Posted by **interes** at Jan. 22, 2015

English | 2014 | ISBN: 1118808568 | 496 pages | PDF | 7 MB

Posted by **vijayvits12** at May 12, 2009

Wiley | ISBN : 0471230650 | 2003-08-01 | PDF | 480 Pages | 2,6 Mb

Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. The first edition of Applied Econometric Time Series was among those chosen.

This new edition reflects recent advances in time-series econometrics, such as out-of-sample forecasting techniques, non-linear time-series models, Monte Carlo analysis, and bootstrapping. Numerous examples from fields ranging from agricultural economics to transnational terrorism illustrate various techniques.

Posted by **ChrisRedfield** at April 12, 2014

Published: 2004-11-08 | ISBN: 0521814073 | PDF | 734 pages | 3 MB

Posted by **tvladb** at Sept. 25, 2012

Publisher: Wiley; 3 edition (November 2, 2009) | ISBN: 0470505397 | Pages: 544 | PDF | 17.98 MB

Enders continues to provide business professionals with an accessible introduction to time-series analysis. He clearly shows them how to develop models capable of forecasting, interpreting, and testing hypotheses concerning economic data using the latest techniques. The third edition includes new discussions on parameter instability and structural breaks as well as out-of-sample forecasting methods. New developments in unit root test and cointegration tests are covered. Multivariate GARCH models are also presented. In addition, several statistical examples have been updated with real-world data to help business professionals understand the relevance of the material.

Posted by **arundhati** at Aug. 3, 2014

2005 | ISBN-10: 981256117X | 245 pages | PDF | 13 MB

Posted by **spiderman** at Nov. 27, 2006

World Scientific Publishing Company | ISBN 981256117X | April 2005 | PDF | 145 Pages | 13,45 Mb

Nonlinear time series methods have developed rapidly over a quarter of a century and have reached an advanced state of maturity during the last decade. Implementations of these methods for experimental data are now widely accepted and fairly routine, however genuinely useful applications remain rare. The aim of this book is to focus on the practice of applying these methods to solve real problems. It is my hope that the methods presented here are sufficiently accessible, and the examples sufficiently detailed, that practitioners in other areas may use this work to begin considering further applications of nonlinear time series analysis in their own disciplines.

This volume is therefore intended to be accessible to a fairly broad audience:

both specialists in nonlinear time series analysis (for whom many of these techniques may be new); and, scientists in other fields (who may be looking to apply these methods within their speciality). For the experimental scientist looking to use these methods, MATLAB implementation of the underlying algorithms accompany this book.

Posted by **libr** at April 12, 2017

English | 2012 | ISBN: 0521139813 , 0521196604 | 937 pages | PDF | 7,5 MB

Posted by **manamba13** at Feb. 12, 2015

English | 2006 | ISBN: 0762312734 | 379 Pages | PDF | 3 MB

The editors are pleased to offer the following papers to the reader in recognition and appreciation of the contributions to our literature made by Robert Engle and Sir Clive Granger

Posted by **manamba13** at Jan. 26, 2015

English | 2003 | ISBN: 1402073682 | 486 Pages | PDF | 11 MB

From 1976 to the beginning of the millennium—covering the quarter-century life span of this book and its predecessor—something remarkable has happened to market response research: it has become practice.

Posted by **interes** at Aug. 1, 2014

English | 2012 | ISBN: 0521139813 , 0521196604 | 937 pages | PDF | 7,5 MB

This book provides a general framework for specifying, estimating, and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalized method of moments estimation, nonparametric estimation, and estimation by simulation.