The hardware technology that underlies computer systems has improved so rapidly and continuously for more than four decades that the ground rules for system design are constantly subject to change. It takes many years for knowledge and experience to be compiled, digested, and presented in the form of a book, so books about computer systems often seem dated or obsolete by the time they appear in print.
Even though some underlying principles are unchanging, the rapid obsolescence of details acts to discourage prospective book authors, and as a result some important ideas are never documented in books. For this reason, an essential part of the study of computer systems is found in current and, frequently, older technical papers, professional journal articles, research reports, and occasional, unpublished memoranda that circulate among active workers in the field.
The present dissertation includes my research on modeling financial market returns and volatility on high frequency data. It consists of three chapters. The first chapter introduces a novel high-frequency volatility estimator based on large price moves, which constitutes a generalization of the standard range. An asymptotic theory is developed in a jump-diffusion setting, originating a family of strongly consistent diffusive volatility estimators that are robust to jumps. This theory tackles maximization on a time grid not previously studied in the volatility literature and uncovers valuable distributional properties of Brownian peaks and troughs, formalized as h-extrema. Sup-plementary extensive simulation evidence documents that the generalized range behaves in accordance with the derived theory and compares favorably to other known estimators of diffusive volatility that are robust to jumps. From this perspective, the generalized range provides a new link between recent advances in high-frequency volatility estimation and the long-established efficiency of the standard range.
It is often tempting to attempt to infer the welfare effects of actual minimum wage changes from empirical observations on pre and post change employment and unemployment levels and wage or earnings distributions. For example, minimum wage increases are often explicitly or implicitly taken to be beneficial for the impacted population if reductions in group employment rates are found to be small or positive. As another example, although a very small percentage of U.S. workers are paid the minimum wage, larger impacts on welfare on the general population are often taken to result from a sort of ripple effect from the bottom up, which in terms of the wage distribution itself is often referred to as spillover. Empirical evidence that demonstrates a change in the shape of the wage distribution above the minimum wage is often interpreted as resulting from these spillover effects, and by their very nature these are assumed to be beneficial for individuals on the supply side of the market.
In this paper we shall rigorously attempt to define, characterize, and explain the phenomenon of spillover. To accomplish this task, we will utilize a simple model of search and bargaining in a stationary environment. While the model is admittedly highly stylized, in a companion paper (Flinn 1999) we show that it can be estimated using Current Population Survey (CPS) data and fits observed wage and unemployment duration distributions quite well. Thus the model at a minimum provides a parsimonious and readily interpretable view of the labor market as it is reflected in CPS data, and for this reason can be given some credibility. The model is readily adapted to allow for the existence of binding minimum wages rates. The equilibrium which results from the imposition of a minimum wage [or an increase in value of an already binding minimum wage] is roughly in accordance with empirical work on this subject based on disaggregated data. In particular, a few of the implications of the model are: (1) the existence of a probability mass at the minimum wage m with an absolutely continuous distribution to the right of m> (2) decreases in employment rates with increases in the minimum wage; and (3) the possible existence of spillover effects in response to a change in the minimum wage rate. Using the model, we will rigorously define a particular welfare measure as well as spillovers. We show that welfare in the population can increase even though employment rates decrease and with or without spillovers in the wage distribution. Conversely, spillover effects of minimum wage changes do not indicate that the change was necessarily welfare?enhancing. The main point to which we want to draw attention is that the welfare effects of minimum wage changes can only be inferred by using empirical evidence on employment rates, wage distributions, and a formal model within which welfare can be rigorously defined and evaluated.
