The three chapters of my dissertation study imperfect competition, multiproduct firms, and consumer demand. Chapter 1 estimates a structural model of consumer demand and oligopolistic retail competition in order to study three mechanisms through which retailers affect allocative efficiency and consumer welfare. First, variable markups across retail stores within a location induce a misallocation of resources. The deadweight loss from this retail misallocation can be large since a significant fraction of household consumption comes from retail goods. Second, across locations, retail markups may vary with market size. This regional variation plays an important role in recent economic geography models as an agglomeration force. In the limit, models predict that the distortion from variable markups disappears in large markets, although it is an open question, How Large is Large? Third, since retail stores are differentiated, differences in the variety of retail stores available to consumers matters for consumer welfare across locations. To quantify the importance of these mechanisms, I estimate my model using retail scanner data with prices and sales at the barcode level from thousands of stores across the US. I find that the deadweight loss and consumption misallocation from variable retail markups are economically significant. I estimate that retail markups are smaller in larger cities, and that markets the size of New York City and Los Angeles are approximately at the undistorted monopolistically competitive limit. My results show that retail store variety significantly impacts the cost of living and could be an important consumption-based agglomeration force. The second chapter of my dissertation develops and structurally estimates a model of heterogeneous multiproduct firms that can be used to decompose the firm-size distribution into the contributions of costs, quality, markups, and product scope. In this joint work with Stephen J. Redding and David E. Weinstein, we find that variation in firm quality and product scope explains at least four fifths of the variation in firm sales using Nielsen barcode data on prices and sales. We show that the imperfect substitutability of products within firms, and the fact that larger firms supply more products than smaller firms, implies that standard productivity measures are not independent of demand system assumptions and probably dramatically understate the relative productivity of the largest firms. Although most firms are well approximated by the monopolistic competition benchmark of constant markups, we find that the largest firms that account for most of aggregate sales depart substantially from this benchmark, and exhibit both variable markups and substantial cannibalization effects. The final chapter of my dissertation develops a new integrable demand system, called the Doubly-Translated CDES demand system, which is well suited to theoretical and empirical work. Commonly used analytically and computationally tractable demand systems severely restrict key properties of demand, which parametrically pins down the answers to many important economic questions. The Doubly-Translated CDES demand system is flexible in important ways that common demand systems are not, while maintaining effective global regularity and global consistency. Using data, I provide examples of this demand system's flexibility by calibrating different parameter values. I discuss how this demand system can be estimated with regularity imposed and correcting for the endogeneity of prices using constrained Nonlinear GMM.

In Chapter 2, we present a model that allows us to study the effect of increased competition among charities for donations, and show that it will result in a lower provision of public goods. When charities get donations, they must pay two fundraising costs: a travel cost and an extra cost, a "premium" in our terminology. This premium arises from the extra time, effort, or incentives a charity must provide to garner a contribution from a donor who is solicited by other charities. Increased competition raises this premium, which leads to deadweight loss, so that revenue net of fundraising costs falls after a new firm enters into the market. A problem with public goods markets is asymmetric information between charities and donors, such that donors do not know which charities will cheat. In Chapter 3, we show that honest charities can get more donations than dishonest charities by investing in a capital stock. We study a two-period model under two assumptions, one where first-period investment does not affect the provision of public goods in the second period, and one where first-period investment does affect the provision of public goods in the second period. In the first case, we prove the existence of a separating equilibrium where honest charities make an investment and dishonest charities invest nothing. Thus, donors will donate more to charities that make investments, even if the investment is not used to produce public goods. In the second case, honest charities may invest the efficient amount, overinvest, or underinvest, depending on the donors' beliefs. In Chapter 4, we borrow parts of the models in the previous two chapters in order to see what effect the signaling cost has on the number of firms and average revenue. In our model, donor utility increases when they give to a charity that matches their ideology. We are interested in the long-run equilibrium, so unlike in Chapter 2, we assume there is free entry in the market. The two important results are that the number of firms decreases and average revenue increases if the required signaling cost increases.

Thesis (Ph. D.)--University of California, Santa Cruz, 1996. / Typescript. Includes bibliographical references (leaves 146-147).

This thesis examines the size and distribution of benefits from the use of transgenic tobacco as a production vehicle for pharmaceutical proteins. Ex-ante welfare benefits are estimated for the introduction of two biotech innovations. In both cases economic surplus model with imperfect competition is employed to assess the size and distribution of benefits from these alternative uses of tobacco. An introductory chapter presents an overview of the topic followed by chapters 2 and 3 which contain the two case studies. The first paper (chapter 2) examines the case of Human Serum Albumin production from transgenic tobacco. The second paper (chapter 3) examines the case of Glucocerebrosidase Enzyme from transgenic tobacco. Results demonstrate that new products from bio-pharming applications stand to generate significant social benefits. The introduction of Human Serum Albumin generates average annual gains of $46 million and the introduction of Glucocerebrosidase Enzyme generates average annual gains of $500 to $600 million. / Master of Science

In recent years, extensive research has been devoted to opportunistically exploiting spectrum in a distributed cognitive radio network. In such a network, autonomous secondary users (SUs) compete with each other for better channels without instructions from a centralized authority or explicit coordination among SUs. Channel selection relies on channel occupancy information observed by SUs, including whether a channel is occupied by a PU or an SU. Therefore, the SUs' performance depends on the quality of the information. Current research in this area often assumes that the SUs can distinguish a channel occupied by a PU from one occupied by another SU. This can potentially be achieved using advanced signal detection techniques but not by simple energy detection. However, energy detection is currently the primary detection technique proposed for use in cognitive radio networks. This creates a need to design a channel selection strategy under the assumption that, when SUs observe channel availability, they cannot distinguish between a channel occupied by a PU and one occupied by another SU. Also, as energy detection is simpler and less costly than more advanced signal detection techniques, it is worth understanding the value associated with better channel occupancy information. The first part of this thesis investigates the impact of different types of imperfect information on the performance of secondary users (SUs) attempting to opportunistically exploit spectrum resources in a distributed manner in a channel environment where all the channels have the same PU duty cycle. We refer to this scenario as the homogeneous channel environment. We design channel selection strategies that leverage different levels of information about channel occupancy. We consider two sources of imperfect information: partial observability and sensing errors. Partial observability models SUs that are unable to distinguish the activity of PUs from SUs. Therefore, under the partial observability models, SUs can only observe whether a channel was occupied or not without further distinguishing it was occupied by a PU or by SUs. This type of imperfect information exists, as discussed above, when energy detection is adopted as the sensing technique. We propose two channel selection strategies under full and partial observability of channel activity and evaluate the performance of our proposed strategies through both theoretical and simulation results. We prove that both proposed strategies converge to a stable orthogonal channel allocation when the missed detection rate is zero. The simulation results validate the efficiency and robustness of our proposed strategies even with a non-zero probability of missed detection. The second part of this thesis focuses on computing the probability distribution of the number of successful users in a multi-channel random access scheme. This probability distribution is commonly encountered in distributed multi-channel communication systems. An algorithm to calculate this distribution based on a recursive expression was previously proposed. We propose a non-recursive algorithm that has a lower execution time than the one previously proposed in the literature. The third part of this thesis investigates secondary users (SUs) attempting to opportunistically exploit spectrum resources in a scenario where the channels have different duty cycles, which we refer to as the heterogeneous channel environment. In particular, we model the channel selection process as a one shot game. We prove the existence of a symmetric Nash equilibrium for the proposed static game and design a channel selection strategy that achieves this equilibrium. The simulation results compare the performance of the Nash equilibrium to two other strategies(the random and the proportional strategies) under different PU activity scenarios. / Master of Science

An electricity market is very complex and different in its nature, when compared to other commodity markets. The introduction of competition and restructuring in global electricity markets brought more complexity and major changes in terms of governance, ownership and technical and market operations. In a liberalized electricity market, all market participants are responsible for their own decisions; therefore, all the participants are trying to make profit by participating in electricity trading. There are different types of electricity market, and in this research a bilateral electricity market has been specifically considered. This thesis not only contributes with regard to the reviewing UK electricity market as an example of a bilateral electricity market with more than 97% of long-term bilateral trading, but also proposes a dual aspect point of view with regard to the bilateral electricity market by splitting the generation and supply sides of the wholesale market. This research aims at maximizing the market participants’ profits and finds the equilibrium point of the bilateral market; hence, various methods such as equilibrium models have been reviewed with regard to management of the risks (e.g. technical and financial risks) of participating in the electricity market. This research proposes a novel Conjectural Variation Equilibrium (CVE) model for bilateral electricity markets, to reduce the market participants’ exposure to risks and maximize the profits. Hence, generation companies’ behaviors and strategies in an imperfect bilateral market environment, oligopoly, have been investigated by applying the CVE method. By looking at the bilateral market from an alternative aspect, the supply companies’ behaviors in an oligopsony environment have also been taken into consideration. At the final stage of this research, the ‘matching’ of both quantity and price between oligopolistic and oligopsonistic markets has been obtained through a novel-coordinating algorithm that includes CVE model iterations of both markets. Such matching can be achieved by adopting a hierarchical optimization approach, using the Matlab Patternsearch optimization algorithm, which acts as a virtual broker to find the equilibrium point of both markets. Index Terms-- Bilateral electricity market, Oligopolistic market, Oligopsonistic market, Conjectural Variation Equilibrium method, Patternsearch optimization, Game theory, Hierarchical optimization method

Hedging a contingent claim with an asset which is not perfectly correlated with the underlying asset results in an imperfect hedge. The residual risk from hedging with a correlated asset is priced using an actuarial standard deviation principle in infinitesmal time, which gives rise to a nonlinear partial differential equation (PDE). A fully implicit, monotone discretization method is developed for solving the pricing PDE. This method is shown to converge to the viscosity solution. Certain grid conditions are required to guarantee monotonicity. An algorithm is derived which, given an initial grid, inserts a finite number of nodes in the grid to ensure that the monotonicity condition is satisfied. At each timestep, the nonlinear discretized algebraic equations are solved using an iterative algorithm, which is shown to be globally convergent. Monte Carlo hedging examples are given, which show the standard deviation of the profit and loss at the expiry of the option.

Thesis advisor: Ryan Chahrour / Expectations formation is central to macroeconomics. Households, firms, and policymakers must form expectations not only about fundamentals, but about what other agents’ beliefs are, because others’ beliefs will determine their actions. The three essays in this dissertation examine empirically and theoretically how agents use both public and private information to form expectations. The first two essays combine a models of optimizing behavior and forecasting with data on the macroeconomy, financial prices, and macroeconomic forecasts to examine the extent to which economic agents learn about the macroeconomy from financial prices and monetary policy actions. The third essay examines theoretically how members of a committee use public and private information to form beliefs when they care both about having accurate forecasts and coordinating actions with others. All three essays emphasize that frictions in expectations formation are a salient feature of the world, and understanding the extent and importance of those frictions is important for both positive and normative questions in macroeconomics and finance. Beliefs about the future determine the willingness of financial market participants to save and invest, and theory suggests they should value more highly assets which are expected to pay higher returns during recessionary periods when consumption is otherwise low. Hence, financial prices reflect macroeconomic expectations. In the first essay, titled "Macroeconomic Disagreement in Treasury Yields," I explore how agents with idiosyncratic, private information form beliefs about both the macroeconomy and the beliefs of other agents. Using data on United States Treasury debt, the macroeconomy, and individual inflation forecasts, I estimate the precision of bond traders’ information about the macroeconomy and how much they disagree with each other. I allow for traders to learn both from private signals and from asset prices, which aggregate the beliefs of all the traders in the market. I find that bond prices are moderately informative about macroeconomic variables, but are the source of most of the information traders have about monetary policy and the beliefs of others. In contrast to studies which assume full information, risk premia are much less important than slow-adjusting interest rate expectations for explaining the behavior of long-run yields. The most important signal for bond traders appears to be the Federal Reserve’s short-run rate, which encodes information about the macroeconomy and the central bank’s intended future policy. Nevertheless, the fact that traders held disparate beliefs about the macroeconomy, and especially about the long-run inflation target of the Federal Reserve, elevated long-term yields on average. The first essay demonstrates empirically that financial market participants learn about the macroeconomy from monetary policy actions. However, it is silent on how monetary policymakers form beliefs about the macroeconomy, or how the information in monetary policy rates endogenously affects macroeconomic outcomes. In the second essay "Your Guess is as Good as Mine: Central Bank Information and Monetary Policy," I use data on private sector forecasts and forecasts from the Federal Reserve Board staff to examine the typical assumption of common information between firms and monetary policymakers. Using forecasts from a survey of professional forecasters and from the Federal Reserve Board staff, I show evidence against the typical assumption of common information between monetary policymakers and the private sector, and also that policymakers are, at best, only weakly better at forecasting than private forecasters. Based on this evidence, I augment an otherwise standard monetary policy model by relaxing the common information assumption. Instead, I assume there is idiosyncratic, private information among price-setting firms, and between firms and the central banker. Firms combine private information about aggregate conditions with the observed monetary policy rate to form expectations about fundamentals and the beliefs of rival firms. The central banker must form expectations about firms’ beliefs because those beliefs will determine inflation and overall economic activity. But as a result of their differences in information sets, firms must form expectations about other firms’ expectations, and what the central banks’ expectations of their expectations are. I examine the ability of this model to fit the data and find that the model can capture features of both firm and central bank inflation expectations, but in the absence of imperfect information among households, it is difficult to simultaneously match the forecast data and data on real activity. This result points to the sensitivity of models with dispersed information to the underlying assumptions about how central bankers will respond to exogenous shocks. The second chapter emphasized how the assumptions economists make regarding monetary policymakers’ information is critical for understanding their actions. Motivated by this example, my third chapter "Information Investment in a Coordination Game" explores theoretically how members of a committee who are uncertain about others’ beliefs decide on a binary action, and how their decision to pay close attention to public or private signals is related to their desire to accurately forecast versus coordinating their behavior with others. I show that when it is assumed that information decisions among committee members are symmetric - everyone pays the same amount of attention to the same things - there is a unique outcome of the coordination game. However, I further show that it is difficult to guarantee that committee members will all choose a symmetric allocation of information. Aside from the direct cost of acquiring better information, allocating attention to more accurate signals can harm welfare when coordination motives are dominant. In a set of numerical exercises, however, I show that it is possible for a unique equilibrium to exist, and that actions that do not have a large impact on the payoffs of committee members (such as changing the size of the committee) may nevertheless have large impacts on the accuracy of the committee’s forecasts. This suggests a possible tension between the welfare of the committee, which benefits from consensus, and the welfare of those affected by the committee’s actions, which likely depends on whether the committee takes the objectively correct action. My dissertation has important implications for both academic economists and policymakers. Understanding the sources of business cycle fluctuations and the determinants of asset prices requires grappling with the fact that people have differences in beliefs. Empirical evidence suggests that agents’ beliefs are shaped by both idiosyncratic forces and by public announcements and policy decisions, and economists’ models need to reflect these features of the world. Policy, too, is affected by the information available to policymakers, and to understand how policymakers have acted in the past and should act in the future, it is necessary to take seriously the ways their belief formation deviates from the full information rational expectations benchmark.

This thesis examines two closely related issues: (1) the ability of imperfect information models to explain some aspects of business cycle dynamics, and (2) the interaction between central bank communications and monetary policy. These issues are related because central bank communications can only be studied in models with imperfect information. In chapter 1, I investigate the ability of a noisy rational expectations model to generate plausible macroeconomic dynamics. The model allows for imperfect, heterogeneous information, and signal extraction from endogenous variables. I find that imperfect information significantly improves the model's ability to generate persistent, hump-shaped responses to a transitory monetary policy shock. This is achieved without the need for mechanical frictions. In addition, the model generates realistic inflation forecast errors. Chapter 2 explores the relationship between central bank statements about future policy and the degree of commitment. I allow the central bank to make (possibly vague) statements about its expected future policy. I begin by assuming that the central bank adopts a loss function which internalizes the bygone costs of deviating from such a pre-announced policy action. The resulting policy is a convex combination of pure discretion and full commitment. As the precision of central bank statements increases, this policy converges to the full commitment policy. I then show that this type of commitment to internalize bygone costs is sustainable only for moderate degrees of precision. Chapter 3 studies the impact of central bank communications about the state of the economy. In particular, I examine the extent to which increased central bank transparency creates a meaningful trade-off between beneficially conveying fundamental information and adversely contaminating observed data with the central bank's opinion. This question is addressed in a variant of the model from chapter 1. In this environment, both the central bank and private agents learn about the state of the economy from observations of endogenous variables. By making the central bank learn from endogenous variables, I am able to study the impact of communications precision on the bank's signal extraction problem.

This thesis examines two closely related issues: (1) the ability of imperfect information models to explain some aspects of business cycle dynamics, and (2) the interaction between central bank communications and monetary policy. These issues are related because central bank communications can only be studied in models with imperfect information. In chapter 1, I investigate the ability of a noisy rational expectations model to generate plausible macroeconomic dynamics. The model allows for imperfect, heterogeneous information, and signal extraction from endogenous variables. I find that imperfect information significantly improves the model's ability to generate persistent, hump-shaped responses to a transitory monetary policy shock. This is achieved without the need for mechanical frictions. In addition, the model generates realistic inflation forecast errors. Chapter 2 explores the relationship between central bank statements about future policy and the degree of commitment. I allow the central bank to make (possibly vague) statements about its expected future policy. I begin by assuming that the central bank adopts a loss function which internalizes the bygone costs of deviating from such a pre-announced policy action. The resulting policy is a convex combination of pure discretion and full commitment. As the precision of central bank statements increases, this policy converges to the full commitment policy. I then show that this type of commitment to internalize bygone costs is sustainable only for moderate degrees of precision. Chapter 3 studies the impact of central bank communications about the state of the economy. In particular, I examine the extent to which increased central bank transparency creates a meaningful trade-off between beneficially conveying fundamental information and adversely contaminating observed data with the central bank's opinion. This question is addressed in a variant of the model from chapter 1. In this environment, both the central bank and private agents learn about the state of the economy from observations of endogenous variables. By making the central bank learn from endogenous variables, I am able to study the impact of communications precision on the bank's signal extraction problem.