Hamilton James D. Time Series Analysis

John Wiley & Sons Inc. , 2002. 458 страниц. Contents: Financial Time Series and Their Characteristics Asset Returns, Distributional Properties of Returns, Processes Considered. Linear Time Series Analysis and Its Applications Stationarity, Correlation and Autocorrelation Function, White Noise and Linear Time Series, Simple Autoregressive Models, Simple Moving-Average Models, Simple ARMA Models, Unit-Root Nonstationarity, Seasonal Models,...

Time series econometrics is a rapidly evolving field. In particular, the cointegration revolution has had a substantial impact on applied analysis. As a consequence of the fast pace of development, there are no textbooks that cover the full range of methods in current use and explain how to proceed in applied domains. This gap in the literature motivates the present volume. The methods are sketched out briefly to remind the reader of the ideas un...

Graduate School of Business. 1997 What is a time series? ARMA model The a utocorrelation and autocovariance functions Prediction and Impulse-Response Functions Stationarity and Wold representation VARs: orthogonalization, variance decomposition, Granger causality Spectral Representation Spectral alanalysis in finite samples Unit Roots Cointegration

Chapman & Hall/CRC, 2010. 305 pages. This book aims at introducing and explaining basic methods of building models for time series. In time series modeling, we try to express the behavior of a certain phenomenon in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part o...

Chapman & Hall. 2001. Pages: 320 Singular spectrum analysis (SSA) has proven very successful. It has already become a standard tool in climatic and meteorological time series analysis and well known in nonlinear physics and signal processing. However, despite the promise it holds for time series applications in other disciplines, SSA is not widely known among statisticians and econometrists, and although the basic SSA algorithm looks simple,...

Wiley – 2010, 550 pages ISBN 9780470066300 Autoregressive Conditional Heteroskedastic (ARCH) processes are used in finance to model asset price volatility over time. This book introduces both the theory and applications of ARCH models and provides the basic theoretical and empirical background, before proceeding to more advanced issues and applications. The Authors provide coverage of the recent developments in ARCH modelling which can be imple...