Browsing by Author "Sizova, Natalia M."
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Item Essays on Productivity and Panel Data Econometrics(2014-03-24) Liu, Junrong; Sickles, Robin C.; Sizova, Natalia M.; Scott, David W.There are four essays on productivity and Panel data econometrics in this dissertation, with the first two essays on empirical research and the last two more focused on theory improvement. The first chapter is study of productivity and efficiency in the Mexican Energy Industry. The second chapter analyzes the productivity and efficiency of U.S. largest banks productivity and efficiency. The third incorporates a Bayesian treatment to two different panel data models. The last chapter introduces a semi-nonparametric method in panel data models. These four chapters have been developed into four working papers. They are Liu et al. (2011), Inanoglu et al. (2012), Liu et al. (2013) and Liu et al. (2014). The first chapter studies the optimizing behavior of Pemex by estimating a cost model of Pemex's production of energy. The estimation using duality between the cost and production function is undertaken, which facilitates the specification. This approach makes it convenient to find the cost shares under different levels of returns to scale. The results indicate the presence of substantial distortions in cost shares. That would be brought back to equilibrium were the Mexican government willing to allow more foreign investment in its energy extraction industry and thus increase the capital use and decrease the labor use. The second chapter utilizes a suite of panel data models in order to examine the extent to which scale economy and efficiencies exist in the largest U.S. banks. The empirical results are assessed based on the consensus among the findings from the various econometric treatments and models. This empirical study is based on a newly developed dataset based on Call Reports from the FDIC for the period 1994-2013. The analyses point to a number of conclusions. First, despite rapid growth over the last 20 years, the largest surviving banks in the U.S. have decreased their level of efficiency as they took on increasing levels of risk (credit, market and liquidity). Second, no measurable scale economies and scope economies are found across our host of models and econometric treatments. In addition to the broad policy implications, this essay also provides an array of econometric techniques, findings from which can be combined to provide a set of robust consensus-based conclusions that can be a valuable analytical tool for supervisors and others involved in the regulatory oversight of financial institutions. The third chapter considers two models for uncovering information about technical change in large heterogeneous panels. The first is a panel data model with nonparametric time effects. The second is a panel data model with common factors whose number is unknown and whose effects are firm-specific. This chapter proposes a Bayesian approach to estimate the two models. Bayesian inference techniques organized around MCMC are applied to implement the models. Monte Carlo experiments are performed to examine the finite-sample performance of this approach and have shown that the method proposed is comparable to the recently proposed estimator of Kneip et al. (2012) (KSS) and dominates a variety of estimators that rely on parametric assumptions. In order to illustrate the new method, the Bayesian approach has been applied to the analysis of efficiency trends in the U.S. largest banks using a dataset based on the Call Report data from FDIC over the period from 1990 to 2009. The fourth chapter introduces a new estimation method under the framework of the stochastic frontier production model. The noise term is assumed to have the traditional normal density but the inefficiency term is spanned by Laguerre Polynomials. This method is a Semi-nonparametric method and follows the spirit of Gallant and Nychka (1987). Finite sample performance of this estimator is shown to dominate the nonparametric estimators via Monte Carlo simulations.Item Essays on Treatment Effects Evaluation(2012-09-05) Guo, Ronghua; Sickles, Robin C.; Sizova, Natalia M.; Scott, David W.The first chapter uses the propensity score matching method to measure the average impact of insurance on health service utilization in terms of office-based physician visits, total number of reported visits to hospital outpatient departments, and emergency room visits. Four matching algorithms are employed to match propensity scores. The results show that insurance significantly increases office-based physician visits, and its impacts on reported visits to hospital outpatient departments and emergency room visits are positive, but not significant. This implies that physician offices will receive a substantial increase in demand if universal insurance is imposed. Government will need to allocate more resources to physician offices relative to outpatient or emergency room services in the case of universal insurance in order to accommodate the increased demand. The second chapter studies the sensitivity of propensity score matching methods to different estimation methods. Traditionally, parametric models, such as logit and probit, are used to estimate propensity score. Current technology allows us to use computationally intensive methods, either semiparametric or nonparametric, to estimate it. We use the Monte Carlo experimental method to investigate the sensitivity of the treatment effect to different propensity score estimation models under the unconfoundedness assumption. The results show that the average treatment effect on the treated (ATT) estimates are insensitive to the estimation methods when index function for treatment is linear, but logit and probit model do better jobs when the index function is nonlinear. The third chapter proposes a Cross-Sectionally Varying (CVC) Coefficient method to approximate individual treatment effects with nonexperimental data, the distribution of treatment effects, the average treatment effect on the treated and the average treatment effect. The CVC method reparameterizes the outcome of no treatment and the treatment effect in terms of observable variables, and uses these observables together with a Bayesian estimator of their coefficients to approximate individual treatment effects. Monte Carlo simulations demonstrate the efficacy and applicability of the proposed estimator. This method is applied to two datasets: data from the U.S. Job Training Partnership ACT (JTPA) program and a dataset that contains firms’ seasoned equity offerings and operating performances.Item Unknown The Cost of Security: Foreign Policy Concessions and Military Alliances(2012-09-05) Johnson, Jesse; Leeds, Brett Ashley; Fang, Songying; Sizova, Natalia M.One way states can mitigate external threats is by entering into military alliances. However, threatened states are reluctant to enter into military alliances because alliance membership can require significant policy concessions. An important and unanswered question is: when will states be willing to make policy concessions in exchange for military alliances? This is the question that is investigated in this project. To address this question I develop a simple three actor bargaining model of alliance formation that endogenizes both external threat and policy concessions. I test the model's implications with two sets of large N analyses and find strong support for the hypotheses. The first set of empirical analyses uses a novel research design that takes into account the attributes of challengers to evaluate states' alliance formation decisions. The second set is based on the same research design and provides one of the first analyses of foreign policy concessions among alliance members. The results suggest that threatened states are willing to make more concessions in exchange for an alliance when they are unlikely to defeat their challengers alone and when their allies have a large effect on their probability of defeating their challengers. This research highlights both the security and non-security motivations for alliance formation and demonstrates that alliances have important influences beyond international security.