abstract: Schennach (2007) has shown that the Empirical Likelihood (EL) estimator may not be asymptotically normal when a misspecified model is estimated. This problem occurs because the empirical probabilities of individual observations are restricted to be positive. I find that even the EL estimator computed without the restriction can fail to be asymptotically normal for misspecified models if the sample moments weighted by unrestricted empirical probabilities do not have finite population moments. As a remedy for this problem, I propose a group of alternative estimators which I refer to as modified EL (MEL) estimators. For correctly specified models, these estimators have the same higher order asymptotic properties as the EL estimator. The MEL estimators are obtained by the Generalized Method of Moments (GMM) applied to an exactly identified model. The simulation results provide promising evidence for these estimators. In the second chapter, I introduce an alternative group of estimators to the Generalized Empirical Likelihood (GEL) family. The new group is constructed by employing demeaned moment functions in the objective function while using the original moment functions in the constraints. This designation modifies the higher-order properties of estimators. I refer to these new estimators as Demeaned Generalized Empirical Likelihood (DGEL) estimators. Although Newey and Smith (2004) show that the EL estimator in the GEL family has fewer sources of bias and is higher-order efficient after bias-correction, the demeaned exponential tilting (DET) estimator in the DGEL group has those superior properties. In addition, if data are symmetrically distributed, every estimator in the DGEL family shares the same higher-order properties as the best member.   / Dissertation/Thesis / Ph.D. Economics 2013

<p>Sedan årsskiftet 2006/2007 har antalet medlemmar i arbetslöshetskassorna minskat drastiskt. Under samma period har ett flertal reformer genomförts på arbetslöshetsförsäkringens område som bland annat resulterat i höjda medlemsavgifter för de flesta a-kassorna. Syftet med denna uppsats är att undersöka huruvida det över tid går att finna något samband mellan förändringar i medlemsantal och medlemsavgifter. För att undersöka detta måste man förutom avgifterna även ta hänsyn till andra variabler kopplade till arbetslöshetsförsäkringen. Dessa övriga variabler är grundbelopp, högsta dagpenning, ersättningsgrad och arbetslöshet. Vi formulerar en modell för sambandet mellan medlemsantal och dessa variabler och skattar denna genom metoden Generalized Method of Moments med hjälp av data från 2000-2009. Våra resultat visar i enlighet med teori och tidigare forskning på ett negativt samband mellan medlemsavgifter och antalet medlemmar i a-kassan. Detta samband visar sig vara starkt, särskilt på lång sikt. För att tydigare se hur avgiftsförändringar påverkar olika typer av individer i olika grad har vi även undersökt huruvida medlemsantalet i a-kassor kopplade till tjänstemanna- respektive arbetarförbund är olika känsliga för förändringar i avgiften. Våra resultat visar i kontrast till tidigare studier att a-kassorna kopplade till tjänstemannaförbunden (TCO och Saco) är mer känsliga för förändringar jämfört med arbetarförbunden (LO). Detta skapar anledning att tro att det finns andra faktorer än avgifter och de övriga variablerna som inkluderats i vår modell vilka påverkar anslutningsgraden och som kan förklara skillnaden mellan de olika grupperna.</p>

Sedan årsskiftet 2006/2007 har antalet medlemmar i arbetslöshetskassorna minskat drastiskt. Under samma period har ett flertal reformer genomförts på arbetslöshetsförsäkringens område som bland annat resulterat i höjda medlemsavgifter för de flesta a-kassorna. Syftet med denna uppsats är att undersöka huruvida det över tid går att finna något samband mellan förändringar i medlemsantal och medlemsavgifter. För att undersöka detta måste man förutom avgifterna även ta hänsyn till andra variabler kopplade till arbetslöshetsförsäkringen. Dessa övriga variabler är grundbelopp, högsta dagpenning, ersättningsgrad och arbetslöshet. Vi formulerar en modell för sambandet mellan medlemsantal och dessa variabler och skattar denna genom metoden Generalized Method of Moments med hjälp av data från 2000-2009. Våra resultat visar i enlighet med teori och tidigare forskning på ett negativt samband mellan medlemsavgifter och antalet medlemmar i a-kassan. Detta samband visar sig vara starkt, särskilt på lång sikt. För att tydigare se hur avgiftsförändringar påverkar olika typer av individer i olika grad har vi även undersökt huruvida medlemsantalet i a-kassor kopplade till tjänstemanna- respektive arbetarförbund är olika känsliga för förändringar i avgiften. Våra resultat visar i kontrast till tidigare studier att a-kassorna kopplade till tjänstemannaförbunden (TCO och Saco) är mer känsliga för förändringar jämfört med arbetarförbunden (LO). Detta skapar anledning att tro att det finns andra faktorer än avgifter och de övriga variablerna som inkluderats i vår modell vilka påverkar anslutningsgraden och som kan förklara skillnaden mellan de olika grupperna.

abstract: When analyzing longitudinal data it is essential to account both for the correlation inherent from the repeated measures of the responses as well as the correlation realized on account of the feedback created between the responses at a particular time and the predictors at other times. A generalized method of moments (GMM) for estimating the coefficients in longitudinal data is presented. The appropriate and valid estimating equations associated with the time-dependent covariates are identified, thus providing substantial gains in efficiency over generalized estimating equations (GEE) with the independent working correlation. Identifying the estimating equations for computation is of utmost importance. This paper provides a technique for identifying the relevant estimating equations through a general method of moments. I develop an approach that makes use of all the valid estimating equations necessary with each time-dependent and time-independent covariate. Moreover, my approach does not assume that feedback is always present over time, or present at the same degree. I fit the GMM correlated logistic regression model in SAS with PROC IML. I examine two datasets for illustrative purposes. I look at rehospitalization in a Medicare database. I revisit data regarding the relationship between the body mass index and future morbidity among children in the Philippines. These datasets allow us to compare my results with some earlier methods of analyses. / Dissertation/Thesis / Arizona Medicare Data on Rehospitalization / Philippine Data on Children's Morbidity / M.S. Statistics 2012

abstract: Correlation is common in many types of data, including those collected through longitudinal studies or in a hierarchical structure. In the case of clustering, or repeated measurements, there is inherent correlation between observations within the same group, or between observations obtained on the same subject. Longitudinal studies also introduce association between the covariates and the outcomes across time. When multiple outcomes are of interest, association may exist between the various models. These correlations can lead to issues in model fitting and inference if not properly accounted for. This dissertation presents three papers discussing appropriate methods to properly consider different types of association. The first paper introduces an ANOVA based measure of intraclass correlation for three level hierarchical data with binary outcomes, and corresponding properties. This measure is useful for evaluating when the correlation due to clustering warrants a more complex model. This measure is used to investigate AIDS knowledge in a clustered study conducted in Bangladesh. The second paper develops the Partitioned generalized method of moments (Partitioned GMM) model for longitudinal studies. This model utilizes valid moment conditions to separately estimate the varying effects of each time-dependent covariate on the outcome over time using multiple coefficients. The model is fit to data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to investigate risk factors of childhood obesity. In the third paper, the Partitioned GMM model is extended to jointly estimate regression models for multiple outcomes of interest. Thus, this approach takes into account both the correlation between the multivariate outcomes, as well as the correlation due to time-dependency in longitudinal studies. The model utilizes an expanded weight matrix and objective function composed of valid moment conditions to simultaneously estimate optimal regression coefficients. This approach is applied to Add Health data to simultaneously study drivers of outcomes including smoking, social alcohol usage, and obesity in children. / Dissertation/Thesis / Doctoral Dissertation Statistics 2018

The research performed throughout this dissertation aims at implementing quantitative methods in order to assess economic and technological performances of firms, i.e. it tries to assess the impacts of the determinants of technological activity on the results of this activity. For this purpose, a representative sample of the most important R&D firms in the world is constituted. The micro-economic nature of the analysis, as well as its international dimension are two main features of this research at the empirical level. The second chapter illustrates the importance of R&D investments, patenting activities and other measures of technological activities performed by firms over the last 10 years. The third chapter describes the main features as well as the construction of the database. The raw data sample consists of comparable detailed micro-level data on 2676 large manufacturing firms from several countries. These firms have reported important R&D expenditures over the period 1980-1994. The fourth chapter explores the dynamic structure of the patent-R&D relationship by considering the number of patent applications as a function of present and lagged levels of R&D expenditures. R&D spillovers as well as technological and geographical opportunities are taken into account as additional determinants in order to explain patenting behaviours. The estimates are based on recently developed econometric techniques that deal with the discrete non-negative nature of the dependent patent variable as well as the simultaneity that can arise between the R&D decisions and patenting. The results show evidence of a rather contemporaneous impact of R&D activities on patenting. As far as R&D spillovers are concerned, these externalities have a significantly higher impact on patenting than own R&D. Furthermore, these effects appear to take more time, three years on average, to show up in patents. The fifth chapter explores the contribution of own stock of R&D capital to productivity performance of firms. To this end the usual productivity residual methodology is implemented. The empirical section presents a first set of results which replicate the analysis of previous studies and tries to assess the robustness of the findings with regard to the above issues. Then, further results, based on different sub samples of the data set, investigate to what extent the R&D contribution on productivity differs across firms of different industries and geographic areas or between small and large firms and low and high-tech firms. The last section explores more carefully the simultaneity issue. On the whole, the estimates indicate that R&D has a positive impact on productivity performances. Yet, this contribution is far from being homogeneous across the different dimensions of data or according to the various assumptions retained in the productivity model. The last empirical chapter goes deeper into the analysis of firms' productivity increases, by considering besides own R&D activities the impact of technological spillovers. The chapter begins by surveying the alternative ways proposed in the literature in order to asses the effect of R&D spillovers on productivity. The main findings reported by some studies at the micro level are then outlined. Then, the framework to formalize technological externalities and other technological determinants is exposed. This framework is based on a positioning of firms into a technological space using their patent distribution across technological fields. The question of whether the externalities generated by the technological and geographic neighbours are different on the recipient's productivity is also addressed by splitting the spillover variable into a local and national component. Then, alternative measures of technological proximity are examined. Some interesting observations emerge from the empirical results. First, the impact of spillovers on productivity increases is positive and much more important than the contribution of own R&D. Second, spillover effects are not the same according to whether they emanate from firms specialized in similar technological fields or firms more distant in the technological space. Finally, the magnitude and direction of these effects are radically different within and between the pillars of the Triad. While European firms do not appear to particularly benefit from both national and international sources of spillovers, US firms are mainly receptive to their national stock and Japanese firms take advantage from the international stock.

This dissertation consists of three essays concerning the effects of human capital and health capital on the labor market. Chapter 1 presents a structural model that incorporates a health capital stock to the traditional learning-by-doing model. The model allows health to affect future wages by interrupting current labor supply and on-the-job human capital accumulation. Using data on sick time from the Panel Study Income of Dynamics the model is estimated using a nonlinear Generalized Method of Moments estimator. The results show human capital production exhibits diminishing returns. Health capital production increases with the current stock of health capital, or better current health improves future health. Among prime age working men, the effect of health on human capital accumulation is relatively small. Chapter 2 explores the role of another form of human capital, noncognitive skills, in explaining racial gaps in wages. Chapter 2 adds two noncognitive skills, locus of control and self-esteem, to a simple wage specification to determine the effect of these skills on the racial wage gap (white, black, and Hispanic) and the return to these skills across the wage distribution. The wage specifications are estimated using pooled, between, and quantile estimators. Results using the National Longitudinal Survey of Youth 1979 show these skills account for differing portions of the racial wage gap depending on race and gender. Chapter 3 synthesizes the idea of health and on-the-job human capital accumulation from Chapter 1 with the idea of noncognitive skills in Chapter 2 to examine the influence of these skills on human capital and health capital accumulation in adult life. Chapter 3 introduces noncognitive skills to a life cycle labor supply model with endogenous health and human capital accumulation. Noncognitive skills, measured by degree of future orientation, self-efficacy, trust-hostility, and aspirations, exogenously affect human capital and health production. The model uses noncognitive skills assessed in the early years of the Panel Study of Income Dynamics and relates these skills to health and human capital accumulation during adult life. The main findings suggest individuals with high self-efficacy receive higher future wages.

Thesis advisor: Uzi Segal / The dissertation studies the property of transitivity in the social choice theory. I explain why we should care about transitivity in decision theory. I propose two social decision theories: redistribution regret and ranking regret, study their properties of transitivity, and discuss the possibility to find a best choice for the social planner. Additionally, in the joint work, we propose a general method to construct a consistent estimator given two parametric models, one of which could be incorrectly specified. In “Why Transitivity”, to explain behaviors violating transitivity, e.g., preference reversals, some models, like regret theory, salience theory were developed. However, these models naturally violate transitivity, which may not lead to a best choice for the decision maker. This paper discusses the consequences and the possible extensions to deal with it. In “Redistribution Regret and Transitivity”, a social planner wants to allocate resources, e.g., the government allocates fiscal revenue or parents distribute toys to children. The social planner cares about individuals' feelings, which depend both on their assigned resources, and on the alternatives they might have been assigned. As a result, there could be intransitive cycles. This paper shows that the preference orders are generally non-transitive but there are two exceptions: fixed total resource and one extremely sensitive individual, or only two individuals with the same non-linear individual regret function. In “Ranking Regret”, a social planner wants to rank people, e.g., assign airline passengers a boarding order. A natural ranking is to order people from most to least sensitive to their rank. But people's feelings can depend both on their assigned rank, and on the alternatives they might have been assigned. As a result, there may be no best ranking, due to intransitive cycles. This paper shows how to tell when a best ranking exists, and that when it exists, it is indeed the natural ranking. When this best does not exist, an alternative second-best group ranking strategy is proposed, which resembles actual airline boarding policies. In “Over-Identified Doubly Robust Identification and Estimation”, joint with Arthur Lewbel and Jinyoung Choi, we consider two parametric models. At least one is correctly specified, but we don't know which. Both models include a common vector of parameters. An estimator for this common parameter vector is called Doubly Robust (DR) if it's consistent no matter which model is correct. We provide a general technique for constructing DR estimators (assuming the models are over identified). Our Over-identified Doubly Robust (ODR) technique is a simple extension of the Generalized Method of Moments. We illustrate our ODR with a variety of models. Our empirical application is instrumental variables estimation, where either one of two instrument vectors might be invalid. / Thesis (PhD) — Boston College, 2021. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

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We consider the effect of disability status on Medicare expenditures. Disabled elderly historically have accounted for a significant portion of Medicare expenditures. Recent demographic trends exhibit a decline in the size of this population, causing some observers to predict declines in Medicare expenditures. There are, however, reasons to be suspicious of this rosy forecast. To better understand the effect of disability on Medicare expenditures, we develop and estimate a model using the generalized method of moments technique. We find that newly disabled elderly generally spend more than those who have been disabled for longer periods of time. Also, we find that increases in expenditures have risen much more quickly for those disabled Medicare beneficiaries who were at the higher ends of the expenditure distribution before the increases.