Nonstationary Time Series Analysis And Cointegration PDF Download
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Author | : Colin P. Hargreaves |
Publisher | : Oxford University Press, USA |
Total Pages | : 336 |
Release | : 1994 |
Genre | : Business & Economics |
ISBN | : |
Download Nonstationary Time Series Analysis and Cointegration Book in PDF, ePub and Kindle
Nonstationary Time Series Analysis and Cointegration shows major developments in the econometric analysis of the long run (of nonstationarity and cointegration) - a field which has developed dramatically over the last twelve years to have a profound effect on econometric analysis in general. The papers here describe and evaluate new methods, provide useful overviews, and show detailed implementations helpful to practitioners. Papers include two substantive analyses of economic forecasting, based around an integral understanding of integration and cointegration and an evaluation of real business cycle models. There is an evaluation of different cointegration estimators and a new test for cointegration. There is a discussion of the effects of seasonality, looking at seasonal unit roots and at encompassing modelling with seasonally unadjusted versus adjusted data. A different style of nonstationarity is raised in a discussion of testing for inflationary bubbles and for time-varying transition probabilities in Hamilton's Markov switching model. This volume provides wide-ranging coverage of the literature, showing the importance of nonstationarity and cointegration.
Author | : Hargreaves Colin P. |
Publisher | : |
Total Pages | : 0 |
Release | : 1994 |
Genre | : |
ISBN | : |
Download Nonstationary Time Series Analysis and Cointegration Book in PDF, ePub and Kindle
Author | : Gebhard Kirchgässner |
Publisher | : Springer Science & Business Media |
Total Pages | : 326 |
Release | : 2012-10-09 |
Genre | : Business & Economics |
ISBN | : 3642334350 |
Download Introduction to Modern Time Series Analysis Book in PDF, ePub and Kindle
This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series, bridging the gap between methods and realistic applications. It presents the most important approaches to the analysis of time series, which may be stationary or nonstationary. Modelling and forecasting univariate time series is the starting point. For multiple stationary time series, Granger causality tests and vector autogressive models are presented. As the modelling of nonstationary uni- or multivariate time series is most important for real applied work, unit root and cointegration analysis as well as vector error correction models are a central topic. Tools for analysing nonstationary data are then transferred to the panel framework. Modelling the (multivariate) volatility of financial time series with autogressive conditional heteroskedastic models is also treated.
Author | : Bernhard Pfaff |
Publisher | : Springer Science & Business Media |
Total Pages | : 193 |
Release | : 2008-09-03 |
Genre | : Business & Economics |
ISBN | : 0387759670 |
Download Analysis of Integrated and Cointegrated Time Series with R Book in PDF, ePub and Kindle
This book is designed for self study. The reader can apply the theoretical concepts directly within R by following the examples.
Author | : Michael P. Clements |
Publisher | : MIT Press |
Total Pages | : 398 |
Release | : 1999 |
Genre | : Business & Economics |
ISBN | : 9780262531894 |
Download Forecasting Non-stationary Economic Time Series Book in PDF, ePub and Kindle
This text on economic forecasting asks why some practices seem to work empirically despite a lack of formal support from theory. After reviewing the conventional approach to forecasting, it looks at the implications for causal modelling, presents forecast errors and delineates sources of failure.
Author | : John Hunter |
Publisher | : Springer |
Total Pages | : 508 |
Release | : 2017-05-08 |
Genre | : Business & Economics |
ISBN | : 113731303X |
Download Multivariate Modelling of Non-Stationary Economic Time Series Book in PDF, ePub and Kindle
This book examines conventional time series in the context of stationary data prior to a discussion of cointegration, with a focus on multivariate models. The authors provide a detailed and extensive study of impulse responses and forecasting in the stationary and non-stationary context, considering small sample correction, volatility and the impact of different orders of integration. Models with expectations are considered along with alternate methods such as Singular Spectrum Analysis (SSA), the Kalman Filter and Structural Time Series, all in relation to cointegration. Using single equations methods to develop topics, and as examples of the notion of cointegration, Burke, Hunter, and Canepa provide direction and guidance to the now vast literature facing students and graduate economists.
Author | : Maurice Bertram Priestley |
Publisher | : |
Total Pages | : 258 |
Release | : 1988 |
Genre | : Mathematics |
ISBN | : |
Download Non-linear and Non-stationary Time Series Analysis Book in PDF, ePub and Kindle
Author | : Constantin Colonescu |
Publisher | : Lulu.com |
Total Pages | : 278 |
Release | : 2017-12-28 |
Genre | : Business & Economics |
ISBN | : 1387473611 |
Download Using R for Principles of Econometrics Book in PDF, ePub and Kindle
This is a beginner's guide to applied econometrics using the free statistics software R. It provides and explains R solutions to most of the examples in 'Principles of Econometrics' by Hill, Griffiths, and Lim, fourth edition. 'Using R for Principles of Econometrics' requires no previous knowledge in econometrics or R programming, but elementary notions of statistics are helpful.
Author | : Gebhard Kirchgässner |
Publisher | : Springer Science & Business Media |
Total Pages | : 277 |
Release | : 2007-08-17 |
Genre | : Business & Economics |
ISBN | : 3540732918 |
Download Introduction to Modern Time Series Analysis Book in PDF, ePub and Kindle
This book contains the most important approaches to analyze time series which may be stationary or nonstationary. It starts with modeling and forecasting univariate time series and then presents Granger causality tests and vector autoregressive models for multiple stationary time series. It also covers modeling volatilities of financial time series with autoregressive conditional heteroskedastic models.
Author | : S. Burke |
Publisher | : Springer |
Total Pages | : 253 |
Release | : 2005-06-14 |
Genre | : Business & Economics |
ISBN | : 0230005780 |
Download Modelling Non-Stationary Economic Time Series Book in PDF, ePub and Kindle
Co-integration, equilibrium and equilibrium correction are key concepts in modern applications of econometrics to real world problems. This book provides direction and guidance to the now vast literature facing students and graduate economists. Econometric theory is linked to practical issues such as how to identify equilibrium relationships, how to deal with structural breaks associated with regime changes and what to do when variables are of different orders of integration.