This study develops a multi-period log-return quantile forecasting procedure to evaluate the performance of eleven nearby commodity futures contracts (NCFC) using a sample of 897 daily price observations and at-the-money (ATM) put and call implied volatilities of the corresponding prices for the period from 1/16/2008 to 7/29/2011. The statistical approach employs dynamic log-returns quantile regression models to forecast price densities using implied volatilities (IVs) and factors estimated through principal component analysis (PCA) from the IVs, pooled IVs and lagged returns. Extensive in-sample and out-of-sample analyses are conducted, including assessment of excess trading returns, and evaluations of several combinations of quantiles, model specifications, and NCFC's. The results suggest that the IV-PCA-factors, particularly pooled return-IV-PCA-factors, improve quantile forecasting power relative to models using only individual IV information. The ratio of the put-IV to the call-IV is also found to improve quantile forecasting performance of log returns. Improvements in quantile forecasting performance are found to be better in the tails of the distribution than in the center. Trading performance based on quantile forecasts from the models above generated significant excess returns. Finally, the fact that the single IV forecasts were outperformed by their quantile regression (QR) counterparts suggests that the conditional distribution of the log-returns is not normal.

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.

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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.

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.

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.

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