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Category Archives: C1

The Impact of Access to Public Transportation on Residential Property Value: A Comparative Analysis of American Cities

By Moses Snow Wayne

This paper develops a consistent model for analyzing the impact of access to public transportation on property value applied to the four cities of Atlanta, Boston, New York, and San Francisco. This study finds a negative relationship between increasing distance to public transit and property value. Additionally, the elicited effects in each city generally align with geographic features and the degree to which a city is monocentric. This study also demonstrates the salience of using actual map-generated distances as proximity measures and characteristics of public
transit systems in modeling the relationship between public transportation and residential property value.

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Advisors: Dr. Patrick Bayer and Kent Kimbrough | JEL Codes: C12, R14, R30, R41

The Effect of Federal Regulations on the Outcomes of Auctions for Oil and Gas Leaseholds

By Artur Shikhaleev

This thesis attempts to analyze the impact of the differences in regulatory frameworks that govern state-owned and federally-owned lands on the outcomes of auctions for oil and natural gas leaseholds in the state of New Mexico. The analysis tries to isolate the effect of ownership by controlling for auction structure, leasehold characteristics, and prices of underlying resources. Given past research, the hypothesis is that stricter regulations carry a heavier cost to buyers, so the expectation is that federally-owned leaseholds, which are more regulated, are traded at a discount to state-owned leaseholds. However, the result of this thesis is contradictory to the hypothesis. The conclusion is that stricter regulations do not lead to a discounted auction price for an oil and gas leasehold.

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Advisor: James Roberts, Kent Kimbrough | JEL Codes: C12, C21, Q35, Q58 | Tagged: Auction, Education, environment, federal, natural gas, Oil, Regulation, State

Dealing with Data: An Empirical Analysis of Bayesian Black-Litterman Model Extensions

By Daniel Roeder

Portfolio Optimization is a common financial econometric application that draws on various types of statistical methods. The goal of portfolio optimization is to determine the ideal allocation of assets to a given set of possible investments. Many optimization models use classical statistical methods, which do not fully account for estimation risk in historical returns or the stochastic nature of future returns. By using a fully Bayesian analysis, however, this analysis is able to account for these aspects and also incorporate a complete information set as a basis for the investment decision. The information set is made up of the market equilibrium, an investor/expert’s personal views, and the historical data on the assets in question. All of these inputs are quantified and Bayesian methods are used to combine them into a succinct portfolio optimization model. For the empirical analysis, the model is tested using monthly return data on stock indices from Australia, Canada, France, Germany, Japan, the U.K.
and the U.S.

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Advisor: Andrew Patton | JEL Codes: C1, C11, C58, G11 | Tagged: Bayesian Analysis Global Markets Mean-Variance Portfolio Optimization

Possibility of Cost Offset in Expanding Health Insurance Coverage: Using Medical Expenditure Panel Survey 2008

By Catherine Moon

The Patient Protection and Affordable Care Act aims to substantially reduce the number of the
uninsured over time and asserts that the financial burden of extending insurance coverage to the
previously uninsured will be offset by the benefit of the attendant improvement in their health.
Motivated by this policy, I explore whether health-insurance status and type affect one’s likelihood of
improving or maintaining health using the Medical Expenditure Panel Survey data. I build a set of
ordered regression models for health-status transitions under the first-order Markov assumption and
estimate it using maximum likelihood estimation. I perform a series of likelihood ratio tests for pooling to determine whether the latent propensity index is the same between adjacent initial health-status groups. Empirical results imply that expanding health care to the unwillingly uninsured due to severe
economic constraints and extending the scope of public insurance to that of private insurance will lead to improvement or maintenance of health for the relatively healthy population, implying the possibility of cost off-set in the expansion of coverage and the extension of scope.

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Advisor: Frank Sloan, Michelle Connolly | JEL Codes: C12, C25, I12, I13, I18 | Tagged: Health Insurance, Health Transition, Ordered Regression Model, Patient Protection and Affordable Care Act (PPACA), Self-Assessed Health Status, Test for Pooling Adjacent Ordinal Categories

Time-Varying Beta: The Heterogeneous Autoregressive Beta Model

By Kunal Jain

Conventional models of volatility estimation do not capture the persistence in high-frequency market data and are not able to limit the impact of market micro-structure noise present at very finely sampled intervals. In an attempt to incorporate these two elements, we use the beta-metric as a proxy for equity-specific volatility and use finely sampled time-varying conditional forecasts estimated using the Heterogeneous Auto-regressive framework to form a predictive beta model. The findings suggest that this predictive beta is better able to capture persistence in financial data and limit the effect of micro-structure noise in high frequency data when compared to the existing benchmarks.

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Advisor: George Tauchen | JEL Codes: C01, C13, C22, C29, C58 | Tagged: Beta, Financial Markets, Heterogeneous Autoregressive, Persistence

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