those, the ISPs guarantee a fixed amount of data at high rates until a byte threshold is reached, at which point the user's data rate is throttled to a lower rate for the remainder of the cycle. In practice, the thresholds and rates of throttling can appear and may be somewhat arbitrary. In this dissertation, we evaluate the choice of threshold and rate as an optimization problem (regret minimization) and demonstrate that intuitive formulations of client regret, which preserve desirable fairness properties, lead to optimization problems that have tractably computable solutions. For zero-rating options in the ISP market, and their relation to net neutrality, we begin by introducing the concept of zero-rating, which refers to the practice of providing free Internet access to some users under certain conditions, and usually concurs with differentiation among users or content providers. Even though zero-rating is banned in some countries (India, Canada), others have either taken no stance or explicitly allowed it (South Africa, Kenya, U.S.). While there is broad agreement that preserving the content quality of service falls under the purview of net neutrality, the role of differential pricing, especially the practice of \emph{zero-rating} remains controversial. An objective of net neutrality is to design regulations for the Internet and ensure that it remains a public, open platform where innovations can thrive. We show the practice of zero-rating does not agree with that. This dissertation shows how ISPs could make zero-rating decisions to attract customers, and then show how these decisions may negatively impact the market and customer welfare, which necessitates the existence of some zero-rating regulations.

New Zealand experienced dramatic restructuring programs after the Labor party won the national elections in 1984. Deregulation of price controls, removal of the log export ban, and privatization of public assets were the main shocks to the forest sector. The purpose of this paper is to analyze the impacts of these reforms on wood and paper industry cost, production, and cost efficiency. Unlike previous work, the effects of privatization and deregulation are compared to determine which shock had the most influence on the forest sector. Results show that production decreased, total cost increased, and cost efficiency decreased after deregulation for the sector, and that deregulation was more significant than privatization for the wood and paper sectors. In particular, removal of the log export ban had the greatest impact, while privatization had little effect on industry production and cost. This suggests that countries with comparative advantages in wood processing who implement deregulation or privatization may suffer through a short term period of lower cost efficiency as the economy adjusts to higher input costs in those sectors. In New Zealand's case, the adjustments most likely affecting efficiency have been investments in new technologies, which require time to attain maximum efficiency. The results are contrary to other studies that have predicted increased efficiency as a result of privatization. / Ph. D.

This dissertation presents an empirical analysis of United States monetary policy over the period ranging from January 1973 to December 1985. Switching regressions are introduced as a method of allowing for discrete shifts in the coefficients of reduced form money supply equations. Two switching models are presented. The first is a monetary policy reaction function using a switching mechanism developed by Goldfeld and Quardt. In this first model, the probability that a particular set of coefficients, or monetary rule, was in place in each sample month is determined by the level of a set of important economic aggregates. The second specification is a time series model in which M-1 money may follow one of two possible autoregressive processes in any given period. The particular process followed in each period is modeled as the outcome of a first order Markov process. Maximum likelihood estimates of each model are presented and interpreted. The results indicate that there were two periods of substantial monetary instability during this sample period. The first period approximately begins in April 1973 and ends in February 1975. The second period corresponds closely to the set of months between the Fed's October 1979 and October 1982 changes in operating procedure. Results from the Goldfeld-Quandt model also show that the levels of four macroeconomic series, the interest rate, the inflation rate, the unemployment rate, and the trade weighted value of the dollar, may be used as indicators of the monetary rule being employed by the Federal Reserve. / Ph. D.

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

Forest and fuel wood are fundamental as sources of energy in almost all developing economies. However there are a few empirical studies addressing the issue of fuelwood production and consumption for rural households. In this paper, household fuelwood use behavior is empirically assessed and policy implications are drawn. with specific reference to Nepal's tarai and mid-hills. Fuelwood production, supply and demand functions are estimated using market, forest and access, and demographic variables characteristic of each region. Both regional and district level supply and demand elasticities are also estimated with respect to opportunity cost of labor, fuelwood price, income, resource stock and access, and demographic variables. The results suggest that rural households produce and consume fuelwood according to the opportunity cost of their labor and market fuel wood prices. Market( economic) variables such as fuel wood price and opportunity costs of labor along with forest stock and its access can provide better insights for assessing household responsiveness to forestry and related development activities and for policy than mere resource stock size or its access. / Master of Science

The adoption of Electric Vehicles (EVs) represents a transformative change in the automotive industry. As more households make the transition to EVs, the traditional dependence on fossil fuels for transportation is being replaced by electricity as the primary energy source. This transition has the potential to increase the electricity consumption within households as well as the demand on the power grids. To maximize the environmental and economic benefits of EV adoption, strategies regarding efficient energy management, integration of renewable energy source, and grid capacity are becoming essential considerations. The current dissertation research is motivated toward evaluating the adoption of EV and its impact on the US household’s energy demand. In pursuit of these goals, this research has made several contributions. First, we proposed an econometric framework to estimate the factors influencing customers’ vehicle purchase decisions. Second, a comparative analysis is conducted between two econometric frameworks –panel mixed random utility maximization MNL model and panel mixed random regret minimization MNL model to estimate the evolving landscape of EV adoption over time. Third, we employed an advanced econometric framework- Multiple Discrete Continuous Extreme Value (MDCEV) model– to evaluate the factors that influence the energy consumption profile of various household end-uses along with their alternating trends over time. Fourth, we employed a novel fusion approach to the MDCEV model to assess the impact of travel behavior along with several household socioeconomic characteristics on various energy end-uses. Finally, by predicting household EV ownership, projections of total household energy demand for the city of Atlanta for the years 2030, 2040 and 2050 are performed. A set of independent variables including vehicle attributes, socio-economic attributes, travel behavior-related attributes, dwelling attributes, appliance-use related attributes, and climate-related attributes from various data sources are employed in this study. The research concludes with an analysis of different policy implications.