Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2013. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 145-147). / This dissertation is a collection of three essays on hospital response to regulation and risk preferences. Chapter 1 analyzes the Medicare Flex Program which allowed rural hospitals with fewer than 25 beds to convert to "Critical Access Hospital" status and receive cost-based Medicare reimbursement. Converting hospitals decrease their inpatient capacity by 30 percent on average to satisfy the bed requirement. They also drop services such as obstetrics, intensive care, and inpatient and outpatient surgery. Medicaid days fall by almost 30 percent at converting hospitals. The results suggest a 6 percent increase in neonatal mortality for high-risk babies delivered by mothers residing in counties with a hospital conversion. There is no significant effect on neonatal mortality among all births. Chapter 2 (co-authored with Liran Einav, Amy Finkelstein and Mark Cullen) analyzes the extent to which individuals' choices over five employer-provided insurance coverage decisions and one 401(k) investment decision exhibit systematic patterns, as would be implied by a general utility component of risk preferences. We provide evidence consistent with an important domain-general component that operates across all insurance choices. We find a considerably weaker relationship between one's insurance decisions and 401(k) asset allocation, although this relationship appears larger for more "financially sophisticated" individuals. Estimates from a stylized coverage choice model suggest that up to thirty percent of our sample makes choices that may be consistent across all six domains. Chapter 3 analyzes the effect of a California regulation mandating maximum patient-to-nurse ratios for inpatient hospital units. I compare changes in inputs for hospitals for which the legislation was more or less binding, based on the initial patient-to-nurse ratio. Hospitals with higher baseline ratios decrease patient-to-nurse ratios by substituting toward licensed nurses, rather than decreasing patient days or lengths of stay. There is suggestive evidence of substitution away from aides and orderlies. The skill ratio of licensed nurses decreases slightly as hospitals hire relatively more licensed vocational nurses. Total costs increase in hospitals with higher baseline ratios. These results are slightly smaller and no longer significant in robustness checks. Estimating these effects on a sample of unaffected units generally finds small, insignificant effects. / by Iuliana Pascu. / Ph.D.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Economics, 2002. / Includes bibliographical references. / This thesis investigates individual decision-making in response to government policies, in particular, state lotteries and the welfare "family cap." Despite considerable controversy surrounding the use of state lotteries as a means of public finance, little is known about their consumer consequences. Chapter one investigates two central questions about state lotteries and consumer behavior. First, do state lotteries primarily crowd out other forms of gambling, or do they crowd out non-gambling consumption? Second, does consumer demand for lottery games respond to expected returns, as maximizing behavior predicts, or do consumers appear to be misinformed about the risks and returns of lottery gambles? Analyses of multiple sources of micro-level gambling data demonstrate that lottery spending does not substitute for other forms of gambling. Household consumption data suggest that household lottery gambling crowds out approximately $43 per month, or two percent, of other household consumption, with larger proportional reductions among low-income households. Demand for lottery products responds positively to the expected value of the gamble, controlling for other moments of the gamble and product characteristics. This suggests that consumers of lottery products are not misinformed and are perhaps making fully-informed purchases. Chapter two investigates the nature of consumer choice under risk in the context of state lottery betting. Economists have traditionally modeled consumer preferences according to expected utility theory, but a recent body of literature challenges this model. / (cont.) An empirical test of the expected utility hypothesis finds that, in general, it is a reasonable description of observed consumer choices. However, the data offer some evidence in support of non-linear probability weighting in consumer preferences. The second application studied in this thesis is welfare reform. A number of states have recently instituted family cap policies, under which women who conceive a child while receiving cash assistance are not entitled to additional cash benefits. Chapter three investigates how fertility behavior responds to this change in government expenditure policy. The analysis takes advantage of the variation across states in the timing of family cap implementation to determine if these policies are discouraging women from having additional births. The data consistently demonstrate that the family cap does not lead to a reduction in births. This finding of no effect is robust to the incorporation of lead and lag effects, to considering separately total and higher-order births, and to limiting the sample to demographic groups with high welfare propensities. / by Melissa Schettini Kearney. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2007. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Includes bibliographical references. / This dissertation develops new econometric procedures for the analysis of high-dimensional datasets commonly encountered in finance, macroeconomics or industrial organization. First, I show that traditional approaches to the estimation of latent factors in financial data underestimate the number of risk factors. They are also biased towards a single market factor, the importance of which is overestimated in samples. In Chapter 3, I derive a new consistent procedure for the estimation of the number of latent factors by examining the effect of the idiosyncratic noise in a factor model. Furthermore, I show that the estimation of factor loadings by Principal Components Analysis is inconsistent for weak factors and suggest alternative Instrumental Variables procedures. Chapter 4 uses the theoretical results of the earlier chapters to estimate the stochastic dimension of the US economy and shows that global risk factors may obfuscate the relationship between inflation and unemployment. Chapter 5 (co-authored with Jerry Hausman) suggests a new procedure for the estimation of discrete choice models with random coe±cients and shows that ignoring individual taste heterogeneity can lead to misleading policy counterfactuals. / by Matthew C. Harding. / Ph.D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 2015. / Title as it appears in MIT Commencement Exercises program, June 5, 2015: Essays in high-dimensional econometrics and model selection. Cataloged from PDF version of thesis. / Includes bibliographical references. / This dissertation consists of three chapters. Chapter 1 proposes a new method to solve the many moment problem: in Generalized Method of Moments (GMM), when the number of moment conditions is comparable to or larger than the sample size, the traditional methods lead to biased estimators. We propose a LASSO based selection procedure in order to choose the informative moments and then, using the selected moments, conduct optimal GMM. My method can significantly reduce the bias of the optimal GMM estimator while retaining most of the information in the full set of moments. We establish theoretical asymptotics of the LASSO and post-LASSO estimators. The formulation of LASSO is a convex optimization problem and thus the computational cost is low compared to all existing alternative moment selection procedures. We propose penalty terms using data-driven methods, of which the calculation is carried out by a non-trivial adaptive algorithm. In Chapter 2, we consider partially identified models with many inequalities. Under such circumstances, existing inference procedures may break down asymptotically and are computationally difficult to conduct. We first propose a combinatorial method to select the informative inequalities in the Core Determining Class problem, in which a large set of linear inequalities are generated from a bipartite graph. Our method selects the set of irredudant inequalities and outperforms all existing methods in shrinking the number of inequalities and computational speed. We further consider a more general problem with many linear inequalities. We propose an inequality selection method similar to the Dantzig selector. We establish theoretical results of such a selection method under our sparsity assumptions. Chapter 3 proposes an innovative way of reporting results in empirical analysis of economic data. Instead of reporting the Average Partial Effect, we propose to report multiple effects sorted in increasing order, as an alternative and more complete summary measure of the heterogeneity in the model. We established asymptotics and inference for such a procedure via functional delta method. Numerical examples and an empirical application to female labor supply using data from the 1980 U.S. Census illustrate the performance of our methods in finite samples. / by Ye Luo. / Chapter 1. Chapter 2. Chapter 3. Selecting informative moments via LASSO -- Core determining class : construction, approximation, and inference -- Summarizing partial effects beyond averages. / Ph. D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 1995. / Includes bibliographical references (leaves 146-151). / by August Joseph Baker. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 1995. / Includes bibliographical references (leaves 94-95). / by Marcello de Moura Estevão Filho. / Ph.D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 2014. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 219-228). / Economic researchers and policymakers need reliable tools both to estimate economic relationships and to measure the uncertainty surrounding their estimates. Unfortunately, economic data sometimes contains limited information useful for estimating relationships of interest. In such cases, the statistical techniques commonly used in applied economics can break down and fail to accurately reflect the level of uncertainty present in the data. If they rely on such tools, researchers and policymakers may come away with serious misconceptions about the precision and reliability of their estimates. Econometricians refer to models where the lack of information in the data causes common statistical techniques to break down as weakly identified. In this thesis, I examine several questions relating to weak identification. In the first chapter, I introduce the class of conditional linear combination tests. These tests control size under weak identification and have a number of optimality properties in a conditional problem. I suggest using minimax regret conditional linear combination tests and propose a computationally tractable class of tests that plug in an estimator for a nuisance parameter. In the second chapter, I consider the problem of detecting weak identification. When weak identification is a concern researchers frequently calculate confidence sets in two steps, first assessing the strength of identification and then deciding whether to use an identification-robust confidence set. Two-step procedures of this sort may generate highly misleading confidence sets, and I demonstrate that two-step confidence sets based on the first stage F-statistic can have extremely poor coverage in linear instrumental variables models with heteroskedastic errors. I introduce a simple approach to detecting weak identification and constructing two-step confidence sets which controls coverage distortions. In the third chapter, joint with Anna Mikusheva, we consider minimum distance statistics and show that in a broad class of models the problem of testing under weak identification is closely related to the problem of testing a "curved null" in a finite-sample Gaussian model. Using the curvature of the model, we develop new finite-sample bounds on the distribution of minimum-distance statistics, which we show can be used to detect weak identification and to construct tests robust to weak identification. / by Isaiah Smith Andrews. / Ph. D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 1997. / Includes bibliographical references (leaf 103). / by Alia Karaouni. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 1996. / Includes bibliographical references (leaves 167-173). / by Rodrigo O. Valdés. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 1993. / Includes bibliographical references (leaves 134-137). / by Douglas A. Galbi. / Ph.D.