Author: Artur C.B. da Silva Lopes Publisher: ISBN: Category : Languages : en Pages : 14
Book Description
In this paper, it is demonstrated by simulation that, contrary to a widely held belief, pure seasonal mean shifts - i.e., seasonal structural breaks which affect only the deterministic seasonal cycle - really do matter for Dickey-Fuller long-run unit root tests.
Author: Artur C. B. Da Silva Lopes Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
In this paper, it is demonstrated by simulation that, contrary to a widely held belief, pure seasonal mean shifts i.e. seasonal structural breaks which affect only the seasonal cycle really do matter for Dickey-Fuller long-run unit root tests. Both size and power properties are affected by such breaks but using the t-sig method for lag selection induces a stabilizing effect. Although most results are reassuring when the t-sig method is used, some concern with this type of breaks cannot be disregarded. Further evidence on the poor performance of the t-sig method for quarterly time series in standard (no-break) cases is also presented.
Author: John C. Brocklebank, Ph.D. Publisher: SAS Institute ISBN: 1629605441 Category : Computers Languages : en Pages : 384
Book Description
To use statistical methods and SAS applications to forecast the future values of data taken over time, you need only follow this thoroughly updated classic on the subject. With this third edition of SAS for Forecasting Time Series, intermediate-to-advanced SAS users—such as statisticians, economists, and data scientists—can now match the most sophisticated forecasting methods to the most current SAS applications. Starting with fundamentals, this new edition presents methods for modeling both univariate and multivariate data taken over time. From the well-known ARIMA models to unobserved components, methods that span the range from simple to complex are discussed and illustrated. Many of the newer methods are variations on the basic ARIMA structures. Completely updated, this new edition includes fresh, interesting business situations and data sets, and new sections on these up-to-date statistical methods: ARIMA models Vector autoregressive models Exponential smoothing models Unobserved component and state-space models Seasonal adjustment Spectral analysis Focusing on application, this guide teaches a wide range of forecasting techniques by example. The examples provide the statistical underpinnings necessary to put the methods into practice. The following up-to-date SAS applications are covered in this edition: The ARIMA procedure The AUTOREG procedure The VARMAX procedure The ESM procedure The UCM and SSM procedures The X13 procedure The SPECTRA procedure SAS Forecast Studio Each SAS application is presented with explanation of its strengths, weaknesses, and best uses. Even users of automated forecasting systems will benefit from this knowledge of what is done and why. Moreover, the accompanying examples can serve as templates that you easily adjust to fit your specific forecasting needs. This book is part of the SAS Press program.
Author: Craig Pirrong Publisher: Cambridge University Press ISBN: 1139501976 Category : Business & Economics Languages : en Pages : 238
Book Description
Commodities have become an important component of many investors' portfolios and the focus of much political controversy over the past decade. This book utilizes structural models to provide a better understanding of how commodities' prices behave and what drives them. It exploits differences across commodities and examines a variety of predictions of the models to identify where they work and where they fail. The findings of the analysis are useful to scholars, traders and policy makers who want to better understand often puzzling - and extreme - movements in the prices of commodities from aluminium to oil to soybeans to zinc.
Author: Richard Harris Publisher: Wiley ISBN: 9780470844434 Category : Business & Economics Languages : en Pages : 316
Book Description
This book covers time series modeling and forecasting for econometrics and finance students. This new edition has been simplified for more ease of use and includes new chapters and substantial important revisions.
Author: Andrew Gelman Publisher: CRC Press ISBN: 1439840954 Category : Mathematics Languages : en Pages : 677
Book Description
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Author: Peter J. Brockwell Publisher: Springer Science & Business Media ISBN: 1475725264 Category : Mathematics Languages : en Pages : 429
Book Description
Some of the key mathematical results are stated without proof in order to make the underlying theory acccessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics. The emphasis is on methods and the analysis of data sets. The logic and tools of model-building for stationary and non-stationary time series are developed in detail and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introducitons are also given to cointegration and to non-linear, continuous-time and long-memory models. The time series package included in the back of the book is a slightly modified version of the package ITSM, published separately as ITSM for Windows, by Springer-Verlag, 1994. It does not handle such large data sets as ITSM for Windows, but like the latter, runs on IBM-PC compatible computers under either DOS or Windows (version 3.1 or later). The programs are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis.
Author: Artur C.B. da Silva Lopes
Author: Artur C. B. Da Silva Lopes
Author: G. S. Maddala
Author: 
Author: John C. Brocklebank, Ph.D.
Author: Robert H. Shumway
Author: Craig Pirrong
Author: Richard Harris
Author: Andrew Gelman
Author: Peter J. Brockwell