Applied Time Series Analysis for the Social Sciences: Specification, Estimation, and Inference

Based on a successful ICPSR course, this book aims to present time series analysis to student and practitioners from a diverse set of backgrounds. The Author assumes minimum mathematical background in order to provide an accessible and comprehensive approach to both the theory and practice of time series analysis. A wide range of topics are covered, including ARIMA probability models, Estimation and forecasting techniques, OLS and the Gauss-Markov assumptions, Intervention models and addresses newer methodologies such as GLS and ADL models, Vector Autoregression and Error correction models. It also introduces Pooled cross-section time series models and ARCH and GARCH models.

The book is designed to break difficult concepts into manageable pieces whilst providing extensive case studies and exercises. It uses Lag operator algebra throughout the book to provide better understanding of applied time series analysis.