Introduction to Multiple Time Series Analysis

Die Prognose multipler Zeitreihen und die Analyse der Beziehung zwischen den beteiligten Variablen wird in diesem Buch behandelt. Zu diesem Zweck werden eine Reihe von Modellen und Methoden diskutiert. Die betrachteten Modelle schließen multivariate autoregressive, multivariate autoregressive Moving-Average-, cointegrierte und periodische Prozesse sowie Zustandsraum- und dynamische simultane Mehrgleichungsmodelle ein. Schätzung, Spezifikation und Überprüfung dieser Modelle werden diskutiert.

This graduate level textbook deals with analyzing and forecasting multiple time series. It considers a wide range of multiple time series models and methods. The models include vector autoregressive, vector autoregressive moving average, cointegrated, and periodic processes as well as state space and dynamic simultaneous equations models. Least squares, maximum likelihood, and Bayesian methods are considered for estimating these models. Different procedures for model selection or specification are treated and a range of tests and criteria for evaluating the adequacy of a chosen model are introduced. The choice of point and interval forecasts is considered and impulse response analysis, dynamic multipliers as well as innovation accounting are presented as tools for structural analysis within the multiple time series context. This book is accessible to graduate students in business and economics.

In addition, multiple time series courses in other fields such as statistics and engin

eering may be based on this book. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their task. It enables the reader to perform his or her analyses in a gap to the difficult technical literature on the topic.