Prior studies show the evidence of non-financial variables such as marketing affects investor¡¦s response to risky asset pricing, and indicate that the distribution of risky asset returns is asymmetric and non-nomality, implying using Ordinary Least Squares (OLS) method with the assumption of normal distributions may lead to unreliable estimates. This study tries to apply quantile regression to the analysis of the sample in order to avoid estimation bias. This study examines whether a firm¡¦s pre-IPO marketing expenditures affects its¡¦ initial public offering (IPO) underpricing in Japan and examine whether market states influence the existing relation between pre-IPO marketing expenditures and IPO underpricing. The empirical results shows: (1) pre-IPO marketing expenditures significantly reduce IPO underpricing levels, (2) pre-IPO marketing expenditures can reduce IPO underpricing levels following bear markets as it cannot reduce IPO underpricing levels following bull markets. Therefore, as firms decide to use marketing strategies to make their firm remarkable, and in turns without concerning for market states to reduce the degree of IPO underpricing, their objective may not be reached.

This paper presents a simple recursive formula to compute the Taylor polynomial of quantile for a continuous random variable. It is very easy to implement the formula in standard symbolic programming system, for example Mathematica (Wolfram, 2003). Applications of the formula to standard normal distribution and to the generation of random variables for continuous distribution with bounded support are illustrated.

There was a well documented surge of wage inequality in the US that started from mid-70s and continued in 80s, slowing down by mid-90s, caused by increased dispersion both between and within groups of people with similar personal characteristics and skills. We analyze the US wage inequality in the more recent years to see if this trend continues. We apply the decomposition technique of Juhn, Murphy and Pierce (1993) and quantile regression to March Current Population Survey data of the US Bureau of Labor Statistics data and Luxembourg Income Study data for a few selected European countries. We find that the increase in wage inequality continues during the 90s, especially in the second half. In addition, the focus of wage inequality shifts into the upper half of the wage distribution after mid-80s. The European countries do not show a common trend in the direction of wage inequality during the 90s. However, the focus of their wage inequality seems to be shifting towards the lower half of the wage distribution as opposed to that of US.

L'objectif de ce travail est l'estimation d'une fonction quantile extrême. Nous considérons n couples de variables aléatoires indépendants et de même loi qu'un couple (X,Y) à support borné du plan. Notre but est d'estimer le quantile extrême (i.e. d'ordre inférieur à 1/n) de la fonction de répartition conditionnelle de Y sachant que X=x. La fonction de x ainsi définie est appelée fonction quantile extrême. Pour ce faire, nous introduisons au préalable un estimateur de l'indice de valeur extreme et nous en déduisons un estimateur de quantile extrême. Ces estimateurs ont la particularité de n'utiliser uniquement l'information apportée par des nombres de points qui dépassent des seuils aléatoires. Nous établissons la consistance faible des estimateurs et nous étudions leurs comportements sur quelques simulations.

Comprehensive statistical models for non-normally distributed cancerous tumor sizes are of prime importance in epidemiological studies, whereas a long term forecasting models can facilitate in reducing complications and uncertainties of medical progress. The statistical forecasting models are critical for a better understanding of the disease and supply appropriate treatments. In addition such a model can be used for the allocations of budgets, planning, control and evaluations of ongoing efforts of prevention and early detection of the diseases. In the present study, we investigate the effects of age, demography, and race on primary brain tumor sizes using quantile regression methods to obtain a better understanding of the malignant brain tumor sizes. The study reveals that the effects of risk factors together with the probability distributions of the malignant brain tumor sizes, and plays significant role in understanding the rate of change of tumor sizes. The data that our analysis and modeling is based on was obtained from Surveillance Epidemiology and End Results (SEER) program of the United States. We also analyze the discretely observed brain cancer mortality rates using functional data analysis models, a novel approach in modeling time series data, to obtain more accurate and relevant forecast of the mortality rates for the US. We relate the cancer registries, race, age, and gender to age-adjusted brain cancer mortality rates and compare the variations of these rates during the period of the study that data was collected. Finally, in the present study we have developed effective statistical model for heterogenous and high dimensional data that forecast the hazard rates with high degree of accuracy, that will be very helpful to address subject health problems at present and in the future.

Using income and other individual data from EU-SILC for Germany and Austria, we analyze wage discrimination for three break-ups: gender, sector of employment, and country of origin. Using the method of Machado and Mata [2005] the discrimination over the whole range of the wage distribution is estimated. Significance of results is checked via confidence interval estimates along the lines of Melly [2006]. To narrow down the extent of discrimination both basic decomposition possibilities are compared. The economies of Germany and Austria appear structurally very similar. Especially the institutional setting of the labor markets seem to be closely comparable. One would, therefore, expect to find similar levels and structures of wage discrimination. Our findings deviate from this conjecture significantly. (author's abstract) / Series: Department of Economics Working Paper Series

With the significant rise in immigration to the U.S. over the last few decades, fully understanding the economic impact of immigration is paramount for policy makers. As such, this dissertation consists of three empirical essays contributing to the literature on the impact of immigration. In my first essay, I re-examine the impact of immigration on housing rents and completely controlling for endogenous location choices of immigrants. I model rents as a function of both contemporaneous and initial economic and housing market conditions. I show that existing estimates of the impact of immigration on rents are biased and the source of the bias is the instrumental variable strategy common in much of the immigration literature. In my second essay, I present a new approach to estimating the effect of immigration on native wages. Noting the imperfect substitutability of immigrants and natives within education groups, I posit an empirical framework where labor markets are stratified by occupations. Using occupation-specific skill to define homogeneous skill groups, I estimate the partial equilibrium (within skill group) effect of immigration. The results suggest that when one defines labor market cohorts that directly compete in the labor market, the effect of immigration on native wages is roughly twice as large as previous estimates in the literature. In my third essay, I return to the housing market and examine the effects of immigration within metropolitan areas. Specifically, I investigate the relationship between immigrant inflows, native outflows, and rents. Taking advantage of the unique settlement patterns of immigrants, I show that the effect of immigration on rents is lower in both high-immigrant neighborhoods and portions of the rent distribution where immigrants cluster. Contrary to the existing belief in the literature, the results suggest that the preferences of natives, not immigrants, bid up rents in response to an immigrant inflow.

Safety reviews of existing roads are becoming a popular practice of many agencies nationally and internationally. Knowing road safety information is of great importance to both policymakers in addressing safety concerns and travelers in managing their trips. There have been various efforts in developing methodologies to measure and assess road safety in an effective manner. However, the existing research and practices are still constrained by their subjective and reactive nature. The goal of this research is to develop a framework of Road Risk Indices (RRIs) to assess road risks of existing highway infrastructure for both road users and agencies based on road geometrics, traffic conditions, and historical crash data. The proposed RRIs are intended to give a comprehensive and objective view of road safety, so that safety problems can be identified at an early stage before they rise in the form of accidents. A methodological framework of formulating RRIs that integrates results from crash prediction models and historical crash data is proposed, and Linear Referencing tools in the ArcGIS software are used to develop digital maps to publish estimated RRIs. These maps provide basic Geographic Information System (GIS) functions, including viewing and querying RRIs, and performing spatial analysis tasks. A semi-parameter count model and quantile regression based estimation are proposed to capture the specific characteristics of crash data and provide more robust and accurate predictions on crash counts. Crash data collected on Interstate Highways in Washington State for the year 2002 was extracted from the Highway Safety Information System (HSIS) and used for the case study. The results from the case study show that the proposed framework is capable of capturing statistical correlations between traffic crashes and influencing factors, leading to the effective integration of safety information in composite indices. / text

This dissertation considers quantile regression models with nonstationary or nearly nonstationary time series. The first chapter outlines the thesis and discusses its theoretical and empirical contributions. The second chapter studies inference in quantile regressions with cointegrated variables allowing for multiple structural changes. The unknown break dates and regression coefficients are estimated jointly and consistently. The conditional quantile estimator has a nonstandard limit distribution. A fully modified estimator is proposed to remove the second-order bias and nuisance parameters and the resulting limit distribution is mixed normal. A simulation study shows that the fully modified quantile estimator has good finite sample properties. The model is applied to stock index data from the emerging markets of China and several mature markets. Financial market integration is found in some quantiles of the Chinese stock indices. The third chapter considers predictive quantile regression with a nearly integrated regressor. We derive nonstandard distributions for the quantile regression estimator and t-statistic in terms of functionals of diffusion processes. The critical values are found to depend on both the quantile of interest and the local-to-unity parameter, which is not consistently estimable. Based on these critical values, we propose a valid Bonferroni bounds test for quantile predictability with persistent regressors. We employ this new methodology to test the ability of many commonly employed and highly persistent regressors, such as the dividend yield, earnings price ratio, and T-bill rate, to predict the median, shoulders, and tails of the stock return distribution. Chapter Four proposes a cumulated sum (CUSUM) test for the null hypothesis of quantile cointegration. A fully modified quantile estimator is adopted for serial correlation and endogeneity corrections. The CUSUM statistic is composed of the partial sums of the residuals from the fully modified quantile regression. Under the null, the test statistic converges to a functional of Brownian motions. In the application to U.S. interest rates of different maturities, evidence in favor of the expectations hypothesis for the term structure is found in the central part of the distributions of the Treasury bill rate and financial commercial paper rate, but in the tails of the constant maturity rate distribution. / Thesis (Ph.D, Economics) -- Queen's University, 2012-07-30 15:20:38.253

Modelling the tails of distributions is important in many fields, such as environmental science, hydrology, insurance, engineering and finance, where the risk of unusually large or small events are of interest. This thesis applies extreme value models in neonatal and finance studies and develops novel extreme value modelling for financial applications, to overcome issues associated with the dependence induced by volatility clustering and threshold choice. The instability of preterm infants stimulates the interests in estimating the underlying variability of the physiology measurements typically taken on neonatal intensive care patients. The stochastic volatility model (SVM), fitted using Bayesian inference and a particle filter to capture the on-line latent volatility of oxygen concentration, is used in estimating the variability of medical measurements of preterm infants to highlight instabilities resulting from their under-developed biological systems. Alternative volatility estimators are considered to evaluate the performance of the SVM estimates, the results of which suggest that the stochastic volatility model provides a good estimator of the variability of the oxygen concentration data and therefore may be used to estimate the instantaneous latent volatility for the physiological measurements of preterm infants. The classical extreme value distribution, generalized pareto distribution (GPD), with the peaks-over-threshold (POT) method to ameliorate the impact of dependence in the extremes to infer the extreme quantile of the SVM based variability estimates. Financial returns typically show clusters of observations in the tails, often termed “volatility clustering” which creates challenges when applying extreme value models, since classical extreme value theory assume independence of underlying process. Explicit modelling on GARCH-type dependence behaviour of extremes is developed by implementing GARCH conditional variance structure via the extreme value model parameters. With the combination of GEV and GARCH models, both simulation and empirical results show that the combined model is better suited to explain the extreme quantiles. Another important benefit of the proposed model is that, as a one stage model, it is advantageous in making inferences and accounting for all uncertainties much easier than the traditional two stage approach for capturing this dependence. To tackle the challenge threshold choice in extreme value modelling and the generally asymmetric distribution of financial data, a two tail GPD mixture model is proposed with Bayesian inference to capture both upper and lower tail behaviours simultaneously. The proposed two tail GPD mixture modelling approach can estimate both thresholds, along with other model parameters, and can therefore account for the uncertainty associated with the threshold choice in latter inferences. The two tail GPD mixture model provides a very flexible model for capturing all forms of tail behaviour, potentially allowing for asymmetry in the distribution of two tails, and is demonstrated to be more applicable in financial applications than the one tail GPD mixture models previously proposed in the literature. A new Value-at-Risk (VaR) estimation method is then constructed by adopting the proposed mixture model and two-stage method: where volatility estimation using a latent volatility model (or realized volatility) followed by the two tail GPD mixture model applied to independent innovations to overcome the key issues of dependence, and to account for the uncertainty associated with threshold choice. The proposed method is applied in forecasting VaR for empirical return data during the current financial crisis period.