This study uses a random coefficient estimation procedure to analyze the U.S. gasoline market from 1960-1995 with three main objectives: (1) provide an empirical methodology that can estimate a gasoline demand function capable of performing well in prediction; (2) evaluate the elasticities of the models presented to determine which model is more accurate at capturing supply shocks that impacted gasoline demand; and (3) evaluate the behavior of the elasticites of the beta coefficients. This research will show that the variation from historical economic patterns was a result of supply shocks. I argue that when the OLS model of the gasoline market developed by William H. Greene is used supply shocks are not well captured because the coefficients are fixed. If the random coefficient model developed by P.A.V.B. Swamy is introduced, the coefficients vary over time, and thereby, enable supply shocks to be included in the model and more accurate forecasts are produced, as well as, meaningful time patterns in the beta coefficients. / Master of Arts

<p>This dissertation consists of three chapters relating to dynamic demand models of storable goods and their application to taxes that are imposed on soft drinks. Broadly speaking, the first chapter builds the estimation strategy for dynamic demand models of storable goods that allows for unobservable heterogeneous preferences in household's tastes. The second chapter uses the estimation strategy developed in the first chapter to study the policy implications of taxes that are imposed on sugary soft drinks. The last chapter explores and provides an explanation for the level of pass-through for soda taxes. </p><p>To be more specific, the first chapter develops techniques for incorporating systematic brand preferences in dynamic demand models of storable goods. Dynamic demand models are important for correctly measuring price elasticities of products that can be stockpiled. However, most of the literature excludes systematic preferences over consumers' brand tastes. This chapter resolves this issue by incorporating random coefficient Logit models into a dynamic demand framework and hence allows for realistic demand substitution patterns. It builds on Hendel and Nevo's 2006 Econometrica paper, where the authors introduce a model of dynamic demand that flexibly incorporates observable heterogeneity and estimates it via a three-step procedure that separates brand and volume choices. While a powerful tool, this method is tricky to execute. Therefore, this chapter also discusses the difficulties that may face implementers.</p><p>The second chapter predicts the effects of taxes on sugar sweetened soft drinks (sugar taxes) on both total consumption and the welfare of different types of consumers. It specifies and estimates a structural dynamic demand model of storable goods with rational and forward-looking households. It flexibly incorporates persistent heterogeneous consumer preferences and develops a computationally attractive method for estimating its parameters. Sugar taxes have been proposed at both the national and state-level, and passed in three states, as a means of slowing or reversing the growth in obesity and diabetes. To accurately analyze the effects of these policies, this chapter takes two specific aspects of soft drinks into account: storability and differentiation. It compares the results from this model to two benchmark studies: a static model with consumer heterogeneity and a dynamic model without households' persistent heterogeneous tastes. It finds that failing to account for dynamics (i.e. storability) results in overestimated reduction in consumption and failing to account for persistent heterogeneous preferences (i.e. differentiation) results in overestimated reduction in consumption and underestimated welfare loss. The model and method developed here are readily applicable to many studies involving storable goods, such as firms' optimal pricing behavior and anti-trust policies analyses.</p><p>The third and last chapter focuses on the incidence of soda taxes by studying the pass-through level of these taxes. It lays out a framework for thinking about the determinants of the pass-through level. More specifically, it builds theoretical models that examine the pass-through under more complex supply structures with multiple manufactures and retailers. In addition to providing some intuition behind theoretical predictions of the models, this chapter also presents empirical results found in the data along with their implications.</p> / Dissertation

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.

Spillover phenomena are usually statistically estimated on the basis of regional and temporal panel data. In this paper we review and investigate exploratory and confirmatory statistical panel data techniques. We illustrate the methods by calculations in the stetting of the well known Research and Development Spillover study by Coe and Helpman (1995). It will be demonstrated that alternative estimation techniques that are well compatible with the data can lead to opposite conclusions. (author's abstract) / Series: Working Papers Series "Growth and Employment in Europe: Sustainability and Competitiveness"

Submitted by Tomás Milanez Ferreira Pinto (tomasmilanez@gmail.com) on 2015-04-28T23:38:11Z No. of bitstreams: 1 mestrado.pdf: 973554 bytes, checksum: ecd785af01da846a4fe24cc7d9882091 (MD5) / Approved for entry into archive by BRUNA BARROS (bruna.barros@fgv.br) on 2015-04-29T20:32:23Z (GMT) No. of bitstreams: 1 mestrado.pdf: 973554 bytes, checksum: ecd785af01da846a4fe24cc7d9882091 (MD5) / Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2015-05-04T12:28:50Z (GMT) No. of bitstreams: 1 mestrado.pdf: 973554 bytes, checksum: ecd785af01da846a4fe24cc7d9882091 (MD5) / Made available in DSpace on 2015-05-04T12:29:05Z (GMT). No. of bitstreams: 1 mestrado.pdf: 973554 bytes, checksum: ecd785af01da846a4fe24cc7d9882091 (MD5) Previous issue date: 2015-01-30 / Consumers often pay different prices for the same product bought in the same store at the same time. However, the demand estimation literature has ignored that fact using, instead, aggregate measures such as the 'list' or average price. In this paper we show that this will lead to biased price coefficients. Furthermore, we perform simple comparative statics simulation exercises for the logit and random coefficient models. In the 'list' price case we find that the bias is larger when discounts are higher, proportion of consumers facing discount prices is higher and when consumers are more unwilling to buy the product so that they almost only do it when facing discount. In the average price case we find that the bias is larger when discounts are higher, proportion of consumers that have access to discount are similar to the ones that do not have access and when consumers willingness to buy is very dependent on idiosyncratic shocks. Also bias is less problematic in the average price case in markets with a lot of bargain deals, so that prices are as good as individual. We conclude by proposing ways that the econometrician can reduce this bias using different information that he may have available.

In this paper we address the problem of negative estimates of willingness to pay. We find that there exist a number of goods and services, especially in the fields of marketing and environmental valuation, for which only zero or positive WTP is meaningful. For the valuation of these goods an econometric model for the analysis of repeated dichotomous choice data is proposed. Our model restricts the domain of the estimates of WTP to strictly positive values, while also allowing for the detection of zero WTP. The model is tested on a simulated and a real data set.

In recent years there were many debates and different opinions whether R&D spillover effects exist or not. In 1995 Coe and Helpman published a study about this phenomenon, based on a panel dataset, that supports the position that such R&D spillover effects are existent. However, this survey was criticized and many different suggestions for improvement came from the scientific community. Some of them were selected and analysed and finally led to a new model. And even though this new model is well compatible with the data, it leads to different conclusions, namely that there does not exist an R&D spillover effect. These different results were the motivation to run a spatial analysis, which can be done by considering the countries as regions and using an adequate spatial link matrix. The used methods from the field of spatial econometrics are described briefly and quite general, and finally the results from the spatial models (the ones which correspond to the non-spatial ones) are compared with the results from the non-spatial analysis. The preferred model supports the position that R&D spillover effects exist.

The subsurface flow is usually subject to uncertain porous media structures. However, in most cases we only have partial knowledge about the porous media properties. A common approach is to model the uncertain parameters as random fields, then the expectation of Quantity of Interest(QoI) can be evaluated by the Monte Carlo method. In this study, we develop a full multigrid-multilevel Monte Carlo (FMG-MLMC) method to speed up the evaluation of random parameters effects on single-phase porous flows. In general, MLMC method applies a series of discretization with increasing resolution and computes the QoI on each of them, the success of which lies in the effective variance reduction. We exploit the similar hierarchies of MLMC and multigrid methods, and obtain the solution on coarse mesh Qcl as a byproduct of the multigrid solution on fine mesh Qfl on each level l. In the cases considered in this thesis, the computational saving is 20% theoretically. In addition, a comparison of Monte Carlo and Quasi-Monte Carlo (QMC) methods reveals a smaller estimator variance and faster convergence rate of the latter method in this study.

This thesis studies novel and efficient computational sampling methods for appli- cations in three types of stochastic inversion problems: seismic waveform inversion, filtering problems, and static parameter estimation. A primary goal of a large class of seismic inverse problems is to detect parameters that characterize an earthquake. We are interested to solve this task by analyzing the full displacement time series at a given set of seismographs, but approaching the full waveform inversion with the standard Monte Carlo (MC) method is prohibitively expensive. So we study tools that can make this computation feasible. As part of the inversion problem, we must evaluate the misfit between recorded and synthetic seismograms efficiently. We employ as misfit function the Wasserstein metric origi- nally suggested to measure the distance between probability distributions, which is becoming increasingly popular in seismic inversion. To compute the expected values of the misfits, we use a sampling algorithm called Multi-Level Monte Carlo (MLMC). MLMC performs most of the sampling at a coarse space-time resolution, with only a few corrections at finer scales, without compromising the overall accuracy. We further investigate the Wasserstein metric and MLMC method in the context of filtering problems for partially observed diffusions with observations at periodic time intervals. Particle filters can be enhanced by considering hierarchies of discretizations to reduce the computational effort to achieve a given tolerance. This methodology is called Multi-Level Particle Filter (MLPF). However, particle filters, and consequently MLPFs, suffer from particle ensemble collapse, which requires the implementation of a resampling step. We suggest for one-dimensional processes a resampling procedure based on optimal Wasserstein coupling. We show that it is beneficial in terms of computational costs compared to standard resampling procedures. Finally, we consider static parameter estimation for a class of continuous-time state-space models. Unbiasedness of the gradient of the log-likelihood is an important property for gradient ascent (descent) methods to ensure their convergence. We propose a novel unbiased estimator of the gradient of the log-likelihood based on a double-randomization scheme. We use this estimator in the stochastic gradient ascent method to recover unknown parameters of the dynamics.

Title: Models of integer-valued time series with random coefficients Author: Petra Burdejová Department: Department of Probability and Mathematical Statistics Supervisor: Doc. RNDr. Zuzana Prášková, CSc. Abstract: In the presented thesis, a generalized integer-valued autoregres- sive process of the order p (GINAR(p)) is considered first. The main aim is taken to introduction of random coefficient integer-valued autoregressive process (RCINAR(p)). We use a thinning operator in order to define the processes. The main characteristics of GINAR(p) and RCINAR(p) are obtained. Condi- tions for stationarity and ergodicity are stated. Three methods of estimation (Yule-Walker, Conditional least squares, Generalized method of moments) are given and compared in simulation with respect to the mean squared error (MSE). At the end, RCINAR(3) model is applied to a real dataset representing a number of earthquakes per year. Keywords: thinning operator, random coefficients, integer-valued time se- ries, GINAR, RCINAR