Diss. Uppsala : Uppsala universitet, 2007.

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.

<div> <div> <div> <p>First Chapter: The ordinary least squares (OLS) estimator for spatial autoregressions may be consistent as pointed out by Lee (2002), provided that each spatial unit is influenced aggregately by a significant portion of the total units. This paper presents a unified asymptotic distribution result of the properly recentered OLS estimator and proposes a new estimator that is based on the indirect inference (II) procedure. The resulting estimator can always be used regardless of the degree of aggregate influence on each spatial unit from other units and is consistent and asymptotically normal. The new estimator does not rely on distributional assumptions and is robust to unknown heteroscedasticity. Its good finite-sample performance, in comparison with existing estimators that are also robust to heteroscedasticity, is demonstrated by a Monte Carlo study.<br></p><p><br></p><p>Second Chapter: This paper proposes a new estimation procedure for the first-order spatial autoregressive (SAR) model, where the disturbance term also follows a first-order autoregression and its innovations may be heteroscedastic. The estimation procedure is based on the principle of indirect inference that matches the ordinary least squares estimator of the two SAR coefficients (one in the outcome equation and the other in the disturbance equation) with its approximate analytical expectation. The resulting estimator is shown to be consistent, asymptotically normal and robust to unknown heteroscedasticity. Monte Carlo experiments are provided to show its finite-sample performance in comparison with existing estimators that are based on the generalized method of moments. The new estimation procedure is applied to empirical studies on teenage pregnancy rates and Airbnb accommodation prices.<br></p><p><br></p><p>Third Chapter: This paper presents a sample selection model with spatial autoregressive interactions and studies the maximum likelihood (ML) approach to estimating this model. Consistency and asymptotic normality of the ML estimator are established by the spatial near-epoch dependent (NED) properties of the selection and outcome variables. Monte Carlo simulations, based on the characteristics of female labor supply example, show that the proposed estimator has good finite-sample performance. The new model is applied to empirical study on examining the impact of climate change on agriculture in Southeast Asia.<br></p></div></div></div><div><div><div> </div> </div> </div>

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.<div><br></div><div>In my second chapter, I analyze possible reasons why airline prices are higher inthe smaller markets compared to larger markets. In the literature, most of the studies ignore the fact that the smaller markets are different compared to larger markets in terms of the nature of competition. I find that a combination of lower competition, and lack of entry from low cost carriers (LCCs) are the reasons behind higher prices in the smaller city-pair markets. I show that price is substantially higher in a market with a fewer number of firms controlling for several other factors. My paper estimates the modified critical number of firms to be 5 and the critical value of the HHI to be .6.<br><div><br></div><div>In my third chapter, I study the effect of announcement of investment in research & development (R&D) on the value of a firm in the pharmaceutical industry. Three types of R&D by the pharmaceutical firms are considered for the analysis: acquisition of other smaller firms, internal investment in R&D, and collaborative investment in R&D. This chapter finds that few target specific characteristics and financial charac-teristics of the acquiring firm are important drivers of the abnormal returns around the announcement period.<br></div><div><br></div></div>

<p>Decades and policies come and go, and the ominous problem of corruption remains almost unaltered. Some of the most sought-after policies for corruption deterrence focus on institutional reforms aimed at assuring the right and effective access to information, reinforcing rule of law, tackling impunity, and increasing integrity standards for public servants. The aim of this dissertation is to test whether the impact of these policies over corruption is traceable at the subnational level of mexico and the united states. Seeking to accomplish this purpose, statistics measuring corruption, transparency and relevant variables are analyzed through ols regression and correlation methods. The findings point that spite of the evident benefits of transparency for democratic governance, under the methodology selected and with the ensuing subnational statistics, it is not possible to affirm that corruption is noticeable affected by transparency or integrity variables. Implications of these findings ask for a revision on the manner corruption is measured, and to devise which sort of circumstances bolster or thwart transparency´s prowess to cause a dent over corruption.</p> <p> </p>

<p>Fundamental changes have been happening in healthcare organizations and delivery in these decades, including more accessible physician information, the low-cost collection and sharing of clinical records, and decision support systems, among others. Emerging information systems and technologies play a signification role in these transformations. To extend the understanding and the implications of information systems on healthcare, my dissertation investigates the influence of information systems on enhancing healthcare operations. The findings reveal the practical value of digitalization in indicating healthcare providers' cognitive behaviors, responding to healthcare crises, and improving medical performance.</p> <p><br></p> <p>The first essay investigates the unrevealed value of a special type of user-generated content in healthcare operations. In today's social media world, individuals are willing to express themselves on various online platforms. This user-generated content posted online help readers get easy assess to individuals' features, including but not limited to personality traits. To study the impact of physicians' personality traits on medicine behaviours and performance, we take a view from the perspective of user generated content posted by their supplier side as well as using physician statements which have been made available in medical review websites. It has been found that a higher openness score leads to lower mortality rates, reduced lab test costs, shorter time usage in hospitals treated by physicians with greater openness scores. Furthermore, taking these personality traits into consideration in an optimization problem of ED scheduling, the estimation of counterfactual analysis shows an average of 11.4%, 18.4%, and 17.8% reduction in in-hospital mortality rates, lab test expenditures, and lengths of stay, respectively. In future operation of healthcare, physicians' personalities should be taken into account when healthcare resources are insufficient in times of healthcare pandemics like COVID-19, as our study indicates that health service providers personality is an actual influence on clinical quality.</p> <p><br></p> <p>In the second essay, we focus on the influences of the most severe healthcare pandemic in these decades, COVID-19, on digital goods consumption and examine whether digital goods consumption is resilient to an individual’s physical restriction induced by the pandemic. Leveraging the enforced quarantine policy during the COVID-19 pandemic as a quasi-experiment, we identify the influence of a specific factor, quarantine policy, on mobile app consumption in every Apple app store category in the short and long terms. In the perspective of better responding in the post-pandemic era, the quantitative findings provide managerial implications to the app industry as well as the stock market for accurately understanding the long-term impact of a significant intervention, quarantine, in the pandemic. Moreover, by using the conditional exogenous quarantine policy to instrument app users’ daily movement patterns, we are able to further investigate the digital resilience of physical mobility in different app categories and quantify the impact of an individual’s physical mobility on human behavior in app usage. For results, we find that the reduction in 10% of one’s physical mobility (measured in the radius of gyration) leads to a 2.68% increase in general app usage and a 5.44% rise in app usage time dispersion, suggesting practitioners should consider users’ physical mobility in future mobile app design, pricing, and marketing.</p> <p><br></p> <p>In the third essay, we investigate the role of an emerging AI-based clinical treatment method, robot-assisted surgery (RAS), in transforming the healthcare delivery. As an advanced technique to help diminish the human physical and intellectual limitations in surgeries, RAS is expected to but has not been empirically proven to improve clinical performance. In this work, we first investigate the effect of RAS on clinical outcomes, controlling physicians' self-selection behavior in choosing whether or not to use RAS treatment methods. In particular, we focus on the accessibility of RAS and explore how physician and patient heterogeneity affect the adoption of the RAS method, including learning RAS and using RAS. Investigating the decision-making process on RAS implementation in both the learning and using stages, we show the synergy of RAS implementation in alleviating healthcare racial disparity. Ultimately, the mechanism analysis will be conducted to reveal the underlying mechanism that induces the enhancement of surgical outcomes. For instance, the estimations tend to reveal that, more than surging clinical performance, RAS tends to increase standardization in time and steps when applying the treatment procedures. </p>

<p>This dissertation is a collection of four essays on nonstationary time series econometrics, which are grouped into four chapters. The first chapter investigates the inference in mildly explosive autoregressions under unconditional heteroskedasticity. The second chapter develops a new approach to forecasting a highly persistent time series that employs feasible generalized least squares (FGLS) estimation of the deterministic components in conjunction with Mallows model averaging. The third chapter proposes new bootstrap procedures for detecting multiple persistence shifts in a time series driven by nonstationary volatility. The last chapter studies the problem of testing partial parameter stability in cointegrated regression models.</p>

<div>State and local adoption/repeal of highly polarized policies causes migration responses both out of and into the affected region. Interpreting the responses as revealed policy pref?erences leads to the conclusion that marijuana legalization and abortion waiting periods had been favored nationally, while gay marriage had been opposed. Policy preferences are geographically heterogeneous, which leads to different responses across counties. From 1992- 2017, these policy changes reduced domestic migration by two percent, which is approxi?mately 20% of the total migration decline. The migration changes, via partisan sorting, accounted for a significant share of the increased political polarization from 2012-2016 in western, urban, and swing counties. <br></div><div><br></div><div>In cases where unmarried parents have joint physical custody of their child(ren), there is a wide range of default relocation restrictions that depend on their state of origin. Using IRS county-to-county migration data, demographic data from the ACS, and state relocation restrictions gathered from divorce law websites, I study the impact of these default reloca?tion restrictions on domestic US migration. Results from both regression discontinuity and selection on observables designs, find about 10% - 30% less migration to counties that are outside the allowed relocation range. This migration friction is shown to strengthen from 1992 - 2012, as both joint physical custody and unmarried parents became more common, thereby contributing to the decline in domestic US migration. <br></div><div><br></div><div>In the United States, between 2004 and 2008, 28 states increased their minimum wage; the national minimum wage was increased in 2007. The average migration response to these increases was a 3% change in migration away from a one dollar increase. These effects are not distributed evenly across the population. People from more impacted demographic groups are more likely to move away from minimum wage increases.</div>

<p dir="ltr">In this thesis, we delve into the exploration of several nonparametric and semiparametric econometric models within the Bayesian framework, highlighting their applicability across a broad spectrum of microeconomic and macroeconomic issues. Positioned in the big data era, where data collection and storage expand at an unprecedented rate, the complexity of economic questions we aim to address is similarly escalating. This dual challenge ne- cessitates leveraging increasingly large datasets, thereby underscoring the critical need for designing flexible Bayesian priors and developing scalable, efficient algorithms tailored for high-dimensional datasets.</p><p dir="ltr">The initial two chapters, Chapter 2 and 3, are dedicated to crafting Bayesian priors suited for environments laden with a vast array of variables. These priors, alongside their corresponding algorithms, are optimized for computational efficiency, scalability to extensive datasets, and, ideally, distributability. We aim for these priors to accommodate varying levels of dataset sparsity. Chapter 2 assesses nonparametric additive models, employing a smoothing prior alongside a band matrix for each additive component. Utilizing the Bayesian backfitting algorithm significantly alleviates the computational load. In Chapter 3, we address multiple linear regression settings by adopting a flexible scale mixture of normal priors for coefficient parameters, thus allowing data-driven determination of the necessary amount of shrinkage. The use of a conjugate prior enables a closed-form solution for the posterior, markedly enhancing computational speed.</p><p dir="ltr">The subsequent chapters, Chapter 4 and 5, pivot towards time series dataset model- ing and Bayesian algorithms. A semiparametric modeling approach dissects the stochastic volatility in macro time series into persistent and transitory components, the latter addi- tional component addressing outliers. Utilizing a Dirichlet process mixture prior for the transitory part and a collapsed Gibbs sampling algorithm, we devise a method capable of efficiently processing over 10,000 observations and 200 variables. Chapter 4 introduces a simple univariate model, while Chapter 5 presents comprehensive Bayesian VARs. Our al- gorithms, more efficient and effective in managing outliers than existing ones, are adept at handling extensive macro datasets with hundreds of variables.</p>

<p dir="ltr">Retail prices for eggs surged during the period from early 2022 to mid-2023 in the U.S. Eggs are important to a wide range of people because of their nutritional benefits and cost relative to other protein sources. Thus, rapidly increasing egg prices can cause risks to numerous people. Using social media listening data, we analyzed the relationship between egg prices and online and social media attention and the relationship between egg prices and online and social media sentiment. Our findings suggest that egg prices are associated with the sentiment of the public as expressed in online media. However, the relationship between egg prices and online and social media attention is complex when studying the timing of increased concern with the timing of online news media coverage. Importantly, by leveraging a method of regression discontinuity in time, we show that online and social media conversations about eggs and egg prices tend to increase after the rapid rise in online news coverage. Similarly, online and social media conversations about eggs and egg prices tend to decrease after the rapid rise in online news coverage. This research also provided an example of how a total number of statements and sentiment score of social media listening data can be utilized to capture people’s attention levels, overall sentiment, and how they change over time.</p>