Time Series Models
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Author | : Rob J Hyndman |
Publisher | : OTexts |
Total Pages | : 380 |
Release | : 2018-05-08 |
Genre | : Business & Economics |
ISBN | : 0987507117 |
Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Author | : Andrew C. Harvey |
Publisher | : Cambridge University Press |
Total Pages | : 574 |
Release | : 1990 |
Genre | : Business & Economics |
ISBN | : 9780521405737 |
A synthesis of concepts and materials, that ordinarily appear separately in time series and econometrics literature, presents a comprehensive review of theoretical and applied concepts in modeling economic and social time series.
Author | : Jeff B. Cromwell |
Publisher | : SAGE |
Total Pages | : 116 |
Release | : 1994 |
Genre | : Social sciences |
ISBN | : 9780803954403 |
Which time series test should researchers choose to best describe the interactions among a set of time series variables? Providing guidelines for identifying the appropriate multivariate time series model to use, this book explores the nature and application of these increasingly complex tests.
Author | : Philip Hans Franses |
Publisher | : Cambridge University Press |
Total Pages | : 421 |
Release | : 2014-04-24 |
Genre | : Business & Economics |
ISBN | : 1139952129 |
With a new author team contributing decades of practical experience, this fully updated and thoroughly classroom-tested second edition textbook prepares students and practitioners to create effective forecasting models and master the techniques of time series analysis. Taking a practical and example-driven approach, this textbook summarises the most critical decisions, techniques and steps involved in creating forecasting models for business and economics. Students are led through the process with an entirely new set of carefully developed theoretical and practical exercises. Chapters examine the key features of economic time series, univariate time series analysis, trends, seasonality, aberrant observations, conditional heteroskedasticity and ARCH models, non-linearity and multivariate time series, making this a complete practical guide. Downloadable datasets are available online.
Author | : Andrew C. Harvey |
Publisher | : Financial Times/Prentice Hall |
Total Pages | : 308 |
Release | : 1993 |
Genre | : Time-series analysis |
ISBN | : 9780745012001 |
A companion volume to The Econometric Analysis of Time series, this book focuses on the estimation, testing and specification of dynamic models which are not based on any behavioural theory. It covers univariate and multivariate time series and emphasizes autoregressive moving-average processes.
Author | : Patrick T. Brandt |
Publisher | : SAGE |
Total Pages | : 121 |
Release | : 2007 |
Genre | : Mathematics |
ISBN | : 1412906563 |
Many analyses of time series data involve multiple, related variables. Modeling Multiple Time Series presents many specification choices and special challenges. This book reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression. The text focuses on vector autoregression (VAR) models as a generalization of the other approaches mentioned. Specification, estimation, and inference using these models is discussed. The authors also review arguments for and against using multi-equation time series models. Two complete, worked examples show how VAR models can be employed. An appendix discusses software that can be used for multiple time series models and software code for replicating the examples is available. Key Features: * Offers a detailed comparison of different time series methods and approaches. * Includes a self-contained introduction to vector autoregression modeling. * Situates multiple time series modeling as a natural extension of commonly taught statistical models.
Author | : David Barber |
Publisher | : Cambridge University Press |
Total Pages | : 432 |
Release | : 2011-08-11 |
Genre | : Computers |
ISBN | : 0521196760 |
The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.
Author | : Raquel Prado |
Publisher | : CRC Press |
Total Pages | : 473 |
Release | : 2021-07-27 |
Genre | : Mathematics |
ISBN | : 1498747043 |
• Expanded on aspects of core model theory and methodology. • Multiple new examples and exercises. • Detailed development of dynamic factor models. • Updated discussion and connections with recent and current research frontiers.
Author | : H. Tong |
Publisher | : Springer Science & Business Media |
Total Pages | : 333 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 1468478885 |
In the last two years or so, I was most fortunate in being given opportunities of lecturing on a new methodology to a variety of audiences in Britain, China, Finland, France and Spain. Despite my almost Confucian attitude of preferring talking (i.e. a transient record) to writing (i.e. a permanent record), the warm encouragement of friends has led to the ensuing notes. I am also only too conscious of the infancy of the methodology introduced in these notes. However, it is my sincere hope that exposure to a wider audience will accelerate its maturity. Readers are assumed to be familiar with the basic theory of time series analysis. The book by Professor M.B. Priestley (1981) may be used as a general reference. Chapter One is addressed to the general question: "why do we need non-linear time series models?" After describing some significant advantages of linear models, it singles out several major limitations of linearity. Of course, the selection reflects my personal view on the subject, which is only at its very beginning, although there does seem to be a general agreement in the literature that time irr'eversibility and limit cycles are among the most obvious.
Author | : Christian Gourieroux |
Publisher | : Cambridge University Press |
Total Pages | : 692 |
Release | : 1997 |
Genre | : Business & Economics |
ISBN | : 9780521411462 |
In this book Christian Gourieroux and Alain Monfort provide an up-to-date and comprehensive analysis of modern time series econometrics. They have succeeded in synthesising in an organised and integrated way a broad and diverse literature. While the book does not assume a deep knowledge of economics, one of its most attractive features is the close attention it pays to economic models and phenomena throughout. The coverage represents a major reference tool for graduate students, researchers and applied economists. The book is divided into four sections. Section one gives a detailed treatment of classical seasonal adjustment or smoothing methods. Section two provides a thorough coverage of various mathematical tools. Section three is the heart of the book, and is devoted to a range of important topics including causality, exogeneity shocks, multipliers, cointegration and fractionally integrated models. The final section describes the main contribution of filtering and smoothing theory to time series econometric problems.