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Multivariate Tests for Time Series Models de Jeffrey B. Cromwell

Multivariate Tests for Time Series Models: Quantitative Applications in the Social Sciences, cartea 100

Autor Jeffrey B. Cromwell, Walter C. Labys, Michael J. Hannan, Michel Terraza
en Limba Engleză Paperback – 15 aug 1994
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. In addition, it covers such topics as: joint stationarity; testing for cointegration; testing for causality; and model order and forecast accuracy. Related models explained include transfer function, vector autoregression and error correction models.
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Specificații

ISBN-13: 9780803954403
ISBN-10: 0803954409
Pagini: 104
Ilustrații: black & white illustrations
Dimensiuni: 140 x 216 x 7 mm
Greutate: 0.13 kg
Ediția:1
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Quantitative Applications in the Social Sciences

Locul publicării:Thousand Oaks, United States

Cuprins

Introduction
Testing for Joint Stationarity, Normality and Independence
Testing for Cointegration
Testing for Causality
Multivariate Linear Model Specification
Multivariate Nonlinear Specification
Model Order and Forecast Accuracy
Computational Methods for Performing the Tests

Notă biografică

Dr. Jeff B. Cromwell is a graduate of West Virginia University with research interests in computational statistics, econometrics and time series analysis.

Descriere

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. In addition, it covers such topics as: joint stationarity; testing for cointegration; testing for causality; and model order and forecast accuracy. Related models explained include transfer function, vector autoregression and error correction models.