Recent studies suggest that the conditional CAPM might hold, period-by-period, and that time-varying betas can explain the failures of the simple, unconditional CAPM. We argue, however, that significant departures from the unconditional CAPM would require implausibly large time-variation in betas and expected returns. Thus, the conditional CAPM is unlikely to explain asset-pricing anomalies like book-to-market and momentum. We test this conjecture empirically by directly estimating conditional alphas and betas from short-window regressions (avoiding the need to specify conditioning information). The tests show, consistent with our analytical results, that the conditional CAPM performs nearly as poorly as the unconditional CAP

Using mainly quantile regressions, this paper evaluates the gender wage gap throughout the conditional wage distribution in Sweden. The gender wage is found to increase at the upper tail of the wage distribution, indicating an enforcement of the glass ceiling effect recorded in earlier studies. The results also indicate that the earlier noted trend of diminishing wage differences at the bottom of the wage distribution now is turning. The increase of overall wage inequalities coincides with a general increase in wage dispersion among high-income and low-income individuals. It is also noted that there are substantial differences in returns to productivity characteristics between the public and the private sectors, and that both the highest and the lowest unexplained gender wage gap is found in the public sector.

This is an introduction to Wiener measure and the Feynman-Kac formula on general Riemannian manifolds for Riemannian geometers with little or no background in stochastics. We explain the construction of Wiener measure based on the heat kernel in full detail and we prove the Feynman-Kac formula for Schrödinger operators with bounded potentials. We also consider normal Riemannian coverings and show that projecting and lifting of paths are inverse operations which respect the Wiener measure.

This paper investigates the impact of Familias en Acción, a conditional cash transfer program in Colombia, on participant credit behavior. The motivation of the research is derived from previous studies which indicate that conditional cash transfer programs have effects on households aside from those directly intentioned by the programs. While the direct impacts of Familias en Acción have been measured by the research team responsible for evaluating the program, potential indirect effects remain uninvestigated. My research specifically focuses on the impacts of the program on credit behavior. I compute estimates on the percent change in loan balance outstanding and credit participation over the four-year evaluation period by comparing households that are benefiting from the program (treatment) and those that are not (control). Because Familias en Acción was not a randomly assigned program, I use quasi-experimental data collected in three rounds over four years. I control for dissimilarities between the treatment and control group by utilizing a difference-in-differences approach and by controlling across a wide-range of observable household characteristics. I find that the program does affect credit behavior in treated households. In both urban and rural areas, the outstanding loan balance and the number of households involved in the credit market increases after the first year of the program. After four years of the program, the effect is still significant and positive in rural areas though not in urban areas. This indicates that the program affects credit behavior in all treated households in the short run and rural households in the long run. These findings provide new considerations for policy makers who are implementing these programs in developing countries.

Autoignition of non-premixed methane-air mixtures is investigated using first-order Conditional Moment closure (CMC). In CMC, scalar quantities are conditionally averaged with respect to a conserved scalar, usually the mixture fraction. The conditional fluctuations are often of small order, allowing the chemical source term to be modeled as a function of the conditional species concentrations and the conditional enthalpy (temperature). The first-order CMC derivation leaves many terms unclosed such as the conditional scalar dissipation rate, velocity and turbulent fluxes, and the probability density function. Submodels for these quantities are discussed and validated against Direct Numerical Simulations (DNS). The CMC and the turbulent velocity and mixing fields calculations are decoupled based on the frozen mixing assumption, and the CMC equations are cross-stream averaged across the flow following the shear flow approximation. Finite differences are used to discretize the equations, and a two-step fractional method is implemented to treat separately the stiff chemical source term. The stiff ODE solver LSODE is used to solve the resulting system of equations. The recently developed detailed chemical kinetics mechanism UBC-Mech 1.0 is employed throughout this study, and preexisting mechanisms are visited. Several ignition criteria are also investigated. Homogeneous and inhomogeneous CMC calculations are performed in order to investigate the role of physical transport in autoignition. Furthermore, the results of the perfectly homogeneous reactor calculations are presented and the critical value of the scalar dissipation rate for ignition is determined. The results are compared to the shock tube experimental data of Sullivan et al. The current results show good agreement with the experiments in terms of both ignition delay and ignition kernel location, and the trends obtained in the experiments are successfully reproduced. The results were shown to be sensitive to the scalar dissipation model, the chemical kinetics, and the ignition criterion.

This thesis investigates robust techniques for mean-variance (MV) portfolio optimization problems under the estimation risk in mean return. We evaluate the performance of the optimal portfolios generated by the min-max robust MV portfolio optimization model. With an ellipsoidal uncertainty set based on the statistics of the sample mean estimates, minmax robust portfolios equal to the ones from the standard MV model based on the nominal mean estimates but with larger risk aversion parameters. With an interval uncertainty set for mean return, min-max robust portfolios can vary significantly with the initial data used to generate the uncertainty set. In addition, by focusing on the worst-case scenario in the mean return uncertainty set, min-max robust portfolios can be too conservative and unable to achieve a high return. Adjusting the conservatism level of min-max robust portfolios can only be achieved by excluding poor mean return scenarios from the uncertainty set, which runs counter to the principle of min-max robustness. We propose a CVaR robust MV portfolio optimization model in which the estimation risk is measured by the Conditional Value-at-Risk (CVaR). We show that, using CVaR to quantify the estimation risk in mean return, the conservatism level of CVaR robust portfolios can be more naturally adjusted by gradually including better mean return scenarios. Moreover, we compare min-max robust portfolios (with an interval uncertainty set for mean return) and CVaR robust portfolios in terms of actual frontier variation, portfolio efficiency, and portfolio diversification. Finally, a computational method based on a smoothing technique is implemented to solve the optimization problem in the CVaR robust model. We numerically show that, compared with the quadratic programming (QP) approach, the smoothing approach is more computationally efficient for computing CVaR robust portfolios.

One of the oldest and most fundamental problems in statistics is the analysis of cross-classified data called contingency tables. Analyzing contingency tables is typically a question of association - do the variables represented in the table exhibit special dependencies or lack thereof? The statistical models which best capture these experimental notions of dependence are the categorical conditional independence models; however, until recent discoveries concerning the strongly algebraic nature of the conditional independence models surfaced, the models were widely overlooked due to their unwieldy implicit description. Apart from the inferential question above, this thesis asks the more basic question - suppose such an experimental model of association is known, how can one incorporate this information into the estimation of the joint distribution of the table? In the traditional parametric setting several estimation paradigms have been developed over the past century; however, traditional results are not applicable to arbitrary categorical conditional independence models due to their implicit nature. After laying out the framework for conditional independence and algebraic statistical models, we consider three aspects of estimation in the models using the minimum Euclidean (L2E), minimum Pearson chi-squared, and minimum Neyman modified chi-squared distance paradigms as well as the more ubiquitous maximum likelihood approach (MLE). First, we consider the theoretical properties of the estimators and demonstrate that under general conditions the estimators exist and are asymptotically normal. For small samples, we present the results of large scale simulations to address the estimators' bias and mean squared error (in the Euclidean and Frobenius norms, respectively). Second, we identify the computation of such estimators as an optimization problem and, for the case of the L2E, propose two different methods by which the problem can be solved, one algebraic and one numerical. Finally, we present an R implementation via two novel packages, mpoly for symbolic computing with multivariate polynomials and catcim for fitting categorical conditional independence models. It is found that in general minimum distance estimators in categorical conditional independence models behave as they do in the more traditional parametric setting and can be computed in many practical situations with the implementation provided.

Quantile regression, a method used to estimate conditional quantiles of a set of data ( X, Y ), was popularized by Koenker and Bassett (1978). For a particular quantile q , the q th quantile estimate of Y given X = x can be found using an asymmetrically-weighted, absolute-loss criteria. This form of regression is considered to be robust, in that it is less affected by outliers in the data set than least-squares regression. However, like standard L 1 regression, this form of quantile regression can still be affected by multiple outliers. In this thesis, we propose a method for improving robustness in quantile regression through an application of Scott's L 2 Estimation (2001). Theoretic and asymptotic results are presented and used to estimate properties of our method. Along with simple linear regression, semiparametric extensions are examined. To verify our method and its extensions, simulated results are considered. Real data sets are also considered, including estimating the effect of various factors on the conditional quantiles of child birth weight, using semiparametric quantile regression to analyze the relationship between age and personal income, and assessing the value distributions of Major League Baseball players.

This study investigates whether the girls' education project "Haydi Kzlar Okula!" was able to increase girls' schooling and to what extent it was effective. In Turkey, there is still gender disparity in primary education although it is compulsory. "Haydi Kzlar Okula!" is UNICEF and Turkish Ministry of Education's joint project, which aims to increase girls' primary enrollment. The project consists of increasing public awareness, free books and incentives (in the form of conditional cash transfer) for female students. To find the magnitude of the program's impact, data is collected from Turkish and European statistical databases and a panel data analysis is employed. The results show that if the program has been implemented in a province, girls' enrollment rate increases by 1.310-2 units and total schooling increases by 1.410-2 units. Conditional Cash Transfer (CCT) found to have a bigger impact on girls' enrollment rates than total enrollment rates but the impact is not statistically significant. When a dummy for poverty is included in the model, then CCT becomes significant and the impact can be interpreted as; 1% increase in the conditional cash paid to a province results in 1.310-4 units increase in girls' enrollment rates. It is concluded that the project's impact is statistically significant but the magnitude is smaller than expected. Improvements are needed for increasing the effectiveness of the project. New cash transfer schemes should be implemented and community contribution should be encouraged. Another result of the analysis show that school buildings and adult literacy have greater impacts than the girls' education project.

In this paper we examine the extent of international trade synchronization during periods of international trade collapses and US recessions. Using dynamic correlations based on monthly trade data for the G7 economies over the period 1961-2011, our results suggest rather idiosyncratic patterns of international trade synchronization during collapses of international trade and US recessions. During the great recession of 2007-2009, however, international trade experienced the most sudden, severe and globally synchronized collapse. (author's abstract)