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In this thesis, we study three revenue management problems where we propose simple algorithms with provable guarantees. While online marketplaces provide retailers with tremendous flexibility, they are often large, noisy, have multiple stakeholders, and could be more challenging to characterize. These complexities give rise to a preference for simple, interpretable policies. Further, traditional marketplaces such as brick-and-mortar stores cannot always leverage tools designed for online environments due to physical constraints, higher latency, etc. With these motivations in mind, we develop algorithms for assortment optimization and pricing that are easy to implement in practice and have theoretical justifications for their performance. In Chapter 1, we consider a dynamic assortment optimization problem where the seller has a fixed inventory of multiple substitutable products to sell over a fixed time horizon. We consider two modifications to the traditional problem. First, we simplify the assortment planning by restricting assortment changes to "product retirements". When a product is retired, it becomes unavailable to all future customers. Second, we assume the seller has flexibility regarding which customers to approach. In each period, the seller chooses which subset of products to retire and selects a customer to visit. The selected customer then receives an option to purchase one of the available products, i.e., non-retired products with positive remaining inventory. We provide two policies for this problem. Our first policy guarantees a constant fraction of the best possible revenue. Our second policy is near-optimal but requires the problem to have a specific structure. In Chapter 2, we study the fundamental joint pricing and inventory management problem. The optimal policy for the model we consider is known to be an (s, S, p) policy: when the inventory level drops to s units, the seller immediately places an order to replenish the inventory to S units. Specifically, the optimal pricing policy p has a different price for every inventory state. We proposed simple policies requiring no more than three prices and prove that these policies are near-optimal compared to optimal policies which require more prices and are less robust. In particular, when orders cannot be backlogged, we show that a single price is sufficient for good performance. In Chapter 3, we analyze assortment optimization and pricing with opaque products. An opaque product is one for which only partial information is available to the buyer at the time of purchase. When a customer selects the opaque product, the seller can fulfill the purchase using any of the offered products. Opaque products can help sellers boost total sales. We propose simple policies for assortment optimization with provable constant factor guarantees, which are near-optimal in numerical experiments. We also provide upper bounds for the advantage of selling opaque products.

This dissertation consists of three chapters on game theory and its application to social segregation and discrimination. In the first chapter, we discuss two interpretations of the Nash equilibrium and connect the remaining two chapters based on such interpretations. The first chapter also provides the motivations and the summary of Chapters 2 and 3. In the second chapter, we consider an extension of an almost strictly competitive game in n-person extensive games by incorporating Seiten's subgame perfection. We call this extension a subgame perfect weakly-almost (SPWA) strictly competitive game, in particular, a SPW A strictly competitive game in strategic form is simply called a WA strictly competitive game. We give some general results on the structure of these classes of games. One result gives an easy way to verify almost strict competitiveness of a given extensive game. We show that a two-person weakly unilaterally competitive extensive game and a finitely repeated WA strictly competitive game are SPW A strictly competitive. In the third chapter, we consider segregations, discriminatory behaviors, and prejudices in a recurrent situation of a game called the festival game with merrymakers. We show that segregation and discriminatory behaviors may occur in Nash equilibria in the sense that players of one ethnic group go to one festival, and, if any member of one ethnic group tries to go to a different festival, he will be treated differently only for the reason of nominal differences in ethnicities between them. One of our results states that if a player tries to enter a larger festival from a smaller one, he would be discriminated against by some people in the larger festival, but not necessarily if one goes from a larger one to a smaller one. We use the theory of stable conventions for the considerations of the entire recurrent situation and of the epistemic assumptions for each individual player. We show that the central parts of the stable conventions are captured by the Nash equilibria. Associating our results with the theory of stable conventions and the cognitive and moral views called subjectivism and retributionism, we discuss the emergence of fallacious views of each player about the utility functions of all the players. One such view explains prejudicial attitudes as a rationalization of discriminatory behaviors. / Ph. D.

This dissertation contains two independent but related papers which investigate theoretically and empirically qualitative aspects of firms' behavior in dynamic settings. CHAPTER 1. ENDOGENOUS ATTRITION OF FIRMS: An Investigation with COMPUSTAT Data Chapter One develops a dynamic programming model of firms' attrition and investigates econometric aspects of firms' exit decisions. Structural econometric analyses of exit decisions of firms involve rather naturally a number of qualitative dimensions. This chapter investigates the exit decision empirically by means of panel data from COMPUSTAT for U.S. manufacturing firms which are publicly traded. A number of different techniques are employed, which include Poisson models, structural form models and duration models. Our findings show that observable characteristics of the individual firms are important in understanding the dynamics of firm's attrition. Cyclical effects and macroeconomic variables have also a strong impact on bankruptcies, liquidations and reorganizations. Unobserved firm heterogeneity, modeled by means of random effects, is not significant in explaining exit decisions by firms. Firms' attrition is more likely to result from random events at the time of exit. CHAPTER 2 AN EMPIRICAL INVESTIGATION ON THE DYNAMICS OF QUALITATIVE DECISIONS OF FIRMS Chapter Two focuses on qualitative aspects of financing, investment and output decisions of firms. Such dimensions can be modeled econometrically by means of dynamic limited dependent variables models. We develop a partial equilibrium dynamic stochastic programming problem of investment, dividend and financing decisions for a typical firm and we use it to examine firms' behavior under exogenous borrowing constraints. We use panel data from COMPUSTAT for publicly traded U.S. manufacturing firms. We apply limited dependent variable models' techniques to study the discrete decisions of whether or not firms pay dividends, or whether they use borrowing or equity issue financing for investment. We study the pattern of transitions over time across various regimes that represent alternative modes of finance while controlling for individual heterogeneity with a general stochastic structure for unobservables. Structural form models show considerable success in explaining the dynamics of such decisions, with individual characteristics of the firms which include firm fundamentals, and lagged values of the decisions, showing a strong explanatory impact. The dynamics of the estimated models reveal high persistence in manufacturing firms to repeat their last period’s decision. Firm heterogeneity modeled by means of random effects, explains also a significant part of firms' qualitative decisions. / Ph. D.

The continuing trend towards greater globalization and interdependency of the world economies offers opportunities for long-run efficiency gains for all economies but poses short-run risks for firms operating in international arenas. Recession, inflation, interest rates and exchange rates in a foreign nation impact multinational firms with business interests in that nation, and ultimately affect the domestic economies of such firms. For example, foreign interest rate shocks or exchange rate volatility increases both increase the uncertainty and risk associated with multinational operations. This study examines the impact of foreign economic shocks on domestic economic variables and evaluates the use of options as financial hedging instruments against foreign currency exchange risk. The study is divided into two parts: (1) a dynamic model of an open economy is developed and used to simulate the impact of foreign economic shocks on domestic economic variables; and (2) two types of path dependent options (Average rate and Lookback options) are compared to standard European options as instruments for hedging against foreign exchange risk under various interest rate and exchange rate volatility scenarios. / Master of Arts

Previous research has shown that the asset pricing model of Sharpe, Litner and Black fails to capture the relationship between market β and average return. This previous work showed that the relationship between β and average return was flat. Subsequently it was shown that a strong relationship between book-to-market equity and stock price returns existed. It has also been shown that book to market equity has strong roots in economic fundamentals. Utilities have historically used betas to justify rate increases I developing rate structures that meet the rate of return demands for investors given the risk profiles that the company betas suggest. Realizing that low betas argue against large rate increases l utilities have turned to other avenues to justify higher returns. The suggested relationship of book-to-market equity and average stock returns would provide utilities with a new argument. This thesis will show that the search for a risk proxy in the rate of return relationship for the electric utility is not resolved. The relationship reported between book-to-market equity and stock price returns does not appear to be statistically significant in the electric utility sector and extreme caution is advised in using this empirical model to predict or explain stock price returns. / Master of Arts

This dissertation consists of three empirical essays focused on understanding how informational and behavioral barriers influence human capital investments in developing countries. In particular, I study two key actors in education production – teachers and parents, and how their beliefs shape investments in child human capital with implications for student learning and inequality. Chapter 1 uses a field experiment with teachers in India to investigate the role and malleability of teachers’ beliefs. In many developing countries, teachers often perceive only a weak mapping between their effort and what students learn. I conduct an experimental evaluation of a psychosocial intervention in India that targets teachers’ beliefs about perceived control – self-beliefs about one’s ability to influence outcomes. I study the extent to which this intervention affects teachers’ beliefs, their effort in class, and their students’ academic performance. I devise a novel experimental task to elicit beliefs through revealed preference, about the relationship between their teaching effort and the performance of students in their classroom. I find that the intervention induced a 14% increase in teachers’ beliefs about their ability to increase learning, as measured by the revealed preference task. Treated teachers exert greater effort at the intensive margin, scoring higher on an index of classroom effort. They also spend more time grading student work and provide more detailed feedback to students. Finally, students taught by teachers in the treatment group learn more, scoring 0.09 SD higher in the end-of-year exams. These findings suggest that teacher beliefs can serve as a powerful lever for changing teaching practice and raising learning levels in developing countries. Chapter 2 studies the relationship between parental perceptions about children’s performance and parental investment in children’s human capital, and how this relationship evolves over the course of schooling. Using rich longitudinal data on investments, test scores, and parental assessments, I implement alternative specifications for the parental investment function that allow investment to depend on the entire history of lagged investment and inputs, account for past parental beliefs to circumvent reverse causality, and use household fixed effects to account for fixed characteristics at the household level. I find that compared to children with poor perceived performance, children with better perceived performance are up to 16 percentage points more likely to be enrolled in private as opposed to public schools, and receive up to 40% higher investment in schooling. This relationship intensifies as children progress from primary to secondary school. Results are robust across specifications, with evidence of complementarity between perceived ability and schooling. Within a household, parents’ behavior is reinforcing, with more spent on the child believed to be the better performer. These findings inform our understanding of parental investment response and intra-household allocation of human capital investment decisions. Chapter 3 (co-authored with Daniel Chen, Sultan Mehmood, and Shaheen Naseer) uses a field experiment to evaluate the impact of providing information about teacher value-added to public school teachers in Pakistan. We show that growth mindset training shifts teachers’ beliefs about the malleability of intelligence, and reduces stereotypes against first-generation learners and students from disadvantaged backgrounds. In contrast, exposure to narrative or empirical evidence about teacher value-added did not have statistically significant effects. We document patterns of teachers’beliefs in a resource-constrained setting and show that perceived returns to effort are increasing in parental education and past performance of students, indicating that teachers view these as complementary inputs for teaching.

This thesis focuses on learning from revealed preferences and their implications across operations management problems through an Inverse Problem perspective. For the first part of the thesis, we focus on decentralized platforms facilitating many-to-many matches between two sides of a marketplace. In the absence of direct matching, inefficiency in market outcomes can easily arise. For instance, popular supply agents may garner many units from the demand side, while other supply units may not receive any match. A central question for the platform is how to manage congestion and improve market outcomes. In Chapter One, we study the impact of a detail-free lever: the disclosure of information to agents on current competition levels. How large are the effects of this lever, and how do they affect overall market outcomes? We answer this question empirically. We partner with the largest service marketplace in Latin America, which sells non-exclusive labor market leads to workers. The key innovation in our approach is the proposal of a structural model that allows agents (workers) to respond to competitors through beliefs about competition at the lead level, which in turn implies an equilibrium at the platform level under the assumption of rational expectations. In this problem, we observe agents' best responses (actions), and from that, we need to infer their structural parameters. Identification follows from an exogenous intervention that changes agents' contextual information and the platform equilibrium. We then conduct counterfactual analyses to study the impact of signaling competition on workers' lead purchasing decisions, the platform's revenue, and the expected number of matches. We find that signaling competition is a powerful lever for the platform to reduce congestion, redirect demand, and ultimately improve the expected number of matches for the markets we analyze. For the second part of the thesis, we discuss both parametric and modelling approaches in Inverse Problems. In Chapter Two, we focus on Inverse Optimization Problems in a single-agent setting. Specifically, we study offline and online contextual optimization with feedback information, where instead of observing the loss, we observe, after-the-fact, the optimal action an oracle with full knowledge of the objective function would have taken. We aim to minimize regret, which is defined as the difference between our losses and the ones incurred by an all-knowing oracle. In the offline setting, the decision-maker has information available from past periods and needs to make one decision, while in the online setting, the decision-maker optimizes decisions dynamically over time based on a new set of feasible actions and contextual functions in each period. For the offline setting, we characterize the optimal minimax policy, establishing the performance that can be achieved as a function of the underlying geometry of the information induced by the data. In the online setting, we leverage this geometric characterization to optimize the cumulative regret. We develop an algorithm that yields the first regret bound for this problem, which is logarithmic in the time horizon. Furthermore, we show via simulation that our proposed algorithms outperform previous methods from the literature. Finally, in Chapter Three, we consider data-driven methods for general Inverse Problem formulations under a statistical framework (Statistical Inverse Problem-SIP) and demonstrate how Stochastic Gradient Descent (SGD) algorithms can be used to solve linear SIP. We provide consistency and finite sample bounds for the excess risk. We exemplify the algorithm in the Functional Linear Regression setting with an empirical application in predicting illegal activity from bitcoin wallets. We also discuss additional applications and extensions.

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Following the debate by empirical finance research on the presence of non-linear predictability in stock market returns, this study examines forecasting abilities of nonlinear STAR-type models. A non-linear model methodology is applied to daily returns of FTSE, S&P, DAX and Nikkei indices. The research is then extended to long-horizon forecastability of the four series including monthly returns and a buy-and-sell strategy for a three, six and twelve month holding period using non-linear error-correction framework. The recursive out-of-sample forecast is performed using the present value model equilibrium methodology, whereby stock returns are forecasted using macroeconomic variables, in particular the dividend yield and price-earnings ratio. The forecasting exercise revealed the presence of non-linear predictability for all data periods considered, and confirmed an improvement of predictability for long-horizon data. Finally, the present value model approach is applied to the housing market, whereby the house price returns are forecasted using a price-earnings ratio as a measure of fundamental levels of prices. Findings revealed that the UK housing market appears to be characterised with asymmetric non-linear dynamics, and a clear preference for the asymmetric ESTAR model in terms of forecasting accuracy.