Summary
Contents
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.
Introduction
Introduction
Time series analysis has advanced from univariate modeling based on a single variable to multivariate models that employ the interrelationships between several such variables. Constructing such models requires the performing of tests to determine and to discover the interactions that exist between a given ...
