forecasting with dynamic regression models(pankratz)ARIMA模型推广 超经典案例丰富

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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

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关键词：Forecasting regression ARIMA模型推广 regressio Forecast dynamic regression ARIMA模型推广 案例丰富

学习一下                                                                        
                                       
                                                     
                                                     
                                             
                                                                 
                                                                                 

This book may be used as a main text for undergraduate and beginning
graduate courses in applied time series and forecasting in areas such as
economics, business, biology, political science, engineering, statistics, and
decision science. It may be used in advanced courses to supplement theoretical
readings with applications. And it can serve as a guide to the construction
and use of regression forecasting models for practicing forecasters in business
and government.

Special features of this book include the following:
• Many step-by-step examples using real data, including cases with multiple
inputs, both stochastic and deterministic.
• Emphasis on model interpretation.
• Emphasis on a model identification method that is easily applied with
multiple inputs.
 Suggested practical rules for identifying rational form transfer functions.
• Emphasis on feedback checks as a preliminary modeling step.
• Careful development of an outlier and intervention detection method,
including a BASIC computing routine.
• A chapter linking dynamic regression models to vector ARMA models.
• Use of the extended autocorrelation function to identify ARIMA models.

有程序实现吗？

spss1010 发表于 2014-10-15 08:14
有程序实现吗？

SAS ETS模块或者proc arima可以解决相关程序问题

想问一下这个PDF是整本书的PDF吗

正在看，写的很易懂，不需要特别高深的数学知识。对于上班N年的业余时序分析爱好者是福音。案列很丰富，但是Case2的建模要用到1976年到1987年的Trading day做为变量，书上没有给出原数据，自己搞不出来啊，没法建模。:(

weihaixiaoseu 发表于 2014-10-4 12:23
This book may be used as a main text for undergraduate and beginning
graduate courses in applied ti ...

学习一下，感谢分享