Dissertations / Theses on the topic 'Econometrics – Statistical methods'

This paper studies the use of Monte Carlo simulation techniques in the field of econometrics, specifically statistical inference. First, I examine several estimators by deriving properties explicitly and generate their distributions through simulations. Here, simulations are used to illustrate and support the analytical results. Then, I look at test statistics where derivations are costly because of the sensitivity of their critical values to the data generating processes. Simulations here establish significance and necessity for drawing statistical inference. Overall, the paper examines when and how simulations are needed in studying econometric theories.

We consider the use of a sieve bootstrap based on moving average (MA) and autoregressive moving average (ARMA) approximations to test the unit root hypothesis when the true Data Generating Process (DGP) is a general linear process. We provide invariance principles for these bootstrap DGPs and we prove that the resulting ADF tests are asymptotically valid. Our simulations indicate that these tests sometimes outperform those based on the usual autoregressive (AR) sieve bootstrap. We study the reasons for the failure of the AR sieve bootstrap tests and propose some solutions, including a modified version of the fast double bootstrap.
We also argue that using biased estimators to build bootstrap DGPs may result in less accurate inference. Some simulations confirm this in the case of ADF tests. We show that one can use the GLS transformation matrix to obtain equations that can be used to estimate bias in general ARMA(p,q) models. We compare the resulting bias reduced estimator to a widely used bootstrap based bias corrected estimator. Our simulations indicate that the former has better finite sample properties then the latter in the case of MA models. Finally, our simulations show that using bias corrected or bias reduced estimators to build bootstrap DGP sometimes provides accuracy gains.

Vector autoregression models have become widely used research tools in the analysis of macroeconomic time series. Cointegrated techniques are an essential part of empirical macroeconomic research. They infer causal long-run relationships between nonstationary variables. In this study, six information criteria were reviewed and compared. The methods focused on determining the optimum information criteria for detecting the correct lag structure of a two-variable cointegrated process.

We begin with the outlining the motivation of this research as there are still so many unanswered research questions on our complex financial and economic systems. The philosophical background and the advances of econometrics and econophysics are discussed to provide an overview of the stochastic and nonstochastic modelling and these disciplines are set as a central theme for the thesis. This thesis investigates the effectiveness of financial econometrics models such as Gaussian, ARCH (1), GARCH (1, 1) and its extensions as compared to econophysics models such as Power Law model, Boltzmann-Gibbs (BG) and Tsallis Entropy as statistical models of volatility in US S&P500, Dow Jones and NASDAQ stock index using daily data. The data demonstrate several distinct behavioural characteristics, particularly the increased volatility during 1998 to 2004. Power Laws appear to describe the large fluctuations and other characteristics of stock price changes. Surprisingly, these Power Laws models also show significant correlations for different types and sizes of markets and for different periods and sub-periods of markets. The results show the robustness of Power Law analysis, with the Power Law exponent (0.4 to 2.4) staying within the acceptable range of significance (83% to 97%), regardless of the percentage change in the index return. However, the procedure for testing empirical data against a hypothesised power-law distribution using a simple rank-frequency plot of the data and the data binning process can turn out to be a spurious result for the distribution. As for the stochastic processes such as ARCH (1) and GARCH (1, 1) the models are explicitly confined to the conditional behaviour of the data and the unconditional behaviour has often been described via moments. In reality, it is the unconditional tail behaviour that accounts for the tail behaviour and hence, we have to convert the unconditional tail behaviour and express the models as two-dimensional stochastic difference equation using the processes of Starica (Mikosch 2000). The results show the random walk prediction successfully describes the stock movements for small price fluctuations but fails to handle large price fluctuations. The Power Law tests prove superior to the stochastic tests when stock price fluctuations are substantially divergent from the mean. One of the main points of the thesis is that these empirical phenomena are not present in the stochastic process but emerge in the non-parametric process. The main objective of the thesis is to study the relatively new field of Econophysics and put its work in perspective relative to the established if not altogether successful practice of econometric analysis of stock market volatility. One of the most exciting characteristics of Econophysics is that, as a developing field, no models as yet perfectly represent the market and there is still a lot of fundamental research to be done. Therefore, we begin to explore the application of statistical physics method particularly Tsallis entropy to give a new insights into problems traditionally associated with financial markets. The results of Tsallis entropy surpass all expectations and it is therefore one of the most robust methods of analysis. However, it is now subject to some challenge from McCauley, Bassler et. al., as they found that the stochastic dynamic process (sliding interval techniques) used in fat tail distributions is time dependent.

Thesis (Ph.D.)--University of Western Sydney, 2008.
Thesis submitted to fulfil the requirements for the degree of Doctor of Philosophy in the School of Economics and Finance, College of Business, University of Western Sydney. Includes bibliography.

This thesis focuses on the renewable energy policies for the case study countries (European Union, United States, United Kingdom, Turkey, and Nigeria) with using quantitative and qualitative analysis. The thesis adopts a three -pronged approach to address three main issues: The first paper investigates a 1990-2008 panel dataset to conduct an econometric analysis of policy instruments, such as; feed-in tariffs, quotas, tenders, and tax incentives, in promoting renewable energy deployment in 27 EU countries and 50 US states. The results suggest that renewable energy policy instruments play a significant role in encouraging renewable energy sources. Using data from 1990 to 2012 with the vector auto regression (VAR) approach for three case study countries, namely United Kingdom, Turkey, and Nigeria, the second paper focuses on how renewable energy consumption as part of total electricity consumption is affected by economic growth and electricity prices. The findings from the VAR model illustrate that the relationship between case study countries’ economic growth and renewable energy consumption is positive and economic growth in case study countries respond positively and significantly. The third paper focuses on the relationship between renewable energy policies and investment in renewables in the countries of United Kingdom and Turkey. The third paper builds upon current knowledge of renewable energy investment and develops a new conceptual framework to guide analyses of policies to support renewables. Past and current trends in the field of renewable energy investment are investigated by reviewing the literature on renewable energy investment linkage with policies, which identifies patterns and similarities in RE investment. This also includes the interview analysis with investors focusing on policies for renewable energy investment. The results from the interview and conceptual analysis show that renewable policies play a crucial role in determining investment in renewable energy sources. The findings from this thesis demonstrate that renewable energy policies increase with a growth of the renewable energy investment in the sector. Finally, the outcomes of this thesis also contribute to the energy economics literature, especially for academic and subsequent research purposes.

Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains x, 140 p. Includes bibliographical references (p. 137-140). Available online via OhioLINK's ETD Center

This thesis wants to put together the area of computer science and statistics. For the IT side, the mechanisms of the blockchain technology and classical concept of computer science necessary for understanding it will be outlined. On the other hand, the quantitative part will present the state of the art of machine learning algorithms. The work will end with an empirical chapter where machine learning methods will be compared to classical statistical models. The comparison metric will be the forecasting error of the conditional mean and the conditional variance of timeseries belonging to the cryptocurrency world.

Neste trabalho será feito um modelo espaço-temporal contínuo para preços de imóveis na cidade de São Paulo estimado através de métodos Bayesianos. Faremos uma decomposição da série em tendência e ciclo além de incorporar um conjunto de variáveis explicativas e efeitos aleatórios espaciais projetados no contínuo. Este modelo introduz um novo método para analisar a formação dos preços dos lançamentos imobiliários. Consideramos em nosso modelo hedônico, além das características intrínsecas, também as características da vizinhança e o ambiente econômico. Com este modelo, conseguimos observar os preços de equilíbrio para as respectivas localizações e uma interpretação mais clara da dinâmica de preços dos imóveis entre janeiro de 2000 e dezembro de 2013 para a cidade de São Paulo.
In this work will be made a continuous spatial-temporal model for real estate prices in the city of São Paulo estimated using Bayesian methods. We will decompose the series into a trend and cycle, and incorporate a set of explanatory variables and random spatial effects projected into the continuum. This model introduces a new method to analyze the price formation of real estate launches. We consider in our hedonic model, besides the intrinsic characteristics, also the characteristics of the neighborhood and the economic environment. With this model, we were able to observe the equilibrium prices for the respective locations and a clearer interpretation of the dynamics of real estate prices between January 2000 and December 2013 for the city of São Paulo.

Le projet de thèse présente différentes méthodes empiriques permettant de détecter des cartels. Il vise à démontrer premièrement que des résultats efficaces peuvent être obtenus avec de simples indicateurs statistiques et deuxièmement que les méthodes économétriques traditionnelles ne sont pas aussi efficaces
The PhD studies different empirical methods to detect bid-rigging cartels. It shows first that simple statistical screens perform very well to detect bid-rigging infringement. Second, the econometric method of Bajari, well established in the literature, produces poor results

Neste trabalho utilizamos um modelo espaço-temporal proposto em Rojas (2004) para medir a interação social da criminalidade na região metropolitana de São Paulo. Realizamos simulações de Monte Carlo para testar a capacidade de estimação do modelo em diferentes cenários. Observamos que a estimação melhora com o aumento de observações ao longo do tempo. Já os resultados empíricos indicam que a região metropolitana de São Paulo é um hot spot no estado, pois é encontrado um maior grau de interação social no índice de homicídio em relação aos índices de roubo e furto.
In this paper we employ a spatio-temporal model proposed in Rojas (2004) to evaluate the social interaction in crime in São Paulo metropolitan area. We carry out Monte Carlo simulations to test the model estimation capability in different scenarios. We notice that the estimation gets better as the number of observations in time raises. The results point out that São Paulo metropolitan area is a hot spot in the state since we found out a greater social interaction for the homicide index, compared to robbery and thievery.

Ce mémoire de thèse porte sur la statistique inférentielle des données spatiales et/ou fonctionnelles. En effet, nous nous sommes intéressés à l’estimation de paramètres inconnus de certains modèles à partir d’échantillons obtenus par un processus d’échantillonnage aléatoire ou non (stratifié), composés de variables indépendantes ou spatialement dépendantes.La spécificité des méthodes proposées réside dans le fait qu’elles tiennent compte de la nature de l’échantillon étudié (échantillon stratifié ou composé de données spatiales dépendantes).Tout d’abord, nous étudions des données à valeurs dans un espace de dimension infinie ou dites ”données fonctionnelles”. Dans un premier temps, nous étudions les modèles de choix binaires fonctionnels dans un contexte d’échantillonnage par stratification endogène (échantillonnage Cas-Témoin ou échantillonnage basé sur le choix). La spécificité de cette étude réside sur le fait que la méthode proposée prend en considération le schéma d’échantillonnage. Nous décrivons une fonction de vraisemblance conditionnelle sous l’échantillonnage considérée et une stratégie de réduction de dimension afin d’introduire une estimation du modèle par vraisemblance conditionnelle. Nous étudions les propriétés asymptotiques des estimateurs proposées ainsi que leurs applications à des données simulées et réelles. Nous nous sommes ensuite intéressés à un modèle linéaire fonctionnel spatial auto-régressif. La particularité du modèle réside dans la nature fonctionnelle de la variable explicative et la structure de la dépendance spatiale des variables de l’échantillon considéré. La procédure d’estimation que nous proposons consiste à réduire la dimension infinie de la variable explicative fonctionnelle et à maximiser une quasi-vraisemblance associée au modèle. Nous établissons la consistance, la normalité asymptotique et les performances numériques des estimateurs proposés.Dans la deuxième partie du mémoire, nous abordons des problèmes de régression et prédiction de variables dépendantes à valeurs réelles. Nous commençons par généraliser la méthode de k-plus proches voisins (k-nearest neighbors; k-NN) afin de prédire un processus spatial en des sites non-observés, en présence de co-variables spatiaux. La spécificité du prédicteur proposé est qu’il tient compte d’une hétérogénéité au niveau de la co-variable utilisée. Nous établissons la convergence presque complète avec vitesse du prédicteur et donnons des résultats numériques à l’aide de données simulées et environnementales.Nous généralisons ensuite le modèle probit partiellement linéaire pour données indépendantes à des données spatiales. Nous utilisons un processus spatial linéaire pour modéliser les perturbations du processus considéré, permettant ainsi plus de flexibilité et d’englober plusieurs types de dépendances spatiales. Nous proposons une approche d’estimation semi paramétrique basée sur une vraisemblance pondérée et la méthode des moments généralisées et en étudions les propriétés asymptotiques et performances numériques. Une étude sur la détection des facteurs de risque de cancer VADS (voies aéro-digestives supérieures)dans la région Nord de France à l’aide de modèles spatiaux à choix binaire termine notre contribution
This thesis is about statistical inference for spatial and/or functional data. Indeed, weare interested in estimation of unknown parameters of some models from random or nonrandom(stratified) samples composed of independent or spatially dependent variables.The specificity of the proposed methods lies in the fact that they take into considerationthe considered sample nature (stratified or spatial sample).We begin by studying data valued in a space of infinite dimension or so-called ”functionaldata”. First, we study a functional binary choice model explored in a case-controlor choice-based sample design context. The specificity of this study is that the proposedmethod takes into account the sampling scheme. We describe a conditional likelihoodfunction under the sampling distribution and a reduction of dimension strategy to definea feasible conditional maximum likelihood estimator of the model. Asymptotic propertiesof the proposed estimates as well as their application to simulated and real data are given.Secondly, we explore a functional linear autoregressive spatial model whose particularityis on the functional nature of the explanatory variable and the structure of the spatialdependence. The estimation procedure consists of reducing the infinite dimension of thefunctional variable and maximizing a quasi-likelihood function. We establish the consistencyand asymptotic normality of the estimator. The usefulness of the methodology isillustrated via simulations and an application to some real data.In the second part of the thesis, we address some estimation and prediction problemsof real random spatial variables. We start by generalizing the k-nearest neighbors method,namely k-NN, to predict a spatial process at non-observed locations using some covariates.The specificity of the proposed k-NN predictor lies in the fact that it is flexible and allowsa number of heterogeneity in the covariate. We establish the almost complete convergencewith rates of the spatial predictor whose performance is ensured by an application oversimulated and environmental data. In addition, we generalize the partially linear probitmodel of independent data to the spatial case. We use a linear process for disturbancesallowing various spatial dependencies and propose a semiparametric estimation approachbased on weighted likelihood and generalized method of moments methods. We establishthe consistency and asymptotic distribution of the proposed estimators and investigate thefinite sample performance of the estimators on simulated data. We end by an applicationof spatial binary choice models to identify UADT (Upper aerodigestive tract) cancer riskfactors in the north region of France which displays the highest rates of such cancerincidence and mortality of the country

Examining Board: Professor Jérôme Adda, European University Institute (Supervisor); Professor Andrea Mattozzi, European University Instute; Uta Schönberg, University College London; Professor Andrea Weber, Universität Mannheim.
Defence date: 7 October 2013
First made available online on 4 February 2014.
Institutions, circumstances and interactions between agents shape economic outcomes on the individual and aggregate level. In this thesis I explore three different set ups which combine a theoretical model and an empirical framework to better understand how the wider environment influences behavior and outcomes in markets. The following three papers focus on applications in the areas of economic growth, labor markets and health economics. The global network position of an economy has a profound impact on economic growth. A new measure of economic integration is implemented to characterize economic globalization. Descriptive statistics suggest that this new methodology offers superior possibilities to capture global trends which reflect patterns of interactions between firms and countries. Findings from a modified empirical growth model suggest that a more central global network position fosters economic growth. Robustness checks and alternative estimation strategies address issues of endogeneity and reversed causality in a dynamic panel framework. Social networks and in particular the interaction between applicants, workers and firms influence labor market outcomes. The behavior of firms, workers and applicants during the recruitment process is modeled in a bayesian signaling model which under certain conditions predicts a higher match quality between an applicant and a firm if employee referrals were used. Here, the theoretical model pays special attention to potential incentive problems due to nepotism and favoritism. Empirical results suggest a higher starting wage and a longer duration of the position as well as a different earnings path for workers who learnt about their job through a social network. Individual behavior in terms of consumption depends on the health status. The theoretical concept of state dependent utility functions illustrates that changes in circumstances impact individual behavior such that the health status influences the relative composition of the consumption basket over different categories of goods and services. Results from the empirical framework support this concept and show robust findings for changes in consumption in non durable and semi durable goods which can be linked to the individual health status measured in terms of functional problems to activities of daily living.

Defense date: 11 June 2010
Examining Board: Professor Richard Spady, Johns Hopkins University, Supervisor Professor Luigi Guiso, EUI Professor Daniela Del Boca, Collegio Carlo Alberto, University of Turin Professor Daniele Paserman, Boston University
Recently, there has been strong interest among economists in the impact of social and cultural factors on economic outcomes. For instance, concepts like culture, social capital or social attitudes have been used to explain several individual and group outcomes such as labor supply, health, financial development or economic growth. In this spirit, in this thesis, I explore differences in individuals’ attitudes, their determinants, and their potential to explain individual behavior. The following are some of the findings. Personal and demographic characteristics, especially education, influence attitudes towards the peace process in the Palestinian-Israeli conflict (Chapter 1). Trust influences the type of child care that mothers use, and this has an effect on female labor supply. Since trust differs across European countries, it may explain differences in female labor supply (Chapter 2). Trust also influences individuals’ investment decisions; individuals with less trust tend to invest more in housing and less in financial assets (Chapter 3). Trust and attitudes towards reciprocity affect individuals’ civic engagement differently. People with more trust participate more through existing formal institutions. People with high levels of reciprocity also tend to participate more, but if their levels of trust are not so high, they may choose a more informal (less traditional) way of doing it (Chapter 4). Good measurement of the latent variables (like trust or attitudes towards reconciliation and concessions) is crucial for understanding the effects of individual unobservable traits such as attitudes on observable outcomes, or the effects of observable personal and demographic characteristics on the formation of those attitudes. It also helps overcome the critique sometimes directed at the applied behavioral economics literature that some researchers make claims that go beyond what the statistical results justify. Therefore, one of the goals of this thesis is to use a rigorous measure of these latent variables. To achieve this, I estimate attitudes and the effects of the individuals’ latent traits on specific outcomes using a hierarchical item response model.

We begin with the outlining the motivation of this research as there are still so many unanswered research questions on our complex financial and economic systems. The philosophical background and the advances of econometrics and econophysics are discussed to provide an overview of the stochastic and nonstochastic modelling and these disciplines are set as a central theme for the thesis. This thesis investigates the effectiveness of financial econometrics models such as Gaussian, ARCH (1), GARCH (1, 1) and its extensions as compared to econophysics models such as Power Law model, Boltzmann-Gibbs (BG) and Tsallis Entropy as statistical models of volatility in US S&P500, Dow Jones and NASDAQ stock index using daily data. The data demonstrate several distinct behavioural characteristics, particularly the increased volatility during 1998 to 2004. Power Laws appear to describe the large fluctuations and other characteristics of stock price changes. Surprisingly, these Power Laws models also show significant correlations for different types and sizes of markets and for different periods and sub-periods of markets. The results show the robustness of Power Law analysis, with the Power Law exponent (0.4 to 2.4) staying within the acceptable range of significance (83% to 97%), regardless of the percentage change in the index return. However, the procedure for testing empirical data against a hypothesised power-law distribution using a simple rank-frequency plot of the data and the data binning process can turn out to be a spurious result for the distribution. As for the stochastic processes such as ARCH (1) and GARCH (1, 1) the models are explicitly confined to the conditional behaviour of the data and the unconditional behaviour has often been described via moments. In reality, it is the unconditional tail behaviour that accounts for the tail behaviour and hence, we have to convert the unconditional tail behaviour and express the models as two-dimensional stochastic difference equation using the processes of Starica (Mikosch 2000). The results show the random walk prediction successfully describes the stock movements for small price fluctuations but fails to handle large price fluctuations. The Power Law tests prove superior to the stochastic tests when stock price fluctuations are substantially divergent from the mean. One of the main points of the thesis is that these empirical phenomena are not present in the stochastic process but emerge in the non-parametric process. The main objective of the thesis is to study the relatively new field of Econophysics and put its work in perspective relative to the established if not altogether successful practice of econometric analysis of stock market volatility. One of the most exciting characteristics of Econophysics is that, as a developing field, no models as yet perfectly represent the market and there is still a lot of fundamental research to be done. Therefore, we begin to explore the application of statistical physics method particularly Tsallis entropy to give a new insights into problems traditionally associated with financial markets. The results of Tsallis entropy surpass all expectations and it is therefore one of the most robust methods of analysis. However, it is now subject to some challenge from McCauley, Bassler et. al., as they found that the stochastic dynamic process (sliding interval techniques) used in fat tail distributions is time dependent.
Doctor of Philosophy (PhD)

M.A.
The purpose of this study is to document and build an econometric model of the demand in the South African Clothing industry. It is important to study the clothing industry because it is labour intensive and thus its growth and development could contribute positively toward eradicating the unemployment problem in South Africa. With globalization of world economies and South Africa being a signatory to the GATT/WTO, the implications for this industry are manifold. The opening chapter lists the problem statement, identifies the method of research utilised and the relevance of the study. Chapter two looks at demand theory, particularly with regard to the quantitative techniques involved in its estimation. It focusses on regression theory and the evaluation of results generated. The third chapter gives a background to the South African clothing industry, and touches on amongst others aspects of current importance such as trade reform, international best practice and the key issues the industry has to deal with. Chapter four looks at the econometrics aspects of the study. A near perfect forecast was obtained, which attests to the stability and superiority of the model which is presented. The main findings of this study are that it is supply considerations such as the wage bill, costs of inputs (eg textile materials) etc which play an important part in the survival and prosperity of the industry. It is also reveals the fact that low productivity levels could be easily and quickly rectified through the introduction of new organizational practices and human resource development, development of quick response relationships and training to support new organizational practices. The study further and finally asserts that, while trade reform could necessitate painful adjustments the industry could actually come out a stronger world player

Ongoing globalisation has resulted in more liberalisation, integration, and competition among countries. An upshot of this has been higher levels of cross-border investment. Foreign direct investment (FDI), long considered an engine of growth, has led to widespread probe with its recent rapid spread. Nevertheless, while research on the contribution of FDI to host countries has concentrated heavily on the developed and developing economies, there has been a marked neglect of small, developed economies. This study proposes to focus on New Zealand, a country that falls within the latter category. The study seeks to verify econometrically the impact of FDI on the country through causality links with growth, trade, domestic investment and labour productivity. The analysis is based upon time-series data, the econometric techniques of single, autoregressive distributed lag (ARDL), and the multiple equations approach, vector error correction method (VECM). The study found that there have been substantial gains to the New Zealand economy. A positive effect of FDI on the variables mentioned above led to an improvement of the balance of payments through an increase in exports rather than in imports. Economic growth has mainly been achieved through FDI's impact on exports and domestic private investment. The dynamic innovation techniques indicated a bi-directional causality between FDI and the variables. The long-run causality, however, runs mainly from growth and labour productivity to FDI rather than in the opposite direction. Another noticeable feature is that New Zealand's regional agreement with Australia, Closer Economic Relations, has brought the country significant gains in terms of growth and development through FDI. Both the ARDL and VECM approaches suggest that for a small, developed country qualitative impacts are greater than quantitative ones. The policy implication is that maintaining sustainable economic growth with a positive domestic investment environment is vital for attracting foreign investors. New Zealand, while continuing to encourage inward FDI, should aim to channel it into 'innovative' tradable sectors. The challenge lies in providing the right kind of policy mix for this purpose.

Traditional multivariate statistical theory and applications are often based on specific parametric assumptions. For example it is often assumed that data follow (nearly) normal distribution. In practice such assumption is rarely true and in fact the underlying data distribution is often unknown. Violations of the normality assumption can be detrimental in inference. In particular, two areas affected by violations of assumptions are quadratic discriminant analysis (QDA), used in classification, and principal component analysis (PCA), commonly employed in dimension reduction. Both PCA and QDA involve the computation of empirical covariance matrices of the data. In econometric and financial data, non-normality is often associated with heavy-tailed distributions and such distributions can create significant problems in computing sample covariance matrix. Furthermore, in PCA non-normality may lead to erroneous decisions about numbers of components to be retained due to unexpected behavior of empirical covariance matrix eigenvalues. In the first part of the dissertation, we consider the so called number-of-factors problem in econometric and financial data, which is related to the number of sources of variations (latent factors) that are common to a set of variables observed multiple times (as in time series). The approach that is commonly used in the literature is the PCA and examination of the pattern of the related eigenvalues. We employ an existing technique for robust principal component analysis, which produces properly estimated eigenvalues that are then used in an automatic inferential procedure for the identification of the number of latent factors. In a series of simulation experiments we demonstrate the superiority of our approach compared to other well-established methods. In the second part of the dissertation, we discuss a method to normalize the data empirically so that classical QDA for binary classification can be used. In addition, we successfully overcome the usual issue of large dimension-to-sample-size ratio through regularized estimation of precision matrices. Extensive simulation experiments demonstrate the advantages of our approach in terms of accuracy over other classification techniques. We illustrate the efficiency of our methods in both situations by applying them to real datasets from economics and bioinformatics.

Complex nonlinear dynamic models with an intractable likelihood or moments are increasingly common in economics. A popular approach to estimating these models is to match informative sample moments with simulated moments from a fully parameterized model using SMM or Indirect Inference. This dissertation consists of three chapters exploring different aspects of such simulation-based estimation methods. The following chapters are presented in the order in which they were written during my thesis. Chapter 1, written with Serena Ng, provides an overview of existing frequentist and Bayesian simulation-based estimators. These estimators are seemingly computationally similar in the sense that they all make use of simulations from the model in order to do the estimation. To better understand the relationship between these estimators, this chapters introduces a Reverse Sampler which expresses the Bayesian posterior moments as a weighted average of frequentist estimates. As such, it highlights a deeper connection between the two class of estimators beyond the simulation aspect. This Reverse Sampler also allows us to compare the higher-order bias properties of these estimators. We find that while all estimators have an automatic bias correction property (Gourieroux et al., 1993) the Bayesian estimator introduces two additional biases. The first is due to computing a posterior mean rather than the mode. The second is due to the prior, which penalizes the estimates in a particular direction. Chapter 2, also written with Serena Ng, proves that the Reverse Sampler described above targets the desired Approximate Bayesian Computation (ABC) posterior distribution. The idea relies on a change of variable argument: the frequentist optimization step implies a non-linear transformation. As a result, the unweighted draws follow a distribution that depends on the likelihood that comes from the simulations, and a Jacobian term that arises from the non-linear transformation. Hence, solving the frequentist estimation problem multiple times, with different numerical seeds, leads to an optimization-based importance sampler where the weights depend on the prior and the volume of the Jacobian of the non-linear transformation. In models where optimization is relatively fast, this Reverse Sampler is shown to compare favourably to existing ABC-MCMC or ABC-SMC sampling methods. Chapter 3, relaxes the parametric assumptions on the distribution of the shocks in simulation-based estimation. It extends the existing SMM literature, where even though the choice of moments is flexible and potentially nonparametric, the model itself is assumed to be fully parametric. The large sample theory in this chapter allows for both time-series and short-panels which are the two most common data types found in empirical applications. Using a flexible sieve density reduces the sensitivity of estimates and counterfactuals to an ad hoc choice of distribution such as the Gaussian density. Compared to existing work on sieve estimation, the Sieve-SMM estimator involves dynamically generated data which implies non-standard bias and dependence properties. First, the dynamics imply an accumulation of the bias resulting in a larger nonparametric approximation error than in static models. To ensure that it does not accumulate too much, a set decay conditions on the data generating process are given and the resulting bias is derived. Second, by construction, the dependence properties of the simulated data vary with the parameter values so that standard empirical process results, which rely on a coupling argument, do not apply in this setting. This non-standard dependent empirical process is handled through an inequality built by adapting results from the existing literature. The results hold for bounded empirical processes under a geometric ergodicity condition. This is illustrated in the paper with Monte-Carlo simulations and two empirical applications.

My dissertation consists of three chapters. In the first chapter, I analyze theeffect of the merger between American Airlines (AA) & US Airways (US) on market price and product quality. I use two complementary methodologies: difference-in-differences (DID) and merger simulation. Contrary to other results in the airline literature, the DID analysis shows that, overall, price has decreased as a result of themerger. While divestitures required as part of the merger had a strong price-reducing effect, the overall decrease involves non-divestiture markets as well. Interestingly, the decrease appears only in large airport-pair markets, whereas prices rose considerably in smaller ones. Effects on quality are mixed. The DID analysis shows that the merger reduced flight cancellations, increased flight delays, and had no effect on flight frequency or capacity overall. Using merger simulation, I find that the change in ownership leads to a 3% increase in price. The structural model performs betterin predicting the post-merger price if I allow the model to deviate from the Bertrand-Nash conduct. A 10% cost reduction due to the merger is able to predict the post-merger price quite well. When I incorporate a conduct parameter into the model, the required percentage of cost savings is lower. Given the divestiture and the subsequententry of low-cost carriers (LCCs), tacit collusion may break down. Thus both cost savings and reduced cooperation could explain a reduction in the price in the post-merger period.

Second Essay: We estimate the cost of transporting corn and the resulting degree of spatial differentiation among downstream firms that buy corn from upstream farmers and examine whether such differentiation softens competition enabling buyers to exert market power (defined as the ability to pay a price for corn that is below its marginal value product net of processing cost). We estimate a structural model of spatial competition using corn procurement data from the US state of Indiana from 2004 to 2014. We adopt a strategy that allows us to estimate firm-level structural parameters while using aggregate data. Our results return a transportation cost of 0.12 cents per bushel per mile (3% of the corn price under average conditions), which provides evidence of spatial differentiation among buyers. The estimated average markdown is $0.80 per bushel (16% of the average corn price in the sample), of which $0.34 is explained by spatial differentiation and the rest by the fact that firms operated under binding capacity constraints. We also find that corn prices paid to farmers at the mill gate are independent of distance between the plant and the farm, providing evidence that firms do not engage in spatial price discrimination. Finally, we evaluate the effect of hypothetical mergers on input markets and farm surplus. A merger between nearby ethanol producers eases competition, increases markdowns by 20%, and triggers a sizable reduction in farm surplus. In contrast, a merger between distant buyers has little effect on competition and markdowns.

Third Essay: We study the dynamic response of local corn prices to entry of ethanol plants. We use spatially explicit panel data on elevator-level corn prices and ethanol plant entry and capacity to estimate an autoregressive distributed lag model with instrumental variables. We find that the average-sized entrant has no impact on local corn prices the year of entry. However, the price subsequently rises and stabilizes after two years at a level that is about 10 cents per bushel higher than the pre-entry level. This price effect dissipates as the distance between elevators and plants increase. Our results imply that long-run (2 years) supply elasticity is smaller than short-run (year of entry) supply elasticity. This may be due to rotation benefits that induce farmers to revert back to soybeans, after switching to corn due to price signals the year the plant enters. Furthermore, our results, in combination with findings in essay 2 of this dissertation, indicate that ethanol plants are likely to use pricing strategies consistent with a static rather than dynamic oligopsony competition.