Long Memory in Economics

Long Memory in Economics


389 Pages


About this book

When applying the statistical theory of long range dependent (LRD) processes to economics, the strong complexity of macroeconomic and financial variables, compared to standard LRD processes, becomes apparent. In order to get a better understanding of the behaviour of some economic variables, the book assembles three different strands of long memory analysis: statistical literature on the properties of, and tests for, LRD processes., mathematical literature on the stochastic processes involved., models from economic theory providing plausible micro foundations for the occurence of long memory in economics. Each chapter of the book will give a comprehensive survey of the state of the art and the directions that future developments are likely to take. Taken as a whole the book provides an overview of LRD processes which is accessible to economists, econometricians and statisticians.

Written for:
Economists, econometricians, and statisticians interested in the study of long memory in economics
Long Memory
Long Range Dependent Processes
Market Interaction



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Published 22 September 2006
Reads 12
EAN13 9783540346258
License: All rights reserved
Language English
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Statistical Methods.- Recent Advances in ARCH Modelling.- Intermittency, Long-Memory and Financial Returns.- The Spectrum of Euro-Dollar.- Hölderian Invariance Principles and Some Applications for Testing Epidemic Changes.- Adaptive Detection of Multiple Change-Points in Asset Price Volatility.- Bandwidth Choice, Optimal Rates and Adaptivity in Semiparametric Estimation of Long Memory.- Wavelet Analysis of Nonlinear Long-Range Dependent Processes. Applications to Financial Time Series.- Prediction, Orthogonal Polynomials and Toeplitz Matrices. A Fast and Reliable Approximation to the Durbin-Levinson Algorithm.- Economic Models.- A Nonlinear Structural Model for Volatility Clustering.- Volatility Clustering in Financial Markets: Empirical Facts and Agent-Based Models.- The Microeconomic Foundations of Instability in Financial Markets.- A Minimal Noise Trader Model with Realistic Time Series Properties.- Long Memory and Hysteresis.