Prepublication Reviews The authors present, in a simple fashion, a new class of filters that greatly expands on those previously available, allowing greater flexibility and generating models with time-varying specifications. The book considers familiar techniques and shows how these can be viewed in new ways, illustrating them with empirical studies from finance. It is particularly recommended for any time series econometrician wanting to keep up to date.—CLIVE W.J. GRANGER, Professor of Economics, University of California, San DiegoThere are many books on linear filters and wavelets, but there is only one book, Gencay, Selcuk, and Whitcher, that provides an introduction to the field for economists and financial analysts and the motivation to study the subject. This book contains many practical economic and financial examples that will stimulate academic and professional research for years to come. This book is a most welcome addition to the wavelet literature.—JAMES B. RAMSEY, Professor of Economics, New York UniversityThe authors have provided a very comprehensive account of the filtering literature, including wavelets, a tool not widely used in economics and finance. The volume includes many numerical illustrations, and should be accessible to a wide range of researchers.—PETER M. ROBINSON, Tooke Professor of Economic Science and Statistics and Leverhulme Research Professor, London School of Economics, U.K.This timely volume will be of interest to anyone who wants to understand the latest technology for analyzing economic and financial time series. The authors are to be commended for their clear and comprehensive presentation of a fascinating and powerful approach to time-series analysis.—Halbert White, University of California, San Diego An Introduction to Wavelets and Other Filtering Methods in Finance and Economics presents a unified view of filtering techniques with a special focus on wavelet analysis in finance and economics. It emphasizes the methods and explanations of the theory that underlies them. It also concentrates on exactly what wavelet analysis (and filtering methods in general) can reveal about a time series. It offers testing issues which can be performed with wavelets in conjunction with the multi-resolution analysis. The descriptive focus of the book avoids proofs and provides easy access to a wide spectrum of parametric and nonparametric filtering methods. Examples and empirical applications will show readers the capabilities, advantages, and disadvantages of each method. CONTENTS: PrefaceIntroductionLinear FiltersOptimum Linear EstimationDiscrete Wavelet TransformsWavelets and Stationary ProcessesWavelet DenoisingWavelets for Variance-Covariance EstimationArtificial Neural Networks