Thesis advisor: Stefan Hoderlein / This dissertation is divided into two parts. Part I - Dynamic Kidney Exchange In recent years, kidney paired donation (KPD) has an emerged as an attractive alternative for end-stage renal disease patients with incompatible living donors. However, we argue that the matching algorithm currently used by organ clearinghouses is inefficient, in the sense that a larger number of patients may be reached if kidney transplant centers take into consideration how their pool of patients and donors will evolve over time. In our work Two Novel Algorithms for Dynamic Kidney Exchange, we explore this claim and propose new computational algorithms to increase the cardinality of matchings in a discrete-time dynamic kidney exchange model with Poisson entries and Geometric deaths. Our algorithms are classified into direct prediction methods and multi-armed bandit methods. In the direct prediction method, we use machine learning estimator to produce a probability that each patient-donor pair should be matched today, as op- posed to being left for a future matching. The estimators are trained on offline optimal solutions. In contrast, in multi-armed bandit methods, we use simulations to evaluate the desirability of different matchings. Since the amount of different matchings is enormous, multi-armed bandits (MAB) are employed to decrease order to decrease the computational burden. Our methods are evaluated using simulations in a variety of simulation configurations. We find that the performance of at least one of our methods, based on multi-armed bandit algorithms, is able to uniformly dominate the myopic method that is used by kidney transplants in practice. We restrict our experiments to pairwise kidney exchange, but the methods described here are easily extensible, computational constraints permitting. Part II - Econometrics In our econometric paper Heterogenous Production Functions, Panel Data, and Productivity, we present methods for identification of moments and nonparametric marginal distributions of endogenous random coefficient models in fixed-T linear panel data models. Our identification strategy is constructive, immediately leading to relatively simple estimators that can be shown to be consistent and asymptotically normal. Because our strategy makes use of special properties of “small” (measure-zero) subpopulations, our estimators are irregularly identified: they can be shown to be consistent and asymptotically Normal, but converge at rates slower than root-n. We provide an illustration of our methods by estimating first and second moments of random Cobb-Douglas coefficients in production functions, using Indian plant-level microdata. / Thesis (PhD) — Boston College, 2018. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.