Forecasting with Dynamic Regression Models: Pankratz/Forecasting

One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.Content: Chapter 1 Introduction and Overview (pages 1–23): Chapter 2 A Primer on ARIMA Models (pages 24–81): Chapter 3 A Primer on Regression Models (pages 82–146): Chapter 4 Rational Distributed Lag Models (pages 147–166): Chapter 5 Building Dynamic Regression Models: Model Identification (pages 167–201): Chapter 6 Building Dynamic Regression Models: Model Checking, Reformulation and Evaluation (pages 202–252): Chapter 7 Intervention Analysis (pages 253–289): Chapter 8 Intervention and Outlier Detection and Treatment (pages 290–323): Chapter 9 Estimation and Forecasting (pages 324–341): Chapter 10 Dynamic Regression Models in a Vector ARMA Framework (pages 342–356):

One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.