PhD, University of Cincinnati, 2017, Business: Business Administration

This dissertation contains three essays examining new Bayesian econometric methodologies. The first develops a heterogeneous Spatial Autoregressive Model by integrating a finite mixture model structure into the traditional homogeneous specification. The second essay builds upon the first by extending this Spatial Mixture Model structure to the more general Spatial Durbin and Spatial Durbin Error specifications. Additionally, this essay covers the interpretation of these new model specifications. Finally, the third essay develops a predictive based model selection process by integrating cross-validation algorithms into standard Bayesian sampling methods with a focus on explicit out-of-sample prediction. 1.0.1 Embracing Heterogeneity: The Spatial Autoregressive Mixture Model In this essay, a mixture distribution model is extended to include spatial dependence of the autoregressive type. The resulting model nests both spatial heterogeneity and spatial dependence as special cases. A data generation process is outlined that incorporates both a finite mixture of normal distributions and spatial dependence. Whether group assignment is completely random by nature or displays some locational "pattern", the proposed spatial-mix estimation procedure is always able to recover the true parameters. As an illustration, a basic hedonic price model is investigated that includes sub-groups of data with heterogeneous coefficients in addition to spatially clustered elements. 1.0.2 Spatial Durbin Mixture Models This essay extends the finite mixture model structure to include Spatial Durbin and Spatial Durbin Error model specifications. The partial derivatives of this heterogeneous spatial model structure are shown to differ between border and interior agents; the designation of which is based on group assignment and first order neighbor designation. As an illustration, individual income based on data from the Panel Study of Income Dynamics (PSID) is examined using the Spatial (open full item for complete abstract)

Committee: Olivier Parent Ph.D. (Committee Chair); David Curry Ph.D. (Committee Member); James LeSage Ph.D. (Committee Member); Jeffrey Mills Ph.D. (Committee Member) Subjects: Economics

Master of Arts, The Ohio State University, 1967, Graduate School

Committee: Not Provided (Other) Subjects:

Doctor of Philosophy, University of Toledo, 2023, Spatially Integrated Social Science

The COVID-19 pandemic ranks as the deadliest on the list of disasters in the United States. After community transmission was confirmed in early 2020, federal and state governments introduced varying restrictions on public mobility through various Non-Pharmaceutical interventions (NPIs) in response to the growing pandemic. Through a spatial and temporal perspective, this study aims to investigate the impact of changing mobility patterns, NPI measures, and local weather on the spread of COVID-19 during the early years of the pandemic at the county level. The primary goals of the dissertation were 1) identify the influence of different mobility categories, policy indices, and county-specific weekly average temperature data on COVID-19 case positive rate between 2020-2020 by employing spatial analysis and econometric modeling. 2) leverage the identified spatiotemporal relationship to develop a spatial panel data weekly predictive model for COVID-19 case positivity at the county level and publish an ArcGIS online dashboard. Considering the diverse nature of mobility and NPIs, this study incorporates five different mobility categories and two different measures of policy stringency index and county-specific weekly average temperature data. From 2020 onwards, twelve pandemic phases were progressively evaluated for 104 weeks using Spatial-Autoregressive & Spatially Autocorrelated Errors Fixed Effect Panel Data Models for 2380 counties. The spatial spillover effects on how each county was influenced by its neighbors were also evaluated. Results revealed a positive correlation between all the outdoor mobility categories and COVID-19 case positivity with varying levels of confidence at different times during the pandemic, except for parks and recreational visits, which demonstrated a negative correlation. Policy indices of different containment measures and economic supports exhibited negative correlations, indicating the association between lower policy index value and highe (open full item for complete abstract)

Committee: Kevin Czajkowski (Committee Chair); Bhuiyan Alam (Committee Member); Sujata Shetty (Committee Member); Barbara Saltzman (Committee Member); April Ames (Committee Member); Yanqing Xu (Committee Member) Subjects: Epidemiology; Geographic Information Science; Geography; Public Health; Statistics

Master of Arts, Miami University, 2023, Economics

Energy markets are often thought of as a strong predictor of the economy's overall health, especially in political discourse. The primary energy sources for the U.S. are oil and natural gas. Both prices are viewed as very volatile which has been particularly noticeable during the COVID-19 pandemic and with the escalation of the Russian-Ukrainian war. In this paper, I look at recent energy price changes and argue that they increase the fit of sample fitting for the returns of the Dow Jones Industrial Index. Then I test for volatility clustering using Engle's Lagrange Multiplier Test and find statistically significant evidence for volatility clustering. With this information, I further improve the models for returns to Dow Jones by using ARCH and GARCH models, with augmentations for lagged values of energy prices. Through this, I find that only oil prices have statistically significant negative effects on the returns of Dow Jones.

Committee: Jing Li (Advisor); Nam Vu (Committee Member); David Lindequist (Committee Member) Subjects: Economics; Energy; Finance

Doctor of Philosophy, The Ohio State University, 2022, Business Administration

This dissertation addresses the relative importance of price and non-price criteria in sourcing decisions from three distinct perspectives. Each essay is motivated by the same problem: that organizations tend to unintentionally overweight cost minimization objectives in their sourcing decisions. In the first of three essays, I show that excessively price-based decision-making is a widespread problem in sourcing. To do this, I combined two sources of data on contract awards by the US federal government. I applied coarsened exact matching to identify cases where contracts were awarded using different criteria in similar situations. I then used logistic regression to show that when non-price criteria are weighted more heavily, the same contractor is more likely to receive awards for similar work in the future. This relationship is absent when there is a requirement for the decision-maker to provide written justification for the use of the more price-based approach, allowing me to infer a solution to the problem identified. In the second essay, I investigate whether the procurement profession's identity influences the relative importance of price in supplier selection decisions. I first conducted a series of semi-structured interviews with current practitioners, eliciting their comments on: their level of identification with the procurement profession; procurement's group image; others' perceptions of procurement's group image; and, procurement's status within their organization. Drawing from the observed variation in responses, I designed and conducted a scenario-based experiment. I find that strong identification with the procurement profession can contribute to more price-based sourcing decisions. In the third essay, I expand my focus from procurement professionals to a broader set of professions that commonly contribute to sourcing decisions: supply management, engineering, and marketing. Seeking to understand how these different perspectives influence (open full item for complete abstract)

Committee: John Gray (Advisor); James Hill (Advisor); Christian Blanco (Committee Member) Subjects: Business Administration; Management; Operations Research

Master of Arts in Economics, Cleveland State University, 2022, College of Liberal Arts and Social Sciences

I examine changes in Medicaid coverage and insurance status surrounding a work requirement policy implemented within the Arkansas Medicaid demonstration waiver. The policy applied to able-bodied, childless adults, aged 30 to 49, not enrolled as students, and was effective from 2018 to 2019. Eligibility was conditional on policy compliance. Taking a sample from the IPUMS American Community Survey database, I use triple-differences modeling to compare Arkansans subject to the policy with unaffected Arkansans and individuals from a set of control states. I find that the policy pilot group in Arkansas was less likely to be insured or have Medicaid coverage in the two years after the work requirement took effect, compared with controls. In 2018 and 2019 respectively, I estimate increases in uninsurance for the pilot group, compared with non-pilot Arkansans, were 7.3 and 10.8 percentage points greater than those experienced by the hypothetical pilot and non-pilot groups from the control states. Similarly, I estimate declines in Medicaid coverage for pilot versus non-pilot-group Arkansans were 6.2 and 10.2 percentage points greater in magnitude, compared with the hypothetical pilot and non-pilot groups from the control states in 2018 and 2019 respectively. In tandem with a series of robustness checks, I outline how asymmetric information, unobservable government intervention, and contemporaneous policies could affect my results.

Committee: Phuong Ngo (Committee Chair); Aycan Grossmann (Committee Member); Bill Kosteas (Committee Member) Subjects: Economic Theory; Economics; Health Care

Master of Arts, Miami University, 2022, Economics

We examine good-level, geographic and exporter behavior in exchange rate pass-through (ERPT). Evidence suggests that our principal focus, the durable good, encounters magnified pass-through effects due to its production duration and consumer behavior. Using a large panel of city-good-level retail price data for Japan, we implement the Arellano-Bond estimator across types of goods and Japan's leading trade partners. The estimates reinforce the theory of magnified pass-through in durables and reasonably mirror the expectations for differences among exporters. Exploiting the high dispersion of sample cities, we produce OLS estimates of city-good pass-through for geographic heterogeneity analysis. Despite the intuition that coastal or port cities encounter heightened ERPT, we find minimal evidence of heterogeneity across various sets of goods.

Committee: Nam Vu (Advisor); David Lindequist (Committee Member); Kimberly Berg (Committee Member) Subjects: Economics; Finance

Doctor of Philosophy, University of Toledo, 2021, Spatially Integrated Social Science

This dissertation bridges the gap between spatial econometrics and machine learning under the theoretical banner of spatial data science. Methodologically, it uses the spatial error model, spatial lag model, and the randomForest algorithm in order to predict Human Development Index (HDI) values within Morocco at the commune scale. This prediction task is done using the Moroccan censuses of 2004 and 2014. The results of this process show that randomForest can outperform the traditional spatial econometric models in terms of numeric accuracy within this specific case. Since spatial thinkers are just as concerned with spatial accuracy as they are with numeric accuracy, post-estimation procedures were developed in order to assess the spatial accuracy of the spatial error model, spatial lag model, and randomForest in the Moroccan case. These post-estimation procedures were developed for both the global and local levels. In both cases, it is shown that randomForest outperforms both of the spatial econometric models in terms of spatial accuracy within the Moroccan case. With the Morocco specific results complete, the dissertation moves to simulated data experiments in order to assess different properties of randomForest vs. the spatial lag model, and randomForest vs. the spatial error model. The simulation experiments are carried out using five different data generation processes. Throughout the experiments bias, consistency, efficiency, and spatial prediction performance are evaluated and compared. These experiments show that when either the spatial lag model or spatial error model are the correct model specification, randomForest is unable to outperform either of them in terms of bias, consistency, efficiency, or spatial prediction performance. Therefore, it is concluded that if randomForest does outperform the traditional spatial econometric models, as happened in the Moroccan case, neither the spatial lag model nor the spatial error model are the correct m (open full item for complete abstract)

Committee: Oleg Smirnov Dr. (Advisor); Neil Reid Dr. (Committee Member); Sujata Shetty Dr. (Committee Member); David Nemeth Dr. (Committee Member); Jack Kalpakian Dr. (Committee Member) Subjects: Geographic Information Science; Geography

Master of Arts, Miami University, 2020, Economics

Previous economic and political studies have struggled to explain the diffusion of suffrage to women in the United States. Western states tended to enfranchise women earlier in history relative to other states. The factors that led to female enfranchisement are not as well understood as with many other social movements. By analyzing the timing of suffrage extension across the US, we offer a comprehensive view of the contributing forces that led to women gaining the vote. While previous hypotheses demand some merit, none of them have been empirically grouped and tested against each other. The high number of males relative to females in the West seemed to spur the transitions toward suffrage, but closer analysis reveals legislative difficulty to enacting new laws, the relative strength of liquor interests, and particular effectiveness of suffragists may have played more substantial roles in suffrage extension patterns.

Committee: Melissa Thomasson (Advisor); Austin Smith (Committee Member); Greg Niemesh (Committee Member) Subjects: Economic History; Economics

Master of Arts, University of Toledo, 2019, Economics

A 5.2% assessed value premium is estimated for homes with an unobstructed view of a natural meadow floodplain in an affluent residential community in Northwest Ohio.

Committee: Kevin Egan PhD (Committee Chair); Kristen Keith PhD (Committee Member); Yanqing Xu PhD (Committee Member) Subjects: Economics; Environmental Economics

Doctor of Philosophy, University of Toledo, 2018, Spatially Integrated Social Science

This dissertation is concerned with the spatial dynamics of the U.S. economy. Spatial dynamics is a termcoined in this dissertation to define the geo-spatial aspects of an observed natural process, particularly changes in its spatial relations over time. Geographic inquiry considering spatial dynamics requires an unassuming examination of spatial panel data, an approach that facilitates the discovery of new regularities and tendencies in spatial data and necessitates the development of more flexible tools and methods tailored to the peculiarities of the observed natural process. This dissertation demonstrates the practicality of spatial dynamics as a promising framework with the discovery, description, and analysis of two spatial economic paradoxes, which impelled the creation of several new tools and methods. The dissertation is composed of three essays linked by the exploration and analysis of the spatial dynamics of the U.S. economy, specifically its metropolitan statistical areas (MSAs). The first essay develops two new statistics that quantify physical and human capital accumulation in MSAs. These statistics are used to calculate the classical production function and derive the percent contribution of physical and human capital to average establishment size and Gross Domestic Product by MSA (MGDP). The results conformtomacroeconomic expectations and are spatially distributed according to the familiar economic geography of the United States, rendering the statistics usefulfor spatial economic analysis. The second essay explores the observation that MGDP growth rates are spatially clustered and MGDP levels are uniformly distributed (i.e., exhibit no spatial correlation). This finding is paradoxical because the level of economic activity is the aggregation of previous growth patterns and, if economic growth in the spatial economy is persistently clustered, the location of economic activity should follow the same pattern. The essay seeks (open full item for complete abstract)

Committee: Oleg Smirnov Dr. (Committee Chair); Olugbenga Ajilore Dr. (Committee Member); Peter S. Lindquist Dr. (Committee Member); David J. Nemeth Dr. (Committee Member); Neil Reid Dr. (Committee Member) Subjects: Economics; Geography; Regional Studies; Statistics

Master of Arts, University of Toledo, 2017, Economics

Little is known about the interaction of spatial dependent models and random decision forests. Most previous research has not implemented modern machine learning techinques with economics let alone spatial econometrics. In this paper we apply random forest analysis with a spatial dependent component to hedonic pricing models. This paper sought to improve parameter identification, prediction performance, and bridge the gap between spatial economics and machine learning. The data provided details 45,381 residential real estate sales in Lucas County, Ohio between 2001-2016. Evaluation by log-linear and spatial log-linear models shows that random forests can make comparatively accurate model predictions using less indicators than models selected by conventional methods. While the spatially dependent random forest models did not produce the lowest root mean square error compared to the spatially dependent models, reducing the number of parameters by 35\% only marginally increased error compared to other models. The results have implications for improving understanding of components used real estate appraisal as well as construction or investment.

Committee: Oleg Smirnov (Committee Chair); Aliaksandr Amialchuk (Committee Member); Kristen Keith (Committee Member) Subjects: Economics

Doctor of Philosophy, The Ohio State University, 2017, Economics

This dissertation consists of four research papers. Three of these papers are based on the analysis the Hodrick-Prescott (HP) filter which is a commonly used technique to extract the trend from a time series in macroeconomics, while the last paper introduces a monitoring procedure to detect a change from spurious regression to cointegration. In the first paper, we derive a new representation of the transformation of the data that is implied by the HP filter. This representation allows us to carry out a rigorous analysis of the properties of the HP filter without using the ARMA based approximation that has been used in the previous literature. In the second paper, we introduce a new property of the HP filter that has not been discovered before. When the trend is extracted from the original time series, the remaining series is called the cyclical component. The new property suggests that the cyclical component is approximately the trend in the fourth difference of the original series. We formalize this approximation by correcting it for the begin and end points of the sample. This property allows us to analyze the properties of the cyclical component when the original series has a linear trend break or is integrated of order up to 4. The third paper approaches the HP filter from frequency domain approach, unlike the first two papers. Since the results of the previous literature are based on the spectral properties of a procedure that is only an approximation to the HP filter, in this paper, we formalize the conjectures that are provided in the literature. The last paper introduces a monitoring procedure to detect a structural break that changes the relation between two integrated time series. It is well-known that two integrated series are highly correlated, while the causality between these series is not obvious. Cointegration is a term that describes the existence of causality between two integrated series. The null hypothesis of the monitoring procedure is that the (open full item for complete abstract)

Committee: Robert de Jong Ph.D. (Advisor) Subjects: Economics

Doctor of Philosophy, The Ohio State University, 1986, Graduate School

Committee: Not Provided (Other) Subjects: Economics

Doctor of Philosophy, The Ohio State University, 1982, Graduate School

Committee: Not Provided (Other) Subjects: Economics

Doctor of Philosophy, The Ohio State University, 1981, Graduate School

Committee: Not Provided (Other) Subjects: Economics

Doctor of Philosophy, The Ohio State University, 1969, Graduate School

Committee: Not Provided (Other) Subjects: Economics

Doctor of Philosophy, The Ohio State University, 1968, Graduate School

Committee: Not Provided (Other) Subjects: Economics

Doctor of Philosophy, The Ohio State University, 2016, Economics

Spatial econometrics has been obtained more and more attention in the recent years. The spatial autoregressive (SAR) model is one of the most widely used and studied models in spatial econometrics. So far, most studies have been focused on linear SAR models. However, some types of spatial or network data, for example, censored data or discrete choice data, are very common and useful, but not suitable to study by a linear SAR model. That is why I study an SAR Tobit model and an SAR binary choice model in this dissertation. Chapter 1 studies a Tobit model with spatial autoregressive interactions. We consider the maximum likelihood estimation (MLE) for this model and analyze asymptotic properties of the estimator based on the spatial near-epoch dependence (NED) of the dependent variable process generated from the model structure. We show that the MLE is consistent and asymptotically normally distributed. Monte Carlo experiments are performed to verify finite sample properties of the estimator. Chapter 2 extends the MLE estimation of the SAR Tobit model studied in Chapter 1 to distribution-free estimation. We examine the sieve MLE of the model, where the disturbances are i.i.d. with an unknown distribution. This model can be applied to spatial econometrics and social networks when data are censored. We show that related variables are spatial NED. An important contribution of this chapter is that I develop some exponential inequalities for spatial NED random fields, which are also useful in other semiparametric studies when spatial correlation exists. With these inequalities, we establish the consistency of the estimator. Asymptotic distributions of structural parameters of the model are derived from a functional central limit theorem and projection. Simulations show that the sieve MLE can improve the finite sample performance upon misspecified normal MLEs, in terms of reduction in the bias and standard deviation. As an empirical application, we examine the school (open full item for complete abstract)

Committee: Lung-fei Lee (Advisor); Jason Blevins (Committee Member); Robert de Jong (Committee Member) Subjects: Economics

Doctor of Philosophy, The Ohio State University, 2015, Agricultural, Environmental and Developmental Economics

This dissertation consists of three chapters: the implications of environmental policy on nutrient outputs in agricultural watersheds; an assessment of the effects of global wood biomass demand projections on forests using the Global Timber Model (GTM), including heterogeneous products in the forestry sector; and the analysis of whether forest-based bioenergy is carbon neutral using the Monte Carlo analysis with the Global Timber Model (GTM). The first chapter examines whether the federally sponsored voluntary environmental programs to reduce phosphorus pollution from agriculture have had any impact on water quality outcomes. Using daily observations on nutrient emissions taken over 37 years in two Great Lakes tributaries, we estimate an econometric model of phosphorus emissions. Phosphorus emissions are the most important contributor to harmful algal blooms, which have recently caused significant health concerns. Our results indicate that these voluntary programs have had very little effect on phosphorus outputs. In contrast, we show that an input tax could be effective in reducing phosphorus pollution, and consequently, the likelihood of future harmful algal blooms. The second chapter uses the Global Timber Model (GTM) to analyze global biomass demand projection scenarios. The current literature in the Global Timber Model lacks implications of diverse utilizations in forests, assuming a homogeneous product of woody use. In this chapter, the model maximizes the present value of net social welfare derived from harvesting and managing the world's forests and assumes that the timber market consists of two heterogeneous goods: saw-timber and pulpwood. A functioning market for cellulosic biomass does not yet exist; however, we assume that either type of wood is an available feedstock for production of cellulosic bioenergy on the global scale, and that it can be substituted for the purposes of making ethanol or other energy, such as electricity and heat. A baseli (open full item for complete abstract)

Committee: Brent Sohngen (Advisor); Ian Sheldon (Committee Member); Abdoul Sam (Committee Member) Subjects: Environmental Economics