This thesis investigates the relationship between mode choice and the complexity of trip chaining patterns. An understanding of the causality between these two choice behaviors may aid in the development of tour-based travel demand modeling systems that attempt to incorporate models of trip chaining and mode choice. The relationship between these two aspects of travel behavior is represented in this thesis by considering three different causal structures: one structure in which the trip chaining pattern is determined first and influences mode choice, another structure in which mode choice is determined first and influences the complexity of the trip chaining pattern, and a third structure in which neither is predetermined but both are determined simultaneously. The first two structures are estimated within a recursive bivariate probit modeling framework that accommodates error covariance. The simultaneous logit model is estimated for the third structure that allows a bidirectional simultaneous causality. The analysis and model estimation are performed separately for work tour and non-work tour samples drawn from the 2000 Swiss Microcensus travel survey. Model estimation results show that the causal structure in which trip chaining precedes mode choice performs best for the non-work tour sample. For the work-tour sample, the findings were less conclusive because two causal structures, one in which trip chaining affects mode choice and the other in which both are determined simultaneously, gave virtually identical goodness-of-fit measures. But the structure in which mode choice precedes trip chaining pattern choice gave significantly inferior goodness-of-fit measures for the work tour sample. These findings should be reflected in the development of activity-based and tour-based modeling systems.

In this era of big data, we often rely on techniques ranging from simple linear regression, structural estimation, and state-of-the-art machine learning algorithms to make operational and financial decisions based on data. This calls for a deep understanding of practical and theoretical aspects of methods and models from statistics, econometrics, and computer science, combined with relevant domain knowledge. In this thesis, we study several practical, data-related problems in the particular domains of sharing economy and financial economics/financial engineering, using appropriate approaches from an arsenal of data-analysis tools. On the methodological front, we propose a new estimator for classic demand estimation problem in economics, which is important for pricing and revenue management. In the first part of this thesis, we study customer preference for the bike share system in London, in order to provide policy recommendations on bike share system design and expansion. We estimate a structural demand model on the station network to learn the preference parameters, and use the estimated model to provide insights on the design and expansion of the system. We highlight the importance of network effects in understanding customer demand and evaluating expansion strategies of transportation networks. In the particular example of the London bike share system, we find that allocating resources to some areas of the station network can be 10 times more beneficial than others in terms of system usage, and that currently implemented station density rule is far from optimal. We develop a new method to deal with the endogeneity problem of the choice set in estimating demand for network products. Our method can be applied to other settings, in which the available set of products or services depends on demand. In the second part of this thesis, we study demand estimation methodology when data has a long-tail pattern, that is, when a significant portion of products have zero or very few sales. Long-tail distributions in sales or market share data have long been an issue in empirical studies in areas such as economics, operations, and marketing, and it is increasingly common nowadays with more detailed levels of data available and many more products being offered in places like online retailers and platforms. The classic demand estimation framework cannot deal with zero sales, which yields inconsistent estimates. More importantly, biased demand estimates, if used as an input to subsequent tasks such as pricing, lead to managerial decisions that are far from optimal. We introduce two new two-stage estimators to solve the problem: our solutions apply machine learning algorithms to estimate market shares in the first stage, and in the second stage, we utilize the first-stage results to correct for the selection bias in demand estimates. We find that our approach works better than traditional methods using simulations. In the third part of this thesis, we study how to extract a signal from option pricing models to form a profitable stock trading strategy. Recent work has documented roughness in the time series of stock market volatility and investigated its implications for option pricing. We study a strategy for trading stocks based on measures of their implied and realized roughness. A strategy that goes long the roughest-volatility stocks and short the smoothest-volatility stocks earns statistically significant excess annual returns of 6% or more, depending on the time period and strategy details. Standard factors do not explain the profitability of the strategy. We compare alternative measures of roughness in volatility and find that the profitability of the strategy is greater when we sort stocks based on implied rather than realized roughness. We interpret the profitability of the strategy as compensation for near-term idiosyncratic event risk. Lastly, we apply a heterogeneous treatment effect (HTE) estimator from statistics and machine learning to financial asset pricing. Recent progress in the interdisciplinary area of causal inference and machine learning has proposed various promising estimators for HTE. We take the R-learner algorithm by [73] and adapt it to empirical asset pricing. We study characteristics associated with standard factors, size, value and momentum through the lens of HTE. Our goal is to identify sub-universes of stocks, ``characteristic responders", in which size, value or momentum trading strategies perform best, compared with the performance had they been applied to the entire universe. On the other hand, we identify subsets of ``characteristic traps" in which the strategies perform the worst. In our test period, the differences in average monthly returns between long-short strategies restricted to ``characteristic responders" and ``characteristic traps" range from 0.77% to 1.54% depending on treatment characteristics. The differences are statistically significant and cannot be explained by standard factors: a long-short of long-short strategy generates alpha of significant magnitude from 0.98% to 1.80% monthly, with respect to standard Fama-French plus momentum factors. Simple interaction terms between standard factors and ex-post important features do not explain the alphas either. We also characterize and interpret the characteristic traps and responders identified by our algorithm. Our study can be viewed as a systematic, data-driven way to investigate interaction effects between features and treatment characteristic, and to identify characteristic traps and responders.

Abstract not available

This thesis investigates implications of interdependence between stock market prices in the context of several financial applications including: portfolio selection, tests of market efficiency and measuring the extent of integration among national stock markets. In Chapter 2, I note that volatility spillovers (transmissions of risk) have been found in numerous empirical studies but that no one, to my knowledge, has evaluated their effects in the general portfolio framework. I dynamically forecast two multivariate GARCH models, one that accounts for volatility spillovers and one that does not, and construct optimal mean-variance portfolios using these two alternative models. I show that accounting for volatility spillovers lowers portfolio risk with statistical significance and that risk-averse investors would prefer realised returns from portfolios based on the volatility spillover model. In Chapter 3, I develop a structural MGARCH model that parsimoniously specifies the conditional covariance matrix and provides an identification framework. Using the model to investigate interdependencies between size-sorted portfolios from the Australian Stock Exchange, I gain new insights into the issue of asymmetric dependence. My findings not only confirm the observation that small stocks partially adjust to market-wide news embedded in the returns to large firms but also present evidence that suggests that small firms in Australia fail to even partially adjust (with statistical significance) to large firms??? shocks contemporaneously. All adjustments in small capitalisation stocks occur with a lag. Chapter 4 uses intra-daily data and develops a new method for measuring the extent of stock market integration that takes into account non-instantaneous adjustments to overnight news. This approach establishes the amounts of time that the New York, Tokyo and London stock markets take to fully adjust to overnight news and then uses this This thesis investigates implications of interdependence between stock market prices in the context of several financial applications including: portfolio selection, tests of market efficiency and measuring the extent of integration among national stock markets. In Chapter 2, I note that volatility spillovers (transmissions of risk) have been found in numerous empirical studies but that no one, to my knowledge, has evaluated their effects in the general portfolio framework. I dynamically forecast two multivariate GARCH models, one that accounts for volatility spillovers and one that does not, and construct optimal mean-variance portfolios using these two alternative models. I show that accounting for volatility spillovers lowers portfolio risk with statistical significance and that risk-averse investors would prefer realised returns from portfolios based on the volatility spillover model. In Chapter 3, I develop a structural MGARCH model that parsimoniously specifies the conditional covariance matrix and provides an identification framework. Using the model to investigate interdependencies between size-sorted portfolios from the Australian Stock Exchange, I gain new insights into the issue of asymmetric dependence. My findings not only confirm the observation that small stocks partially adjust to market-wide news embedded in the returns to large firms but also present evidence that suggests that small firms in Australia fail to even partially adjust (with statistical significance) to large firms??? shocks contemporaneously. All adjustments in small capitalisation stocks occur with a lag. Chapter 4 uses intra-daily data and develops a new method for measuring the extent of stock market integration that takes into account non-instantaneous adjustments to overnight news. This approach establishes the amounts of time that the New York, Tokyo and London stock markets take to fully adjust to overnight news and then uses this

The factors affecting the supply of rubber from Sarawak, East Malaysia, were identified and reviewed in an historical framework. A methodical framework for the general analysis of economic relationships between variables was reviewed and a practical application of the methodology to the supply of rubber from Sarawak was carried out. An econometric analysis of the long run factors affecting the production of rubber was carried out. (1) Two log-differenced autoregressive models of the rubber supply were formulated. (2) The models were tested for parameter constancy to identify structural breaks in the time series and for structural invariance to determine whether they were suitable for policy analysis, forecasting and backcasting. (3) The variables were tested for bivariate Granger Causality to determine the relationships between the factors of production and the output of rubber. (4) Forecast Error Variance Decomposition analysis of multivariate Granger Causality was carried out using a Vector Autoregressive Model. The results confirm the a priori economic theory that long run changes in supply have been affected primarily by changes in area under rubber production and long term price trends. The area planted to rubber has depended upon price incentives and the availability of scarce labour resources. Prices have been affected by the supply of rubber from Sarawak but this is posited to be a reflection of global supply trends affecting prices. While the results generally confirm the economic theory, caution is urged when interpreting the results. The severe inadequacies of the data used highlights the need for more accurate time series and the mainly methodological approach of this study.

This thesis presents two models to determine technological change and cost inefficiency in the regulated U.S. Motor Carrier Industry following regulatory reform. Data from the LTL sector of the industry from 1976 to 1987 are used in this study. Results provide insights about the observed increase in industry concentration and the effects of regulatory reform. In chapter II, a translog cost function model is used to examine the impact of deregulation and technological bias. We show that technological change has been labor saving and purchased capital using, and that these input biases were induced by changes in output level. The increase of capital cost share and the decrease of fuel cost share are attributed to deregulation. Overtime, the LTL sector of the motor carrier industry has become more capital intensive resulting in even higher entry barriers. Deregulation has had a negative impact on technological change and led to higher industry costs. In chapter III, a stochastic cost frontier model is used to examine cost inefficiency. Results suggest that cost inefficiency accounts for 12.61% of the industry's total cost and the average level of inefficiency has not significantly changed over time. The mean estimates of firm-specific inefficiencies range between 5.5% and 29.6% for the period 1976-1987. Based on the estimated firm-specific inefficiencies, Tobit regression models are constructed to examine variations of inefficiency among firms in different ICC regions and to identify factors contributing to overall inefficiency. The main factors contributing to inefficiency are output, percent of LTL shipments, and input ratios; in particular, large firms appears to operate more efficiently than small firms. We also show that, although large firms have a slower rate of technological advancement than small firms, economies of scale exist and are increasing over time. Therefore, the rise in industry concentration could be justified from the standpoint of scale economies and efficiency gain. Finally, deregulation has had no impact on the overall level of inefficiency. / Graduation date: 1999

This study estimates the welfare effects of cigarette advertising using the framework posited by Becker and Murphy (1993). This model exposes previously unaccounted benefits of cigarette advertising and allows for conventional social welfare estimation by assimilating the theory of advertising into the general theory of complements. The policy implications of the Becker and Murphy framework will rely on the impact of advertising on equilibrium output price. A modification of the new empirical industrial organization technique allows estimation of a supply relation containing advertising in an imperfectly competitive environment. Allowing for different price effects of cigarette advertising before and after the Broadcast Advertising Ban leads to the conclusion that advertising after the ban has a larger price effect than before. This suggests that cigarette advertising is better able to enhance market power after the Broadcast Advertising Ban. Parameter estimates indicate that a one percent increase in cigarette advertising above its 1994 level will precipitate a conservative estimate of a reduction in social welfare of $14.3 million (in 1982 dollars). Thus, even if one ignores externalities altogether, cigarette advertising is clearly excessive from society's point of view. / Graduation date: 1997

The thesis presents a useful and effective blend of insights about macroeconomic business fluctuations and the effects of government expenditure in economic growth in Korea. In Chapter I, I show that the joint behavior of key Korean macroeconomic variables is consistent with traditional Keynesian interpretation of macroeconomic business fluctuations by using standard VAR analysis and structural VAR analysis. Both analyses consistently confirmed that aggregate demand shocks move output and prices in the same direction, whereas aggregate supply shocks move output and prices in the opposite direction in the short run, and that aggregate demand shocks are reflected mostly in prices in the long run, while aggregate supply shocks are likely to have long run effects on output. In Chapter II, I analyze the long run effects of different types of government spending on economic growth in Korean economy by using Transfer Function Analysis and Impulse Response Analysis. Both analyses indicated that the most efficient way to enhance the economic growth in Korea is by increasing expenditure on health, education, electricity, gas and water without ignoring expenditures on roads, social security and welfare, transportation and communication. / Graduation date: 1997

Economic systems are often subject to structural variability. For the achievement of correct structural specification in econometric modelling it is then important to allow for parameters that are time-varying, and to apply estimation techniques suitably designed for inference in such models. One realistic model assumption for such parameter variability is the Markovian model, and Kaiman filtering is then assumed to be a convenient estimator. In the thesis several aspects of using Kaiman filtering approaches to estimation in that framework are considered. The application of the Kaiman filter to estimation in econometric models is straightforward if a set of basic assumptions are satisfied, and if necessary initial specifications can be accurately made. Typically, however, these requirements can generally not be perfectly met. It is therefore of great importance to know the consequences of deviations from the basic assumptions and correct initial specifications for inference, in particular for the small sample situations typical in econometrics. If the consequences are severe it is essential to develop techniques to cope with such aspects.For estimation in interdependent systems a two stage Kaiman filter is proposed and evaluated, theoretically, as well as by a small sample Monte Carlo study, and empirically. The estimator is approximative, but with promising small sample properties. Only if the transition matrix of the parameter model and an initial parameter vector are misspecified, the performance deteriorates. Furthermore, the approach provides useful information about structural properties, and forms a basis for good short term forecasting.In a reduced form fraaework most of the basic assumptions of the traditional Kaiman filter are relaxed, and the implications are studied. The case of stochastic regressors is, under reasonable additional assumptions, shown to result in an estimator structurally similar to that due to the basic assumptions. The robustness properties are such that in particular the transition matrix and the initial parameter vector should be carefully estimated. An estimator for the joint estimation of the transition matrix, the parameter vector and the model residual variance is suggested and utilized to study the consequences of a misspecified parameter model. By estimating th transitions the parameter estimates are seen to be robust in this respect. / <p>Härtill 4 delar</p> / digitalisering@umu

Population retention and growth is a concern for cities, towns, and rural municipalities across Canada, and population change is one of the best available indicators of economic prosperity and community success. As such, it is important to understand the factors driving the location decisions of Canadians, and to use this information to help communities develop strategies to ensure their longevity and to comprehend the various features influencing future prosperity. The results of this study clearly show that local community characteristics do indeed influence local population growth. Important factors include economic indicators, the presence of different types of amenities, and the proximity of the community to urban areas. <p> Previous research has been completed on the topic of community population change and amenities in other countries, but Canada has not been examined until now. This study utilizes census data at the municipality level to examine these issues. The analysis consists of an econometric model with population change as the dependent variable, and a number of local factors as the explanatory variables. In general, the results of this study complied with theoretical predictions. Communities with favourable amenities and economic factors were found to have higher population growth. Also, different age groups were found to value different bundles of amenities and economic opportunities. <p> Amenities were found to be important factors affecting population growth. Communities with higher average housing prices and lower average incomes had higher population growth. Although this is seemingly a contradictory result, it implies that amenities have been capitalized into incomes and housing prices over time and thus more than income appears to be determining the pattern of housing values across Canada; an outcome predicted by the theoretical framework of the study. Medical amenities were found to be more important for older segments of the population, though all ages valued being near large acute care hospitals. Communities with high rates of violent crime tended to have lower population growth rates. Natural amenities such as mountains and pleasant weather, and the presence of water did not consistently result in higher community population growth. <p> Economic factors such as industry diversification, high local employment rates, and growing employment prospects were very important in influencing population growth, especially among younger segments of the population. However, economic and financial opportunities do not appear to affect migration decisions of the elderly, who are influenced more by medical amenities. Except for youth, local employment opportunities were not as important as having opportunities in surrounding communities. The presence of agriculture and resource extraction sectors tended to result in lower population growth. Finally, proximity to larger urban centres and population size appeared to be beneficial for communities. <p> Overall, the results of this study provide insight for community leaders, policy makers, and others interested in the dynamics of community population change, and will help governments efficiently allocate resources to communities and form strategies to deal with declining community populations.