The structure of the package vars and its implementation of vector autoregressive, structural vector autoregressive and structural vector error correction models are explained in this paper. In addition to the three cornerstone functions VAR(), SVAR() and SVEC() for estimating such models, functions for diagnostic testing, estimation of a restricted models, prediction, causality analysis, impulse response analysis and forecast error variance decomposition are provided too. It is further possible to convert vector error correction models into their level VAR representation. The different methods and functions are elucidated by employing a macroeconomic data set for Canada. However, the focus in this writing is on the implementation part rather than the usage of the tools at hand.
Чтобы скачать этот файл зарегистрируйтесь и/или войдите на сайт используя форму сверху.
Publisher: Wiley | 2010 | ISBN10: 0470583622 | 316 pages A new edition of the comprehensive, hands-on guide to financial time series, now featuring S-Plus and R software. Time Series: Applications to Finance with R and S-Plus, Second Edition is designed to present an in-depth introduction to the conceptual underpinnings and modern ideas of time series analysis. Utilizing...
Springer, 2009. — 262 p. — ISBN 978-0-387-88697-8 This book gives you a step-by-step introduction to analysing time series using the open source software R. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence confirms understanding of both the model and the R...
Second Edition. — Springer, 2008. — (501 + 305) p. — ISBN 0387759586. The book was developed for a one-semester course usually attended by students in statistics, economics, business, engineering, and quantitative social sciences. Basic applied statistics through multiple linear regression is assumed. Calculus is assumed only to the extent of minimizing sums of squares, but a...
Wiley – 2011, 296 pages ISBN: 0470669438 Financial Risk Forecasting is a complete introduction to practical quantitative risk management, with a focus on market risk. Derived from the authors teaching notes and years spent training practitioners in risk management techniques, it brings together the three key disciplines of finance, statistics and modeling (programming), to...
Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA...
2nd ed. — Springer, 2008. — 189 p. ISBN: 0387759662. Analysis of Integrated and Cointegrated Time Series with R (2nd Edition) by Bernhard Pfaff offers a rigorous introduction to unit roots and cointegration, along with numerous examples in R to illustrate the various methods. The book, now in its second edition, provides an overview of this active area of research in time...
