Download or read book Analysis of Integrated and Cointegrated Time Series with R written by Bernhard Pfaff and published by Springer Science & Business Media. This book was released on 2008-09-03 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed for self study. The reader can apply the theoretical concepts directly within R by following the examples.
Download or read book Cointegrated Economic Time Series written by Robert F. Engle and published by . This book was released on 1990 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Cointegrated VAR Model written by Katarina Juselius and published by OUP Oxford. This book was released on 2006-12-07 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Time Series Econometrics written by Pierre Perron and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Part I. Unit roots and trend breaks -- Part II. Structural change
Download or read book Forecasting Economic Time Series written by Michael Clements and published by Cambridge University Press. This book was released on 1998-10-08 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a formal analysis of the models, procedures, and measures of economic forecasting with a view to improving forecasting practice. David Hendry and Michael Clements base the analyses on assumptions pertinent to the economies to be forecast, viz. a non-constant, evolving economic system, and econometric models whose form and structure are unknown a priori. The authors find that conclusions which can be established formally for constant-parameter stationary processes and correctly-specified models often do not hold when unrealistic assumptions are relaxed. Despite the difficulty of proceeding formally when models are mis-specified in unknown ways for non-stationary processes that are subject to structural breaks, Hendry and Clements show that significant insights can be gleaned. For example, a formal taxonomy of forecasting errors can be developed, the role of causal information clarified, intercept corrections re-established as a method for achieving robustness against forms of structural change, and measures of forecast accuracy re-interpreted.
Download or read book Time series based Econometrics written by Michio Hatanaka and published by Oxford University Press. This book was released on 1996 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last decade, time-series econometrics has made extraordinary developments on unit roots and cointegration. However, this progress has taken divergent directions, and has been subjected to criticism from outside the field. In this book, Professor Hatanaka surveys the field, examinesthose portions that are useful for macroeconomics, and responds to the criticism. His survey of the literature covers not only econometric methods, but also the application of these methods to macroeconomic studies.The most vigorous criticism has been that unit roots to do not exist in macroeconomic variables, and thus that cointegration analysis is irrelevant to macroeconomics. The judgement of this book is that unit roots are present in macroeconomic variables when we consider periods of 20 to 40 years, butthat the critics may be right when periods of 100 years are considered. Fortunately, most of the time series data used for macroeconomic studies cover fall within the shorter time span.Among the numerous methods for unit roots and cointegration, those useful from macroeconomic studies are examined and explained in detail, without overburdening the reader with unnecessary mathematics. Other, less applicable methods are dicussed briefly, and their weaknesses are exposed. Hatanakahas rigourously based his judgements about usefulness on whether the inference is appropriate for the length of the data sets available, and also on whether a proper inference can be made on the sort of propositions that macroeconomists wish to test.This book highlights the relations between cointegration and economic theories, and presents cointegrated regression as a revolution in econometric methods. Its analysis is of relevance to academic and professional or applied econometricians. Step-by-step explanations of concepts and techniquesmake the book a self-contained text for graduate students.
Download or read book Workbook on Cointegration written by Peter Reinhard Hansen and published by Oxford University Press, USA. This book was released on 1998 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Likelihood based Inference in Cointegrated Vector Autoregressive Models written by Søren Johansen and published by Oxford University Press, USA. This book was released on 1995 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Introduction to Multiple Time Series Analysis written by Helmut Lütkepohl and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Unit Roots Cointegration and Structural Change written by G. S. Maddala and published by Cambridge University Press. This book was released on 1998 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive review of unit roots, cointegration and structural change from a best-selling author.
Download or read book State Space Modeling of Time Series written by Masanao Aoki and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: model's predictive capability? These are some of the questions that need to be answered in proposing any time series model construction method. This book addresses these questions in Part II. Briefly, the covariance matrices between past data and future realizations of time series are used to build a matrix called the Hankel matrix. Information needed for constructing models is extracted from the Hankel matrix. For example, its numerically determined rank will be the di mension of the state model. Thus the model dimension is determined by the data, after balancing several sources of error for such model construction. The covariance matrix of the model forecasting error vector is determined by solving a certain matrix Riccati equation. This matrix is also the covariance matrix of the innovation process which drives the model in generating model forecasts. In these model construction steps, a particular model representation, here referred to as balanced, is used extensively. This mode of model representation facilitates error analysis, such as assessing the error of using a lower dimensional model than that indicated by the rank of the Hankel matrix. The well-known Akaike's canonical correlation method for model construc tion is similar to the one used in this book. There are some important differ ences, however. Akaike uses the normalized Hankel matrix to extract canonical vectors, while the method used in this book does not normalize the Hankel ma trix.
Download or read book Modelling Non Stationary Economic Time Series written by S. Burke and published by Springer. This book was released on 2005-06-14 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book The Econometric Analysis of Seasonal Time Series written by Eric Ghysels and published by Cambridge University Press. This book was released on 2001-06-18 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Causality in Time Series Challenges in Machine Learning written by Florin Popescu and published by . This book was released on 2013-06 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume in the Challenges in Machine Learning series gathers papers from the Mini Symposium on Causality in Time Series, which was part of the Neural Information Processing Systems (NIPS) confernce in 2009 in Vancouver, Canada. These papers present state-of-the-art research in time-series causality to the machine learning community, unifying methodological interests in the various communities that require such inference.
Download or read book Long run economic relationships written by Clive William John Granger and published by . This book was released on 1991 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Applied Economic Forecasting Using Time Series Methods written by Eric Ghysels and published by Oxford University Press. This book was released on 2018 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: Economic forecasting is a key ingredient of decision making in the public and private sectors. This book provides the necessary tools to solve real-world forecasting problems using time-series methods. It targets undergraduate and graduate students as well as researchers in public and private institutions interested in applied economic forecasting.
Download or read book Time Series Analysis for the Social Sciences written by Janet M. Box-Steffensmeier and published by Cambridge University Press. This book was released on 2014-12-22 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Time series, or longitudinal, data are ubiquitous in the social sciences. Unfortunately, analysts often treat the time series properties of their data as a nuisance rather than a substantively meaningful dynamic process to be modeled and interpreted. Time Series Analysis for the Social Sciences provides accessible, up-to-date instruction and examples of the core methods in time series econometrics. Janet M. Box-Steffensmeier, John R. Freeman, Jon C. Pevehouse and Matthew P. Hitt cover a wide range of topics including ARIMA models, time series regression, unit-root diagnosis, vector autoregressive models, error-correction models, intervention models, fractional integration, ARCH models, structural breaks, and forecasting. This book is aimed at researchers and graduate students who have taken at least one course in multivariate regression. Examples are drawn from several areas of social science, including political behavior, elections, international conflict, criminology, and comparative political economy.
