The Econometric Analysis of Seasonal Time Series
The Econometric Analysis of Seasonal Time Series

Author: Eric Ghysels

Publisher: Cambridge University Press

Published: 2001-06-18

Total Pages: 258

ISBN-13: 9780521565882

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Eric Ghysels and Denise R. Osborn provide a thorough and timely review of the recent developments in the econometric analysis of seasonal economic time series, summarizing a decade of theoretical advances in the area. The authors discuss the asymptotic distribution theory for linear nonstationary seasonal stochastic processes. They also cover the latest contributions to the theory and practice of seasonal adjustment, together with its implications for estimation and hypothesis testing. Moreover, a comprehensive analysis of periodic models is provided, including stationary and nonstationary cases. The book concludes with a discussion of some nonlinear seasonal and periodic models. The treatment is designed for an audience of researchers and advanced graduate students.

Cointegration, Causality, and Forecasting
Cointegration, Causality, and Forecasting

Author: Halbert White

Publisher: Oxford University Press, USA

Published: 1999

Total Pages: 512

ISBN-13: 9780198296836

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A collection of essays in honour of Clive Granger. The chapters are by some of the world's leading econometricians, all of whom have collaborated with and/or studied with both) Clive Granger. Central themes of Granger's work are reflected in the book with attention to tests for unit roots and cointegration, tests of misspecification, forecasting models and forecast evaluation, non-linear and non-parametric econometric techniques, and overall, a careful blend of practical empirical work and strong theory. The book shows the scope of Granger's research and the range of the profession that has been influenced by his work.

A Course in Time Series Analysis
A Course in Time Series Analysis

Author: Daniel Peña

Publisher: John Wiley & Sons

Published: 2011-01-25

Total Pages: 494

ISBN-13: 1118031229

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New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include: Contributions from eleven of the worldâ??s leading figures in time series Shared balance between theory and application Exercise series sets Many real data examples Consistent style and clear, common notation in all contributions 60 helpful graphs and tables Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis. An Instructor's Manual presenting detailed solutions to all the problems in he book is available upon request from the Wiley editorial department.

Handbook of Economic Forecasting
Handbook of Economic Forecasting

Author: G. Elliott

Publisher: Elsevier

Published: 2006-05-30

Total Pages: 1071

ISBN-13: 0080460674

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Research on forecasting methods has made important progress over recent years and these developments are brought together in the Handbook of Economic Forecasting. The handbook covers developments in how forecasts are constructed based on multivariate time-series models, dynamic factor models, nonlinear models and combination methods. The handbook also includes chapters on forecast evaluation, including evaluation of point forecasts and probability forecasts and contains chapters on survey forecasts and volatility forecasts. Areas of applications of forecasts covered in the handbook include economics, finance and marketing.*Addresses economic forecasting methodology, forecasting models, forecasting with different data structures, and the applications of forecasting methods *Insights within this volume can be applied to economics, finance and marketing disciplines

Likelihood-based Inference in Cointegrated Vector Autoregressive Models
Likelihood-based Inference in Cointegrated Vector Autoregressive Models

Author: Søren Johansen

Publisher: Oxford University Press, USA

Published: 1995

Total Pages: 280

ISBN-13: 0198774508

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This monograph is concerned with the statistical analysis of multivariate systems of non-stationary time series of type I. It applies the concepts of cointegration and common trends in the framework of the Gaussian vector autoregressive model.

Periodic Time Series Models
Periodic Time Series Models

Author: Philip Hans Franses

Publisher: OUP Oxford

Published: 2004-03-25

Total Pages: 166

ISBN-13: 0191529265

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This book considers periodic time series models for seasonal data, characterized by parameters that differ across the seasons, and focuses on their usefulness for out-of-sample forecasting. Providing an up-to-date survey of the recent developments in periodic time series, the book presents a large number of empirical results. The first part of the book deals with model selection, diagnostic checking and forecasting of univariate periodic autoregressive models. Tests for periodic integration, are discussed, and an extensive discussion of the role of deterministic regressors in testing for periodic integration and in forecasting is provided. The second part discusses multivariate periodic autoregressive models. It provides an overview of periodic cointegration models, as these are the most relevant. This overview contains single-equation type tests and a full-system approach based on generalized method of moments. All methods are illustrated with extensive examples, and the book will be of interest to advanced graduate students and researchers in econometrics, as well as practitioners looking for an understanding of how to approach seasonal data.

Workbook on Cointegration
Workbook on Cointegration

Author: Peter Reinhard Hansen

Publisher: Oxford University Press, USA

Published: 1998

Total Pages: 178

ISBN-13: 9780198776086

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Aimed at graduates and researchers in economics and econometrics, this is a comprehesive exposition of Soren Johansen's remarkable contribution to the theory of cointegration analysis.

Econometric Forecasting and High-frequency Data Analysis
Econometric Forecasting and High-frequency Data Analysis

Author: Roberto S. Mariano

Publisher: World Scientific

Published: 2008

Total Pages: 200

ISBN-13: 9812778969

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This important book consists of surveys of high-frequency financial data analysis and econometric forecasting, written by pioneers in these areas including Nobel laureate Lawrence Klein. Some of the chapters were presented as tutorials to an audience in the Econometric Forecasting and High-Frequency Data Analysis Workshop at the Institute for Mathematical Science, National University of Singapore in May 2006. They will be of interest to researchers working in macroeconometrics as well as financial econometrics. Moreover, readers will find these chapters useful as a guide to the literature as well as suggestions for future research. Sample Chapter(s). Foreword (32 KB). Chapter 1: Forecast Uncertainty, Its Representation and Evaluation* (97 KB). Contents: Forecasting Uncertainty, Its Representation and Evaluation (K F Wallis); The University of Pennsylvania Models for High-Frequency Macroeconomic Modeling (L R Klein & S Ozmucur); Forecasting Seasonal Time Series (P H Franses); Car and Affine Processes (C Gourieroux); Multivariate Time Series Analysis and Forecasting (M Deistler). Readership: Professionals and researchers in econometric forecasting and financial data analysis.