Springer New Introduction to Multiple Time Series Analysis

Master complex data forecasting with this comprehensive reference work and graduate-level textbook from Springer. This text provides a deep dive into the models and methods required for analyzing and forecasting multiple time series, making it an essential resource for students and researchers in applied mathematics. The content covers a broad spectrum of essential models, including vector autoregressive, cointegrated, and vector autoregressive moving average models. Readers will also gain expertise in multivariate ARCH, periodic processes, dynamic simultaneous equations, and state space models. To ensure a complete understanding of model estimation, the book explores least squares, maximum likelihood, and Bayesian methods. Beyond estimation, this work provides practical guidance on model selection and specification procedures. You will find a wide range of tests and criteria used for effective model checking, helping you ensure accuracy in your statistical analysis and forecasting results. This book serves as a foundational tool for anyone working with multivariate data structures.