Preface
Chapter 1 Introduction and Overview
1.1 Related Time Series, 1
1.2 Overview: Dynamic Regression Models, 7
1.3 Box and Jenkins' Modeling Strategy, 15
1.4 Correlation, 17
1.5 Layout of the Book, 21
Questions and Problems, 22

Chapter 2 A Primer on ARIMA Models
2.1 Introduction, 24
2.2 Stationary Variance and Mean, 27
2.3 Autocorrelation, 34
2.4 Five Stationary ARIMA Processes, 39
2.5 ARIMA Modeling in Practice, 49
2.6 Backshift Notation, 52
2.7 Seasonal Models, 54
2.8 Combined Nonseasonal and Seasonal Processes, 57
2.9 Forecasting, 59
2.10 Extended Autocorrelation Function, 62
2.11 Interpreting ARIMA Model Forecasts, 64
Questions and Problems, 69

Case 1 Federal Government Receipts (ARIMA) 72
Chapter 3 A Primer on Regression Models 82
3.1 Two Types of Data, 82
3.2 The Population Regression Function (PRF) with One Input, 82
3.3 The Sample Regression Function (SRF) with One Input, 86
3.4 Properties of the Least-Squares Estimators, 88
3.5 Goodness of Fit (R2), 89
3.6 Statistical Inference, 92
3.7 Multiple Regression, 93
3.8 Selected Issues in Regression, 96
3.9 Application to Time Series Data, 103
Questions and Problems, 113

Case 2 Federal Government Receipts (Dynamic Regression) 115
Case 3 Kilowatt-Hours Used 131
Chapter 4 Rational Distributed Lag Models 147
4.1 Linear Distributed Lag Transfer Functions, 148
4.2 A Special Case: The Koyck Model, 150
4.3 Rational Distributed Lags, 156
4.4 The Complete Rational Form DR Model and Some
Special Cases, 163
Questions and Problems, 165

Chapter 5 Building Dynamic Regression Models: Model Identification 167
5.1 Overview, 167
5.2 Preliminary Modeling Steps, 168
5.3 The Linear Transfer Function (LTF) Identification Method, 173
5.4 Rules for Identifying Rational Distributed Lag Transfer
Functions, 184
Questions and Problems, 193
Appendix 5A The Corner Table, 194
Appendix 5B Transfer Function Identification Using Prewhitening
and Cross Correlations, 197

Chapter 6 Building Dynamic Regression Models: Model Checking,
Reformulation, and Evaluation 202
6.1 Diagnostic Checking and Model Reformulation, 202
6.2 Evaluating Estimation Stage Results, 209
Questions and Problems, 215
Case 4 Housing Starts and Sales 217
Case 5 Industrial Production, Stock Prices, and Vendor Performance 232
Chapter 7 Intervention Analysis 253
7.1 Introduction, 253
7.2 Pulse Interventions, 254
7.3 Step Interventions, 259
7.4 Building Intervention Models, 264
7.5 Multiple and Compound Interventions, 272
Questions and Problems, 276

Case 6 Year-End Loading 279
Chapter 8 Intervention and Outlier Detection and Treatment 290
8.1 The Rationale for Intervention and Outlier Detection, 291
8.2 Models for Intervention and Outlier Detection, 292
8.3 Likelihood Ratio Criteria, 299
8.4 An Iterative Detection Procedure, 313
8.5 Application, 315
8.6 Detected Events Near the End of a Series, 319
Questions and Problems, 320
Appendix 8A BASIC Program to Detect AO, LS, and IO
Events, 321
Appendix 8B Specifying IO Events in the SCA System, 322
Chapter 9 Estimation and Forecasting 324
9.1 DR Model Estimation, 324
9.2 Forecasting, 328
Questions and Problems, 340
Appendix 9A A BASIC Routine for Computing the Nonbiasing Factor in (9.2.24), 340

Chapter 10 Dynamic Regression Models in a Vector ARMA
Framework 342
10.1 Vector ARMA Processes, 342
10.2 The Vector AR (IT Weight) Form, 345
10.3 DR Models in VAR Form, 346
10.4 Feedback Check, 349
10.5 Check for Contemporaneous Relationship and
Dead Time, 354
Questions and Problems, 356
Appendix 357
Table A Student's / Distribution, 357
Table B x2 Critical Points, 359
Table C F Critical Points, 360