This paper introduces a time-varying threshold autoregressive model (TVTAR), which is used to examine the persistence of deviations from PPP. We find support for the stationary TVTAR against the unit root hypothesis; however, for some developing countries, we do not reject the TVTAR with a unit root in the corridor regime. We calculate magnitudes, frequencies, and durations of the deviations of exchange rates from forecasted changes in exchange rates. A key result is asymmetric adjustment. In developing countries, the average cumulative deviation from forecasts during periods when exchange rates are below forecasts is twice the corresponding measure during periods when exchange rates are above forecasts.

This book is the outgrowth of both a research program and a graduate course at the University of California, Irvine (UCI) since 1966, as well as a graduate course at the California State Polytechnic University, Pomona (Cal Poly Pomona). The research program, part of the UCI Pattern Recogni tion Project, was concerned with the design of trainable classifiers; the graduate courses were broader in scope, including subjects such as feature selection, cluster analysis, choice of data set, and estimates of probability densities. In the interest of minimizing overlap with other books on pattern recogni tion or classifier theory, we have selected a few topics of special interest for this book, and treated them in some depth. Some of this material has not been previously published. The book is intended for use as a guide to the designer of pattern classifiers, or as a text in a graduate course in an engi neering or computer science curriculum. Although this book is directed primarily to engineers and computer scientists, it may also be of interest to psychologists, biologists, medical scientists, and social scientists.

This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.