Likelihood-based Inference in Cointegrated Vector Autoregressive Models PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Likelihood-based Inference in Cointegrated Vector Autoregressive Models PDF full book. Access full book title Likelihood-based Inference in Cointegrated Vector Autoregressive Models by Søren Johansen. Download full books in PDF and EPUB format.
Author: Søren Johansen Publisher: Oxford University Press, USA ISBN: 0198774508 Category : Business & Economics Languages : en Pages : 280
Book Description
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.
Author: Søren Johansen Publisher: Oxford University Press, USA ISBN: 0198774508 Category : Business & Economics Languages : en Pages : 280
Book Description
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.
Author: Peter Reinhard Hansen Publisher: Oxford University Press, USA ISBN: 9780198776086 Category : Business & Economics Languages : en Pages : 178
Book Description
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.
Author: Katarina Juselius Publisher: OUP Oxford ISBN: 0191622966 Category : Business & Economics Languages : en Pages : 478
Book Description
This valuable text provides a comprehensive introduction to VAR modelling and how it can be applied. In particular, the author focuses on the properties of the Cointegrated VAR model and its implications for macroeconomic inference when data are non-stationary. The text provides a number of insights into the links between statistical econometric modelling and economic theory and gives a thorough treatment of identification of the long-run and short-run structure as well as of the common stochastic trends and the impulse response functions, providing in each case illustrations of applicability. This book presents the main ingredients of the Copenhagen School of Time-Series Econometrics in a transparent and coherent framework. The distinguishing feature of this school is that econometric theory and applications have been developed in close cooperation. The guiding principle is that good econometric work should take econometrics, institutions, and economics seriously. The author uses a single data set throughout most of the book to guide the reader through the econometric theory while also revealing the full implications for the underlying economic model. To test ensure full understanding the book concludes with the introduction of two new data sets to combine readers understanding of econometric theory and economic models, with economic reality.
Author: Helmut Lütkepohl Publisher: Cambridge University Press ISBN: 1139454730 Category : Business & Economics Languages : en Pages : 351
Book Description
Time series econometrics is a rapidly evolving field. Particularly, the cointegration revolution has had a substantial impact on applied analysis. Hence, no textbook has managed to 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, reminding the reader of the ideas underlying them and giving sufficient background for empirical work. The treatment can also be used as a textbook for a course on applied time series econometrics. Topics include: unit root and cointegration analysis, structural vector autoregressions, conditional heteroskedasticity and nonlinear and nonparametric time series models. Crucial to empirical work is the software that is available for analysis. New methodology is typically only gradually incorporated into existing software packages. Therefore a flexible Java interface has been created, allowing readers to replicate the applications and conduct their own analyses.
Author: Lutz Kilian Publisher: Cambridge University Press ISBN: 1107196574 Category : Business & Economics Languages : en Pages : 757
Book Description
This book discusses the econometric foundations of structural vector autoregressive modeling, as used in empirical macroeconomics, finance, and related fields.
Author: Constantin Colonescu Publisher: Lulu.com ISBN: 1387473611 Category : Business & Economics Languages : en Pages : 278
Book Description
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: Alessandra Canepa Publisher: LAP Lambert Academic Publishing ISBN: 9783838314693 Category : Languages : en Pages : 172
Book Description
Obtaining reliable inference procedures is one of the main challenges of econometric research. Test statistics are usually based on applications of the central limit theorem. However, in order to work well the first order asymptotic approximation requires that the asymptotic distribution is an accurate approximation to the finite sample distribution. When dealing with time series models, this is not generally the case. In this book we investigate the small sample performance of various bootstrap based inference procedures when applied to vector autoregressive models. Special attention is given to Johansen s maximum likelihood method for conducting inference on cointegrated VAR models. Throughout the book, empirical applications are provided to illustrate the bootstrap method and its applications. The analysis should provide some guidance to practitioners in doubt about which inference procedure to use when dealing with cointegrated VAR models.