This thesis contains two essays that use state-of-the-art econometric methods to estimate the implicit prices of various housing and vehicle attributes in hedonic price analysis. The additive nonparametric regression proposed by Hastie and Tibshirani (1990) is applied to capture a series of nonlinearities relating prices to their attributes that cannot be captured by conventional parametric approach. Due to its additive structure, the additive nonparametric regression retains an important interpretative feature of the linear model and avoids the drawbacks of a fully nonparametric design such as slow rates of convergence and the "curse of dimensionality." The "benchmark" parametric specification for the hedonic price function is carefully chosen via the estimation of the Box and Cox (1964) and Wooldridge (1992) transformations. The additive nonparametric model provides smaller price prediction errors than the benchmark parametric specification in standard goodness of fit measures. The first study examines the effects on housing prices of the structural and environmental attributes using residential sales data from Portland, Oregon. The overall estimation results verify that most housing attributes that are generally linked to the perception of quality, such as larger total structure square footage and higher elevation, have significant positive implicit prices. Attributes that reduce house quality, such as age of house and distance to environmental amenities, discount the value of a house. Complex price effects of various housing attributes are revealed by the additive nonparametric regression. The second study uses a hedonic price approach to estimate the effects on used car prices of vehicle emission attributes, such as hydrocarbon and carbon monoxide emissions, using data from the Vehicle Inspection Program in Portland, Oregon. The estimation results show that used car value is on average higher for vehicles with lower hydrocarbon and carbon monoxide emissions, ceteris paribus. This empirical finding is consistent with recent reports from the U.S. Environmental Protection Agency, which indicate that used vehicles failing to pass required emission tests face potentially high repair costs and frequent smog-check requirements. More cylinders and larger engine size are highly valued. Higher mileage receives relatively little discount compared to age of vehicle. / Graduation date: 2000

Not available / text

The Best PhD Thesis in the Faculties of Architecture, Arts, Business & Economics, Education, Law and Social Sciences (University of Hong Kong), Li Ka Shing Prize, 2006-2007. / published_or_final_version / abstract / Real Estate and Construction / Doctoral / Doctor of Philosophy

by Ma Chun Ho Eric. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 102-105). / Abstracts in English and Chinese. / INTERNET ROUTING AND PRICING --- p.1 / ABSTRACT --- p.I / ACKNOWLEDGEMENTS --- p.III / LIST OF FIGURES --- p.IV / LIST OF TABLES --- p.VI / CONTENTS --- p.VII / Chapter CHAPTER1 --- Introduction --- p.1 / Chapter 1.1 --- What is Internet? --- p.1 / Chapter 1.2 --- Internet Routing and Pricing --- p.3 / Chapter 1.3 --- Overview of QoS Routing --- p.4 / Chapter 1.3.1 --- Classification of Routing --- p.6 / Chapter 1.3.2 --- Optimal Routing --- p.7 / Chapter 1.4 --- An Introduction to Internet Economics --- p.8 / Chapter 1.4.1 --- Internet Externality --- p.9 / Chapter 1.4.2 --- Current Pricing Practice --- p.10 / Chapter 1.4.3 --- Network Interconnection --- p.14 / Chapter 1.4 --- Organization of Thesis --- p.16 / Chapter CHAPTER2 --- Economic Theory for Interconnection Model --- p.18 / Chapter 2.1 --- Introduction --- p.18 / Chapter 2.2 --- Demand and Supply --- p.20 / Chapter 2.2.1 --- Consumer Behavior --- p.20 / Chapter 2.2.2 --- Demand Curve --- p.25 / Chapter 2.2.3 --- Price Elasticity --- p.30 / Chapter 2.2.4 --- Estimation of Market Demand --- p.32 / Chapter 2.3 --- Market Structure --- p.33 / Chapter 2.3.1 --- Competitive Firm --- p.34 / Chapter 2.3.2 --- Monopoly --- p.35 / Chapter 2.3.3 --- Oligopoly --- p.35 / Chapter 2.4 --- Game Theory --- p.35 / Chapter 2.4.1 --- The Payoff Matrix of a game --- p.36 / Chapter 2.4.2 --- Nash Equilibrium --- p.37 / Chapter 2.4.3 --- Mixed Strategies --- p.38 / Chapter 2.4.4 --- Existence of Nash Equilibrium --- p.39 / Chapter 2.5 --- Summary --- p.39 / Chapter CHAPTER3 --- Problem Formulation Interconnection Network for Pricing and Routing in Internet --- p.40 / Chapter 3.1 --- Introduction --- p.40 / Chapter 3.2 --- Problem Formulation --- p.41 / Chapter 3.2 --- Existence of NEP Interconnection Network --- p.46 / Chapter 3.3 --- "A ""Cookbook"" Procedure" --- p.53 / Chapter 3.4 --- Cookbook Examples --- p.54 / Chapter 3.5 --- Summary --- p.65 / Chapter CHAPTER4 --- Price Competition for Interconnection Models --- p.66 / Chapter 4.1 --- Introduction --- p.66 / Chapter 4.2 --- Competitive Pricing of Parallel Networks --- p.66 / Chapter 4.2.1 --- Model and Problem Formulation --- p.67 / Chapter 4.2.2 --- Existence of Nash Equilibrium Point --- p.68 / Chapter 4.2.3 --- Numerical Example and Properties --- p.71 / Chapter 4.3 --- Price Collusion for Serial Networks --- p.75 / Chapter 4.3.1 --- Model and Problem Formulation --- p.75 / Chapter 4.3.2 --- Existence of Nash Equilibrium Point --- p.77 / Chapter 4.3.3 --- Numerical Example and Properties --- p.79 / Chapter 4.4 --- Summary --- p.83 / Chapter CHAPTER5 --- Price Distortion for Series-Parallel Networks with Dominant Carriers --- p.85 / Chapter 5.1 --- Problem Motivation and Formulation --- p.85 / Chapter 5.2 --- Properties under NEP --- p.90 / Chapter 5.3 --- A Simple Simulation --- p.95 / Chapter 5.5 --- Summary --- p.98 / Chapter CHAPTER6 --- Conclusion --- p.99 / BIBLIOGRAPHY --- p.102

This dissertation studies statistical decision making in various guises. I start by providing a general decision theoretic model of statistical behavior, and then analyze two particular instances which fit in that framework. Chapter 1 studies statistical decision theory (SDT), a class of models pioneered by Abraham Wald to analyze how agents use data when making decisions under uncertainty. Despite its prominence in information economics and econometrics, SDT has not been given formal choice-theoretic or behavioral foundations. This chapter axiomatizes preferences over decision rules and experiments for a broad class of SDT models. The axioms show how certain seemingly-natural decision rules are incompatible with this broad class of SDT models. Using those representation result, I then develop a methodology to translate axioms from classical decision-theory, a la Anscombe and Aumann (1963), to the SDT framework. The usefulness of this toolkit is then illustrated by translating various classical axioms, which serve to refine my baseline framework into more specific statistical decision theoretic models, some of which are novel to SDT. I also discuss foundations for SDT under other kinds of choice data. Chapter 2 studies statistical identifiability of finite mixture models. If a model is not identifiable, multiple combinations of its parameters can lead to the same observed distribution of the data, which greatly complicates, if not invalidates, causal inference based on the model. High-dimensional latent parameter models, which include finite mixtures, are widely used in economics, but are only guaranteed to be identifiable under specific conditions. Since these conditions are usually stated in terms of the hidden parameters of the model, they are seldom testable using noisy data. This chapter provides a condition which, when imposed on the directly observable mixture distribution, guarantees that a finite mixture model is non-parametrically identifiable. Since the condition relates to an observable quantity, it can be used to devise a statistical test of identification for the model. Thus I propose a Bayesian test of whether the model is close to being identified, which the econometrician may apply before estimating the parameters of the model. I also show that, when the model is identifiable, approximate non-negative matrix factorization provides a consistent, likelihood-free estimator of mixture weights. Chapter 3 studies the robustness of pricing strategies when a firm is uncertain about the distribution of consumers' willingness-to-pay. When the firm has access to data to estimate this distribution, a simple strategy is to implement the mechanism that is optimal for the estimated distribution. We find that such an empirically optimal mechanism boasts strong profit and regret guarantees. Moreover, we provide a toolkit to evaluate the robustness properties of different mechanisms, showing how to consistently estimate and conduct valid inference on the profit generated by any one mechanism, which enables one to evaluate and compare their probabilistic revenue guarantees.

Internet Service Providers, or ISPs, like any other rational entity make decisions to maximize their profit. While some of their decisions are on how to attract customers, they inevitably need to control how much resources consumers utilize. In this dissertation, we focus on two different aspects of ISP's decisions, including bandwidth allocation and pricing techniques through which ISPs manage allotting their limited capacity to users with high demand, and zero-rating, which can be one of the tools through which the ISP can attract customers. For bandwidth allocation, this dissertation discusses the data plans available for each user's monthly billing cycle. Within those, the ISPs guarantee a fixed amount of data at high rates until a byte threshold is reached, at which point the user's data rate is throttled to a lower rate for the remainder of the cycle. In practice, the thresholds and rates of throttling can appear and may be somewhat arbitrary. In this dissertation, we evaluate the choice of threshold and rate as an optimization problem (regret minimization) and demonstrate that intuitive formulations of client regret, which preserve desirable fairness properties, lead to optimization problems that have tractably computable solutions. For zero-rating options in the ISP market, and their relation to net neutrality, we begin by introducing the concept of zero-rating, which refers to the practice of providing free Internet access to some users under certain conditions, and usually concurs with differentiation among users or content providers. Even though zero-rating is banned in some countries (India, Canada), others have either taken no stance or explicitly allowed it (South Africa, Kenya, U.S.). While there is broad agreement that preserving the content quality of service falls under the purview of net neutrality, the role of differential pricing, especially the practice of \emph{zero-rating} remains controversial. An objective of net neutrality is to design regulations for the Internet and ensure that it remains a public, open platform where innovations can thrive. We show the practice of zero-rating does not agree with that. This dissertation shows how ISPs could make zero-rating decisions to attract customers, and then show how these decisions may negatively impact the market and customer welfare, which necessitates the existence of some zero-rating regulations.

published_or_final_version / Economics and Finance / Master / Master of Economics

This thesis studies four independent resource allocation problems with different assumptions on information available to the central planner, and strategic considerations of the agents present in the system. We start off with an online, non-strategic agents setting in Chapter 1, where we study the dynamic pricing and learning problem under the Bass demand model. The main objective in the field of dynamic pricing and learning is to study how a seller can maximize revenue by adjusting price over time based on sequentially realized demand. Unlike most existing literature on dynamic pricing and learning, where the price only affects the demand in the current period, under the Bass model, price also influences the future evolution of demand. Finding arevenue-maximizing dynamic pricing policy in this model is non-trivial even in the full information case, where model parameters are known. We consider the more challenging incomplete information problem where dynamic pricing is applied in conjunction with learning the unknown model parameters, with the objective of optimizing the cumulative revenues over a given selling horizon of length 𝑻. Our main contribution is an algorithm that satisfies a high probability regret guarantee of order 𝑚²/³; where the market size 𝑚 is known a priori. Moreover, we show that no algorithm can incur smaller order of loss by deriving a matching lower bound. We then switch our attention to a single round, strategic agents setting in Chapter 2, where we study a multi-resource allocation problem with heterogeneous demands and Leontief utilities. Leontief utility function captures the idea that for certain resource allocation settings, the utility of marginal increase in one resource depends on the availabilities of other resources. We generalize the existing literature on this model formulation to incorporate more constraints faced in real applications, which in turn requires new algorithm design and analysis techniques. The main contribution of this chapter is an allocation algorithm that satisfies Pareto optimality, envy-freenss, strategy-proofness, and a notion of sharing incentive. In Chapter 3, we study a single round, non-strategic agent setting, where the central planner tries to allocate a pool of items to a set of agents who each has to receive a prespecified fraction of all items. Additionally, we want to ensure fairness by controlling the amount of envy that agents have with the final allocations. We make the observation that this resource allocation setting can be formulated as an Optimal Transport problem, and that the solution structure displays a surprisingly simple structure. Using this insight, we are able to design an allocation algorithm that achieves the optimal trade-off between efficiency and envy. Finally, in Chapter 4 we study an online, strategic agent setting, where similar to the previous chapter, the central planner needs to allocate a pool of items to a set of agents who each has to receive a prespecified fraction of all items. Unlike in the previous chapter, the central planner has no a priori information on the distribution of items. Instead, the central planner needs to implicitly learn these distributions from the observed values in order to pick a good allocation policy. Additionally, an added challenge here is that the agents are strategic with incentives to misreport their valuations in order to receive better allocations. This sets our work apart both from the online auction mechanism design settings which typically assume known valuation distributions and/or involve payments, and from the online learning settings that do not consider strategic agents. To that end, our main contribution is an online learning based allocation mechanism that is approximately Bayesian incentive compatible, and when all agents are truthful, guarantees a sublinear regret for individual agents' utility compared to that under the optimal offline allocation policy.

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.

The study of macroeconomics and finance has evolved tremendously over the last few decades---significant advancements have taken place in both gaining access to an increasing scale and scope of observational, policy and private data, as well as empirical methods to derive novel economic insights from such data. In this dissertation, I attempt to shed light on three problems broadly across macroeconomics and asset pricing, taking a data-driven approach to answer them. For the first essay, we construct a novel dataset which captures the geographic incidence of government revenues and expenditures. Government revenues and expenditures revenues and expenditures occur on three different levels in the United States: local, state, and federal. At all levels, government revenues and expenditures add and subtract resources from the private sector. The dataset encompasses all revenues and expenditures at the county-level and thus allows to see the net resource allocation through the government. We use this dataset to document several new facts about the relationship between economic activity and the resource allocation by the government. The governments' resource allocation is generally redistributive. That is, levels and changes of median income are associated with the level and changes of net resources. Second, we evaluate response of governmental revenues and spending in response to the China shock. We find a decline in total governmental receipts in counties that are hardest hit, while a muted response of total governmental spending. The aggregate response conceals a lot of heterogeneity: a decomposition at the governmental level shows an increase in expenditures and lower receipts at the federal level; at the local and state level we find a simultaneous reduction of receipts and spending. The latter is a consequence of the balanced budget constraint. Overall, total government spending is approximately constant while total receipts are falling. As a result, the insurance function of the federal government is offset by a reduction at the state and local level which renders total government spending neutral to the China shock. This stands in contrast to prior research which has focused on the federal response. In our second essay, we attempt to answer the question---how should an investor value financial data? The answer is complicated because it depends on the characteristics of all investors. We develop a sufficient statistics approach that uses equilibrium asset return moments to summarize all relevant information about others' characteristics. It can value data that is public or private, about one or many assets, relevant for dividends or for sentiment. While different data types, of course, have different valuations, heterogeneous investors also value the same data very differently, which suggests a low price elasticity for data demand. Heterogeneous investors' data valuations are also affected very differentially by market illiquidity. Lastly, in the third essay, we examine the economic impact of droughts on asset markets, specifically on land valuation. Specifically, we focus on farmland valuations in California---one of the most productive farmlands in the world. The semi-arid climate makes its valuation particularly sensitive to the amount of surface and groundwater water available for irrigation. The detailed administrative transaction data from the counties' assessor offices allows us to estimate repeat sales indices as opposed to a hedonic model which make our results less likely to be affected by omitted variables. We find that parcels with better access to freshwater see a larger appreciation in land values from 2011 to 2020; whereas we find no statistical significant differential price change between 2000-2011. The differential change in land values points towards large economic effects of water scarcity with beliefs about future climatic conditions being updated due to two severe episodes of drought and signals of legislative willingness to curb groundwater overdraft.