Taking a sequential approach to time-series model building, this easy-to-use and widely applicable book explores how to test for stationarity, normality, independence, linearity, model order, and properties of the residual process. The authors clearly define each testing procedure and offer examples to illustrate each concept. They also offer sound advice on how to perform the tests using different software packages.

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.