This dissertation includes three essays on unit root tests and cointegration. The first essay uses econometric tools to explore the possible causes of global warming. By examining the time series properties of the global mean temperature, solar irradiance and greenhouse gases, it finds static long-run steady-state and dynamic long-run steady-state relations between temperature and radiative forcing of solar irradiance and a set of three greenhouse gas series. Estimates of the adjustment coefficients indicate that temperature series is error correcting around 5% to 65% of the disequilibria each year, depending on the type of the long-run relation. The estimates of the I(1) and I(2) trends indicate that they are driven by linear combinations of three greenhouse gases and their loadings indicate strong impact on the temperature series. The equilibrium temperature change for a doubling of carbon dioxide is between 2.15 and 3.40 Celsius degree, which is in agreement with past literature and the report of the Intergovernmental Panel on Climate Change using fifteen different general circulation models. The second essay examines the effect of random initial condition on the power of unit root tests. It develops the test statistics in the context of unknown structural change models using generalized least square detrended data when the initial observation is drawn from its unconditional distribution. It derives the limiting distributions of M-tests, ADF test and a family of feasible point optimal tests. Using two methods to estimate the break point, this study calculates the power envelopes and asymptotic power functions, and compares them with the case where the initial condition is fixed. Finite sample Monte Carlo simulations under various forms of error processes are performed using different lag length selection methods. Empirical applications are also provided. The third essay employs a multivariate framework to improve the power of unit root tests. In the context of unknown structural change models, it shows that when testing for a unit root against stationarity with an unknown structural break, substantial power gains can be achieved by incorporating extra information contained in an arbitrary number of covariates. The power gains are dependent on the long-run correlations between the shocks of covariates and quasi-differences of potentially integrated time series. The higher correlation between covariates and quasi-differences of time series, the higher power gains can be realized. A feasible statistic to estimate the unknown correlation is also derived. In finite sample experiments, Monte Carlo simulation results confirm large power improvements without size deteriorations.

The thesis is motivated on the background of rapid biodiversity depletion around the world. Using a general equilibrium model, the first essay shows how free trade can have negative impacts on the biodiversity stock of the South due to missing markets. It also shows that free trade added with Southern rapid population growth and agricultural extensions are the main reasons of biodiversity loss. Demand side mechanisms like discounting of biodiversity depleting products and supply side mechanism like ecofriendly agricultural technologies can have a positive impact to decrease biodiversity loss. Essay two shows how absence of a market for ecosystem services causes depletion of biodiversity in the developing countries. In any period, the amount of land development in the south depends on how large the relative size of the southern younger generation in that period is, compared to the size of the maximum available land resources. Lack of any other assets in the South for old age savings, low land clearing cost and low discount rate causes biodiversity loss. There is scope of conserving biodiversity and the flow of ecosystem services by fiscal tools like land development fee or land tax and market creation through payment for ecosystem services. But these policies may deteriorate Southern terms of trade. Through a two period general equilibrium model with an open economy, paper three shows how bio-prospecting can help in the conservation of biodiversity and under what conditions it can fail. The paper shows that a systematic search process depending on prior information on the plant species and the health characteristics increases the success of bio-prospecting. Also a lower cost of the bio-prospecting process or manufacturing of drugs, a high probability of drug discovery, and a drug with a high benefit result in a higher royalty payment to the South and conservation of biodiversity. Strategic pricing by the bio-prospecting firms or marketing of only high benefit drugs ensure successful bio-prospecting schemes. A larger set of bio-prospected drugs may drive away the low-benefit drug firms out of market. The caveat is that bio-prospecting process may ignore Southern low-value diseases. The three essays of this thesis suggest that green consumerism, payment for ecosystem services or bio-prospecting scheme can be viable tools for biodiversity conservation. But the strong message in all the three essays is also that there is no one miracle tool that can save biodiversity. So a mix of market and non-market tools has to be used for the conservation of world's biodiversity.

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<p> My dissertation consists of three essays which contribute new theoretical results to large panel data models with cross-sectional dependence. These essays try to answer or partially answer some prominent questions such as how to detect the presence of cross-sectional dependence and how to capture the latent structure of cross-sectional dependence and estimate parameters efficiently by removing its effects.</p><p> Chapter 2 introduces a nonparametric test for cross-sectional contemporaneous dependence in large dimensional panel data models based on the squared distance between the pair-wise joint density and the product of the marginals. The test can be applied to either raw observable data or residuals from local polynomial time series regressions for each individual to estimate the joint and marginal probability density functions of the error terms. In either case, we establish the asymptotic normality of our test statistic under the null hypothesis by permitting both the cross section dimension <i> n</i> and the time series dimension <i>T</i> to pass to infinity simultaneously and relying upon the Hoeffding decomposition of a two-fold <i> U</i>-statistic. We also establish the consistency of our test. A small set of Monte Carlo simulations is conducted to evaluate the finite sample performance of our test and compare it with that of Pesaran (2004) and Chen, Gao, and Li (2009).</p><p> Chapter 3 analyzes nonparametric dynamic panel data models with interactive fixed effects, where the predetermined regressors enter the models nonparametrically and the common factors enter the models linearly but with individual specific factor loadings. We consider the issues of estimation and specification testing when both the cross-sectional dimension <i>N</i> and the time dimension <i> T</i> are large. We propose sieve estimation for the nonparametric function by extending Bai's (2009) principal component analysis (PCA) to our nonparametric framework. Following Moon and Weidner's (2010, 2012) asymptotic expansion of the Gaussian quasilog-likelihood function, we derive the convergence rate for the sieve estimator and establish its asymptotic normality. The sources of asymptotic biases are discussed and a consistent bias-corrected estimator is provided. We also propose a consistent specification test for the linearity of the nonparametric functional form by comparing the linear and sieve estimators. We establish the asymptotic distributions of the test statistic under both the null hypothesis and a sequence of Pitman local alternatives.</p><p> To improve the finite sample performance of the test, we also propose a bootstrap procedure to obtain the bootstrap <i>p</i>-values and justify its validity. Monte Carlo simulations are conducted to investigate the finite sample performance of our estimator and test. We apply our model to an economic growth data set to study the relationship between capital accumulation and real GDP growth rate.</p><p> Chapter 4 proposes a nonparametric test for common trends in semiparametric panel data models with fixed effects based on a measure of nonparametric goodness-of-fit (<i>R</i>²). We first estimate the model under the null hypothesis of common trends by the method of profile least squares, and obtain the augmented residual which consistently estimates the sum of the fixed effect and the disturbance under the null.</p><p> Then we run a local linear regression of the augmented residuals on a time trend and calculate the nonparametric <i>R</i>² for each cross section unit. The proposed test statistic is obtained by averaging all cross sectional nonparametric <i>R</i>²'s, which is close to 0 under the null and deviates from 0 under the alternative. We show that after appropriate standardization the test statistic is asymptotically normally distributed under both the null hypothesis and a sequence of Pitman local alternatives. We prove test consistency and propose a bootstrap procedure to obtain <i>p</i>-values. Monte Carlo simulations indicate that the test performs well infinite samples. Empirical applications are conducted exploring the commonality of spatial trends in UK climate change data and idiosyncratic trends in OECD real GDP growth data. Both applications reveal the fragility of the widely adopted common trends assumption.</p>

<p> In every year since 1982, the popular magazine <i>Forbes Magazine </i> has published a list of the 400 wealthiest Americans. That list has attracted attention from the press and public, but it has been largely ignored by economists, at least in their professional capacities. Although a list published by a popular magazine may seem like a dubious source of data, the magazine's list is arguably the best source of data on the very top of the wealth distribution in the United States. This dissertation is a series of essays that use the magazine's list to study the wealthiest Americans. The essays study inequality between the wealthiest Americans and everyone else, inequality among the wealthiest Americans themselves, and mobility among the wealthiest Americans over time. Taken together, the essays offer insight into some basic empirical facts about a much noticed but little studied group. </p>

Over the past ten years, the Big Three (General Motors, Ford, and Chrysler) have taken drastic steps to improve quality and reduce costs. Early evidence indicates that these steps have been successful in substantially narrowing the quality/cost gap between the Big Three and the Japanese. Despite these improvements, however, the public appears reluctant to relinquish past association with poor quality and fully incorporate these improvements in their expectations about the quality of domestic automobiles. / This dissertation examines the impact of reputation and changing beliefs on price, quantity traded, and total economic surplus in the market for used cars. Based on stylized facts about the U.S. auto industry, prior expectations are assumed to lie below the true probability that quality is high and are therefore interpreted as a vehicle's "reputation". Agents are assumed to combine these prior beliefs with recent news announcements according to Bayes rule. / Initially, an optimal stopping model is developed where supply is exogenous and agents decide each period between buying and waiting for the arrival of more information. In this representative agent framework, it is shown that a poor reputation postpones purchase decisions. Extending this model by including multiple agents with heterogeneous beliefs and allowing supply to be endogenous, a poor reputation is shown to lower price and retard its convergence to the full-information equilibrium. Moreover, the number of trades and the value of total economic surplus are also reduced. / Hypotheses about the existence of reputational effects are tested using used car price and quality data for the 1985-1990 model years. Variables based on past and current quality indicators proxy actual beliefs extant in the market and are interpreted as the prior and "news" arrival respectively. The empirical results support the existence of reputational effects in the U.S. market for used automobiles. Reputation is strongest for the earlier model years with contemporaneous news variables becoming significant in later years. This suggests that reputational effects arising from poor quality production in the early 1980's may be waning. / Source: Dissertation Abstracts International, Volume: 55-09, Section: A, page: 2940. / Major Professor: Gary M. Fournier. / Thesis (Ph.D.)--The Florida State University, 1994.