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Many analyses of time series data involve multiple, related variablesá Multiple Time Series Models presents many specification choices and special challengesá This book reviews the main competing approaches tmodeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregressionááThe text focuses on vector autoregression (VAR) models as a generalization of the other approaches mentionedá Specification, estimation, and inference using these modelsáis discussedá The authors alsreview arguments for and against using multi-equation time series models Twcomplete, 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 availableKey FeaturesOffers a detailed comparison of different time series methods and approaches Includes a self-contained introduction tvector autoregression modeling Situates multiple time series modeling as a natural extension of commonly taught statistical models
