20 research outputs found
RACIAL AND GENDER DIFFERENCES IN COLLEGE STUDENT EMPLOYMENT CHOICES
No full textThis paper finds racial and gender differences in college students’ decision to work for pay during school even after accounting for socioeconomic and other factors. Black and female students are found to have a greater likelihood of working for pay during college, while Asian students are found to be less likely to work for pay. These analysis findings are based off of four years of data from the National Longitudinal Survey of Freshman (NLSF) indicating whether students worked for pay during each academic year
Family Planning and Female Empowerment: An Empirical Analysis of Birth Prevention and Women’s Labor Force Participation and Education
No full textBias reduction in nonlinear and dynamic panels in the presence of cross-section dependence
No full textFixed effects estimation of nonlinear dynamic panel models is subject to the incidental
parameter issue, leading to a biased asymptotic distribution. While this problem has
been studied extensively in the literature, a general analysis allowing for both serial
and cross-sectional dependence is missing. In this paper we investigate the large-N, T
theory of the profile and integrated likelihood estimators, allowing for dependence
across both dimensions. We show that under stronger dependence types the asymptotic
bias disappears, but a Op(1/T ) small-sample bias remains. We provide bias correction
and inference methods, and also obtain primitive conditions for asymptotic normality
under various dependence settings
Essays in panel data and financial econometrics
No full textThis thesis is concerned with volatility estimation using financial panels and bias-reduction in non-linear dynamic panels in the presence of dependence.Traditional GARCH-type volatility models require large time-series for accurate estimation. This makes it impossible to analyse some interesting datasets which do not have a large enough history of observations. This study contributes to the literature by introducing the GARCH Panel model, which exploits both time-series and cross-section information, in order to make up for this lack of time-series variation. It is shown that this approach leads to gains both in- and out-of-sample, but suffers from the well-known incidental parameter issue and therefore, cannot deal with short data either. As a response, a bias-correction approach valid for a general variety of models beyond GARCH is proposed. This extends the analytical bias-reduction literature to cross-section dependence and is a theoretical contribution to the panel data literature. In the final chapter, these two contributions are combined in order to develop a new approach to volatility estimation in short panels. Simulation analysis reveals that this approach is capable of removing a substantial portion of the bias even when only 150-200 observations are available. This is in stark contrast with the standard methods which require 1,000-1,500 observations for accurate estimation. This approach is used to model monthly hedge fund volatility, which is another novel contribution, as it has hitherto been impossible to analyse hedge fund volatility, due to their typically short histories. The analysis reveals that hedge funds exhibit variation in their volatility characteristics both across and within investment strategies. Moreover, the sample distributions of fund volatilities are asymmetric, have large right tails and react to major economic events such as the recent credit crunch episode
Essays in panel data and financial econometrics
No full textThis thesis is concerned with volatility estimation using financial panels and bias-reduction in non-linear dynamic panels in the presence of dependence. Traditional GARCH-type volatility models require large time-series for accurate estimation. This makes it impossible to analyse some interesting datasets which do not have a large enough history of observations. This study contributes to the literature by introducing the GARCH Panel model, which exploits both time-series and cross-section information, in order to make up for this lack of time-series variation. It is shown that this approach leads to gains both in- and out-of-sample, but suffers from the well-known incidental parameter issue and therefore, cannot deal with short data either. As a response, a bias-correction approach valid for a general variety of models beyond GARCH is proposed. This extends the analytical bias-reduction literature to cross-section dependence and is a theoretical contribution to the panel data literature. In the final chapter, these two contributions are combined in order to develop a new approach to volatility estimation in short panels. Simulation analysis reveals that this approach is capable of removing a substantial portion of the bias even when only 150-200 observations are available. This is in stark contrast with the standard methods which require 1,000-1,500 observations for accurate estimation. This approach is used to model monthly hedge fund volatility, which is another novel contribution, as it has hitherto been impossible to analyse hedge fund volatility, due to their typically short histories. The analysis reveals that hedge funds exhibit variation in their volatility characteristics both across and within investment strategies. Moreover, the sample distributions of fund volatilities are asymmetric, have large right tails and react to major economic events such as the recent credit crunch episode.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Second-order corrected likelihood for nonlinear panel models with fixed effects
No full textsponsorship: We thank Stephane Bonhomme, Roger Moon, Cavit Pakel, participants of the 2017 Tinbergen Institute Conference, the 2017 International Panel Data Conference, and the 2018 International Association for Applied Econometrics Annual Conference, two referees, and the editors for helpful comments. Geert Dhaene acknowledges financial support from the Flemish Science Foundation [grant number G.0505.11]. (Flemish Science Foundation|G.0505.11)status: Publishe
Bounds on Average Effects in Discrete Choice Panel Data Models
No full textIn discrete choice panel data, the estimation of average effects is crucial
for quantifying the effect of covariates, and for policy evaluation and
counterfactual analysis. This task is challenging in short panels with
individual-specific effects due to partial identification and the incidental
parameter problem. In particular, estimation of the sharp identified set is
practically infeasible at realistic sample sizes whenever the number of support
points of the observed covariates is large, such as when the covariates are
continuous. In this paper, we therefore propose estimating outer bounds on the
identified set of average effects. Our bounds are easy to construct, converge
at the parametric rate, and are computationally simple to obtain even in
moderately large samples, independent of whether the covariates are discrete or
continuous. We also provide asymptotically valid confidence intervals on the
identified set
