How resources are allocated across plants is crucial for understanding cross-country output and productivity differences. This thesis contributes to the growing literature on resource misallocation by studying the particular channels through which misallocation can arise. In Chapter 1, I examine extortion at the plant-level, its effects on individual incentives to become an entrepreneur, and how production is affected by the presence of extortion. I show that extortion is especially burdensome on moderate-ability entrepreneurs forcing them to either forgo entry into entrepreneurship or produce at an inefficiently small scale. When property rights are weak, the frequency of extortion is higher producing a society where much of it's entrepreneurial talent is heavily under-utilized. In Chapter 2, I study plant-level distortions and it's effects on incentives to improve productivity. I build a model where plant innovation improves future productivity so that productivity dynamics are endogenous. Distortions that are tied to productivity are introduced to the model to examine how plant innovation is affected. All plants lower innovation resulting in a distribution over productivity that is right-skewed and a distribution over plant size that is left-skewed, consistent with empirical findings in developing countries. The final Chapter is closely related to Chapter 1 but is more empirically focused. I study the role of theft as a means to explaining the abundance of small plants in developing countries and estimate the causal effect of theft on plant capital demand. I find that plant capital would be significantly higher if theft is eliminated.

This thesis develops new hidden Markov models and applies them to financial market and macroeconomic time series. Chapter 1 proposes a probabilistic model of the return distribution with rich and heterogeneous intra-regime dynamics. It focuses on the characteristics and dynamics of bear market rallies and bull market corrections, including, for example, the probability of transition from a bear market rally into a bull market versus back to the primary bear state. A Bayesian estimation approach accounts for parameter and regime uncertainty and provides probability statements regarding future regimes and returns. A Value-at-Risk example illustrates the economic value of our approach. Chapter 2 develops a new efficient approach to model and forecast time series data with an unknown number of change-points. The key is assuming a conjugate prior for the time-varying parameters which characterize each regime and treating the regime duration as a state variable. Conditional on this prior and the time-invariant parameters, the predictive density and the posterior of the change-points have closed forms. The conjugate prior is further modeled as hierarchical to exploit the information across regimes. This framework allows breaks in the variance, the regression coefficients or both. In addition to the time-invariant structural change probability, one extension assumes the regime duration has a Poisson distribution. A new Markov Chain Monte Carlo sampler draws the parameters from the posterior distribution efficiently. The model is applied to Canadian inflation time series. Chapter 3 proposes an infinite dimension Markov switching model to accommodate regime switching and structural break dynamics or a combination of both in a Bayesian framework. Two parallel hierarchical structures, one governing the transition probabilities and another governing the parameters of the conditional data density, keep the model parsimonious and improve forecasts. This nonparametric approach allows for regime persistence and estimates the number of states automatically. A global identification algorithm for structural changes versus regime switching is presented. Applications to U.S. real interest rates and inflation compare the new model to existing parametric alternatives. Besides identifying episodes of regime switching and structural breaks, the hierarchical distribution governing the parameters of the conditional data density provides significant gains to forecasting precision.

This dissertation studies three important questions in international political economy: The long run consequences of social divisions created by historical colonialism, the importance of trade shocks in shifting political power balances and shaping institutional development and the influence that major political powers have over the decisions of smaller nations. I study these three questions empirically in four papers that span three distinct regions and time periods. The first paper asks whether the large differences in economic development across Native American reservations today can be explained by social divisions that were created more than 150 years ago when the US government forcibly integrated distinct Native American bands into shared reservations, condemning them to a system of shared governance that was not consistent with their political traditions and tribal identities. The second and third papers study the effect of the first globalization on the political and economic equilibrium in seventeen 19th century British Caribbean plantation colonies. I use this set of highly comparable but in precise ways distinct islands as a laboratory to study the effect of globalization on the long run development of representative institutions and on the coerciveness of labour markets at the time. The first of two papers provides insights into the working of colonial institutions and traces the mechanisms through which the planter elite managed to maintain a monopoly over policy making and retard long run development. The second paper highlights the importance that exogenous output price changes had for the willingness of planter elites to engage in costly coercion that distorted labour markets in their favour. In the final paper I test whether major aid donors use foreign aid to buy the votes of developing countries. Taking advantage of a unique long-running dispute between major donors in the International Whaling Commission, I am able to address the three major empirical challenges in answering this question: that aid moves much slower than voting behaviour, that alliances constantly change with issues and that most international organizations vote frequently and on a range of issues while data on aid disbursals is available only in yearly aggregates.

This thesis consists of three essays in empirical finance and macroeconomics. The first essay proposes a new structural-break vector autoregressive model for predicting real output growth by the nominal yield curve. The model allows for the possibility of both in-sample and out-of-sample breaks in parameter values and uses information in historical regimes to make inference on out-of-sample breaks. A Bayesian estimation and forecasting procedure is developed which accounts for the uncertainty of both structural breaks and model parameters. I discuss dynamic consistency when forecasting recursively and provide a solution. Applied to monthly US data, I find strong evidence of breaks in the predictive relation between the yield curve and output growth. Incorporating the possibility of structural breaks improves out-of-sample forecasts of output growth. The second essay proposes a sequential Monte Carlo method for estimating GARCH models subject to an unknown number of structural breaks. We use particle filtering techniques that allow for fast and efficient updates of posterior quantities and forecasts in real-time. The method conveniently deals with the path dependence problem that arises in these type of models. The performance of the method is shown to work well using simulated data. Applied to daily NASDAQ returns, we find strong evidence of structural breaks in the long-run variance of returns. Models with flexible return distributions such as t-innovations or with jumps indicate fewer breaks than models with normal return innovations and are favored by the data. The third essay proposes a new tilt stochastic volatility model which extends the existing volatility models by modeling the asymmetric correlation between return and volatility innovations in a unified and flexible framework. The Efficient Importance Sampling (EIS) procedure is adapted to estimate the model. Simulation studies show that the Maximum Likelihood (ML)-EIS estimation of the model is accurate. The new model is applied to the CRSP daily returns. I find the extensions are significant and incorporating them improves the accuracy of volatility estimates.

Understanding the effects of taxes on the financial decision making process sheds light on the process itself and has important ramifications for policymakers. In this thesis, I study these effects in three different contexts: international acquisitions, executive compensation, and dividend payout. In Chapter 1, I investigate the possibility that tax rather than productivity differences are driving international acquisition decisions. A theoretical model of this process yields two testable implications of tax-induced sorting: that, relative to high-tax domestic bidders, low-tax foreign bidders will specialize in both high profitability target firms and those with low levels of tax deductions. I find support for both of these effects in the U.S. acquisition market using cross-sectional variation in target profitability and industry-level variation in deductions from the tax reform of bonus depreciation. Counterfactual simulations show that this reform induced a large drop in foreign acquisitions, leading to a significant loss of aggregate wealth. In Chapter 2, I study a recent increase in the tax rate on stock options for a subset of firms to learn about the effects of taxation on executive compensation. Using novel executive compensation data, I find that this tax increase resulted in an immediate reduction in both option grants and the share of options in total compensation. There appears to have been limited, if any, substitution towards other components of compensation. Hence, the burden of the tax increase must have been substantially borne by the affected executives. In Chapter 3, I use a 2006 tax cut in Canada to study the effects of dividend taxes on corporate payout. Analysis of discrete dividend events suggest little effect from the reform, in stark contrast with recent evidence from the United States. Difference-in-differences estimates using control groups comprised of firms which were exogenously unlikely to be affected by the reform suggest a small positive effect on net dividend initiations. Finally, fixed effect models of regular dividends reveal a small increase around the reform. However, the type of firms responding casts serious doubt on taxes as the cause. Overall, these results are consistent with the small and open nature of the Canadian economy.

This thesis studies the benefits and costs of urban living. Chapter 1 is a theoretical and empirical analysis of the benefits of urban density for consumers, while Chapter 2 proposes a model of how cities enhance the incentives for knowledge diffusion. Chapter 3 investigates the costs of congestion and the determinants of car travel speed across US cities. In Chapter 1, I study the consumption value of urban density by combining Google’s local business data with microgeographic travel data. I show that increased density enables consumers to both realize welfare gains from variety and save time through shorter trips. I estimate the gains from density in the restaurant industry, identifying willingness to pay for access to a slightly preferred location from the extra travel costs incurred to reach it. The results reveal large but very localized gains from density. Increasing the density of destinations generates little reduction in trip times, so most of these gains from density are gains from variety, not savings on travel time. In Chapter 2, I propose a new micro-foundation for knowledge spillovers. I model a city in which uncompensated knowledge transfers to entrepreneurs are bids by experts in auctions for jobs. The model derives from the key ideas about how knowledge differs from other inputs of production, namely that knowledge must be possessed for its value to be assessed, and that knowledge is freely reproducible. Agglomeration economies result from growth in the number of meetings between experts and entrepreneurs, and from heightened competition for jobs among experts. In Chapter 3, written jointly with Gilles Duranton and Matt Turner, we investigate the determinants of driving speed in large US cities. We first estimate city level supply functions for travel in an econometric framework where both the supply and demand for travel are explicit. These estimations allow us to calculate a city level index of driving speed. Our investigation of the determinants of speed provides the foundations for a welfare analysis. This analysis suggests large gains in speed if slow cities can emulate fast cities, and sizable deadweight losses from congestion.

Understanding the effects of taxes on the financial decision making process sheds light on the process itself and has important ramifications for policymakers. In this thesis, I study these effects in three different contexts: international acquisitions, executive compensation, and dividend payout. In Chapter 1, I investigate the possibility that tax rather than productivity differences are driving international acquisition decisions. A theoretical model of this process yields two testable implications of tax-induced sorting: that, relative to high-tax domestic bidders, low-tax foreign bidders will specialize in both high profitability target firms and those with low levels of tax deductions. I find support for both of these effects in the U.S. acquisition market using cross-sectional variation in target profitability and industry-level variation in deductions from the tax reform of bonus depreciation. Counterfactual simulations show that this reform induced a large drop in foreign acquisitions, leading to a significant loss of aggregate wealth. In Chapter 2, I study a recent increase in the tax rate on stock options for a subset of firms to learn about the effects of taxation on executive compensation. Using novel executive compensation data, I find that this tax increase resulted in an immediate reduction in both option grants and the share of options in total compensation. There appears to have been limited, if any, substitution towards other components of compensation. Hence, the burden of the tax increase must have been substantially borne by the affected executives. In Chapter 3, I use a 2006 tax cut in Canada to study the effects of dividend taxes on corporate payout. Analysis of discrete dividend events suggest little effect from the reform, in stark contrast with recent evidence from the United States. Difference-in-differences estimates using control groups comprised of firms which were exogenously unlikely to be affected by the reform suggest a small positive effect on net dividend initiations. Finally, fixed effect models of regular dividends reveal a small increase around the reform. However, the type of firms responding casts serious doubt on taxes as the cause. Overall, these results are consistent with the small and open nature of the Canadian economy.

This thesis studies the benefits and costs of urban living. Chapter 1 is a theoretical and empirical analysis of the benefits of urban density for consumers, while Chapter 2 proposes a model of how cities enhance the incentives for knowledge diffusion. Chapter 3 investigates the costs of congestion and the determinants of car travel speed across US cities. In Chapter 1, I study the consumption value of urban density by combining Google’s local business data with microgeographic travel data. I show that increased density enables consumers to both realize welfare gains from variety and save time through shorter trips. I estimate the gains from density in the restaurant industry, identifying willingness to pay for access to a slightly preferred location from the extra travel costs incurred to reach it. The results reveal large but very localized gains from density. Increasing the density of destinations generates little reduction in trip times, so most of these gains from density are gains from variety, not savings on travel time. In Chapter 2, I propose a new micro-foundation for knowledge spillovers. I model a city in which uncompensated knowledge transfers to entrepreneurs are bids by experts in auctions for jobs. The model derives from the key ideas about how knowledge differs from other inputs of production, namely that knowledge must be possessed for its value to be assessed, and that knowledge is freely reproducible. Agglomeration economies result from growth in the number of meetings between experts and entrepreneurs, and from heightened competition for jobs among experts. In Chapter 3, written jointly with Gilles Duranton and Matt Turner, we investigate the determinants of driving speed in large US cities. We first estimate city level supply functions for travel in an econometric framework where both the supply and demand for travel are explicit. These estimations allow us to calculate a city level index of driving speed. Our investigation of the determinants of speed provides the foundations for a welfare analysis. This analysis suggests large gains in speed if slow cities can emulate fast cities, and sizable deadweight losses from congestion.

This thesis consists of three chapters in Bayesian financial econometrics. The first chapter proposes new dynamic component models of returns and realized covariance (RCOV) matrices based on timevarying Wishart distributions. Bayesian estimation and model comparison is conducted with a range of multivariate GARCH models and existing RCOV models from the literature. The main method of model comparison consists of a term-structure of density forecasts of returns for multiple forecast horizons. The new joint return-RCOV models provide superior density forecasts for returns from forecast horizons of 1 day to 3 months ahead as well as improved point forecasts for realized covariances. Global minimum variance portfolio selection is improved for forecast horizons up to 3 weeks out. The second chapter proposes a full Bayesian nonparametric procedure to investigate the predictive power of exchange rates on commodity prices for 3 commodity-exporting countries: Canada, Australia and New Zealand. I examine the predictive effect of exchange rates on the entire distribution of commodity prices and how this effect changes over time. A time-dependent infinite mixture of normal linear regression model is proposed for the conditional distribution of the commodity price index. The mixing weights of the mixture follow a Probit stick-breaking prior and are hence time-varying. As a result, I allow the conditional distribution of the commodity price index given exchange rates to change over time nonparametrically. The empirical study shows some new results on the predictive power of exchange rates on commodity prices. The third chapter proposes a flexible way of modeling heterogeneous breakdowns in the volatility dynamics of multivariate financial time series within the framework of MGARCH models. During periods of normal market activities, volatility dynamics are modeled by a MGARCH specification. I refer to any significant temporary deviation of the conditional covariance matrix from its implied GARCH dynamics as a covariance breakdown, which is captured through a stochastic component that allows for changes in the whole conditional covariance matrix. Bayesian inference is used and I propose an efficient posterior sampling procedure. Empirical studies show the model can capture complex and erratic temporary structural change in the volatility dynamics.

How resources are allocated across plants is crucial for understanding cross-country output and productivity differences. This thesis contributes to the growing literature on resource misallocation by studying the particular channels through which misallocation can arise. In Chapter 1, I examine extortion at the plant-level, its effects on individual incentives to become an entrepreneur, and how production is affected by the presence of extortion. I show that extortion is especially burdensome on moderate-ability entrepreneurs forcing them to either forgo entry into entrepreneurship or produce at an inefficiently small scale. When property rights are weak, the frequency of extortion is higher producing a society where much of it's entrepreneurial talent is heavily under-utilized. In Chapter 2, I study plant-level distortions and it's effects on incentives to improve productivity. I build a model where plant innovation improves future productivity so that productivity dynamics are endogenous. Distortions that are tied to productivity are introduced to the model to examine how plant innovation is affected. All plants lower innovation resulting in a distribution over productivity that is right-skewed and a distribution over plant size that is left-skewed, consistent with empirical findings in developing countries. The final Chapter is closely related to Chapter 1 but is more empirically focused. I study the role of theft as a means to explaining the abundance of small plants in developing countries and estimate the causal effect of theft on plant capital demand. I find that plant capital would be significantly higher if theft is eliminated.