Diagnostic Checks In Time Series
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Author | : Wai Keung Li |
Publisher | : CRC Press |
Total Pages | : 276 |
Release | : 2003-12-29 |
Genre | : Mathematics |
ISBN | : 1135441154 |
Diagnostic checking is an important step in the modeling process. But while the literature on diagnostic checks is quite extensive and many texts on time series modeling are available, it still remains difficult to find a book that adequately covers methods for performing diagnostic checks. Diagnostic Checks in Time Series helps to fill that
Author | : Wai Keung Li |
Publisher | : CRC Press |
Total Pages | : 211 |
Release | : 2003-12-29 |
Genre | : Mathematics |
ISBN | : 0203485602 |
Diagnostic checking is an important step in the modeling process. But while the literature on diagnostic checks is quite extensive and many texts on time series modeling are available, it still remains difficult to find a book that adequately covers methods for performing diagnostic checks. Diagnostic Checks in Time Series helps to fill that
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 | : George E. P. Box |
Publisher | : |
Total Pages | : 616 |
Release | : 1976 |
Genre | : Mathematics |
ISBN | : |
Introduction and summary; Stochastic models and their forecasting; The autocorrelation function and spectrum; Linear stationary models; Linear nonstationary models; Forecasting; Stochastic model building; Model identification; Model estimation; Model diagnostic checking; Seasonal models; Transfer function models; Identification fitting, and checking of transfer function models.
Author | : Fumiya Akashi |
Publisher | : Springer Nature |
Total Pages | : 117 |
Release | : 2021-06-08 |
Genre | : Mathematics |
ISBN | : 9811622647 |
This book contains new aspects of model diagnostics in time series analysis, including variable selection problems and higher-order asymptotics of tests. This is the first book to cover systematic approaches and widely applicable results for nonstandard models including infinite variance processes. The book begins by introducing a unified view of a portmanteau-type test based on a likelihood ratio test, useful to test general parametric hypotheses inherent in statistical models. The conditions for the limit distribution of portmanteau-type tests to be asymptotically pivotal are given under general settings, and very clear implications for the relationships between the parameter of interest and the nuisance parameter are elucidated in terms of Fisher-information matrices. A robust testing procedure against heavy-tailed time series models is also constructed in the context of variable selection problems. The setting is very reasonable in the context of financial data analysis and econometrics, and the result is applicable to causality tests of heavy-tailed time series models. In the last two sections, Bartlett-type adjustments for a class of test statistics are discussed when the parameter of interest is on the boundary of the parameter space. A nonlinear adjustment procedure is proposed for a broad range of test statistics including the likelihood ratio, Wald and score statistics.
Author | : Walter Vandaele |
Publisher | : |
Total Pages | : 440 |
Release | : 1983 |
Genre | : Business & Economics |
ISBN | : |
This text presents Time Series analysis and Box-Jenkins models.
Author | : Jacques J. F. Commandeur |
Publisher | : OUP Oxford |
Total Pages | : 192 |
Release | : 2007-07-19 |
Genre | : Business & Economics |
ISBN | : 0191607800 |
Providing a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. The only background required in order to understand the material presented in the book is a basic knowledge of classical linear regression models, of which a brief review is provided to refresh the reader's knowledge. Also, a few sections assume familiarity with matrix algebra, however, these sections may be skipped without losing the flow of the exposition. The book offers a step by step approach to the analysis of the salient features in time series such as the trend, seasonal, and irregular components. Practical problems such as forecasting and missing values are treated in some detail. This useful book will appeal to practitioners and researchers who use time series on a daily basis in areas such as the social sciences, quantitative history, biology and medicine. It also serves as an accompanying textbook for a basic time series course in econometrics and statistics, typically at an advanced undergraduate level or graduate level.
Author | : William W. S. Wei |
Publisher | : Pearson |
Total Pages | : 648 |
Release | : 2018-03-14 |
Genre | : Time-series analysis |
ISBN | : 9780134995366 |
With its broad coverage of methodology, this comprehensive book is a useful learning and reference tool for those in applied sciences where analysis and research of time series is useful. Its plentiful examples show the operational details and purpose of a variety of univariate and multivariate time series methods. Numerous figures, tables and real-life time series data sets illustrate the models and methods useful for analyzing, modeling, and forecasting data collected sequentially in time. The text also offers a balanced treatment between theory and applications. Time Series Analysis is a thorough introduction to both time-domain and frequency-domain analyses of univariate and multivariate time series methods, with coverage of the most recently developed techniques in the field.
Author | : William W. S. Wei |
Publisher | : Addison-Wesley Longman |
Total Pages | : 648 |
Release | : 2006 |
Genre | : Mathematics |
ISBN | : |
With its broad coverage of methodology, this comprehensive book is a useful learning and reference tool for those in applied sciences where analysis and research of time series is useful. Its plentiful examples show the operational details and purpose of a variety of univariate and multivariate time series methods. Numerous figures, tables and real-life time series data sets illustrate the models and methods useful for analyzing, modeling, and forecasting data collected sequentially in time. The text also offers a balanced treatment between theory and applications. Overview. Fundamental Concepts. Stationary Time Series Models. Nonstationary Time Series Models. Forecasting. Model Identification. Parameter Estimation, Diagnostic Checking, and Model Selection. Seasonal Time Series Models. Testing for a Unit Root. Intervention Analysis and Outlier Detection. Fourier Analysis. Spectral Theory of Stationary Processes. Estimation of the Spectrum. Transfer Function Models. Time Series Regression and GARCH Models. Vector Time Series Models. More on Vector Time Series. State Space Models and the Kalman Filter. Long Memory and Nonlinear Processes. Aggregation and Systematic Sampling in Time Series. For all readers interested in time series analysis.
Author | : Marcelo Medeiros |
Publisher | : |
Total Pages | : 24 |
Release | : 2000 |
Genre | : |
ISBN | : |