预测与时间序列 第3版PDF电子书下载

PART ⅠINTRODUCTION 1

CHAPTER 1AN INTRODUCTION TO FORECASTING 2

1.1 Introduction 2

1.2 Forecasting and Time Series 3

1.3 Forecasting Methods 8

1.4 Errors in Forecasting 12

1.5 Choosing a Forecasting Technique 17

1.6 An Overview of Quantitative Forecasting Techniques 19

1.7 Computer Packages:Minitab and SAS 23

Exercises 23

CHAPTER 2 BASIC STATISTICAL CONCEPTS 26

2.1 Populations 27

2.2 Probability 29

2.3 Random Samples and Sample Statistics 31

2.4 Continuous Probability Distributions 34

2.5 The Normal Probability Distribution 36

2.6 The t-Distribution,the F-Distribution,and the Chi-Square Distribution 45

2.7 Confidence Intervals for a Population Mean 48

2.8 Hypothesis Testing for a Population Mean 58

Exercises 72

PART Ⅱ FORECASTING BY USING REGRESSION ANALYSIS 76

CHAPTER 3 SIMPLE LINEAR REGRESSION 77

3.1 The Simple Linear Regression Model 78

3.2 The Least Squares Point Estimates 86

3.3 Point Estimates and Point Predictions 90

3.4 Model Assumptions,the Mean Square Error,and the Standard Error 93

3.5 Testing the Significance of the Independent Variable 97

3.6 A Confidence Interval for a Mean Value of the Dependent Variable and a Prediction Interval for an Individual Value of the Dependent Variable 104

3.7 Simple Coefficients of Determination and Correlation 112

3.8 An F-Test for the Simple Linear Regression Model 118

3.9 Using the Computer 121

Exercises 122

CHAPTER 4 MULTIPLE REGRESSION 131

4.1 The Linear Regression Model 132

4.2 The Least Squares Point Estimates 144

4.3 Point Estimates and Point Predictions 149

4.4 The Regression Assumptions and the Standard Error 153

4.5 Multiple Coefficients of Determination and Correlation 156

4.6 An F-Test for the Overall Model 159

4.7 Statistical Inference for βj and Multicollinearity 161

4.8 Confidence Intervals and Prediction Intervals 166

4.9 An Introduction to Model Building 172

4.10 Residual Analysis 179

4.11 Using the Computer 198

Exercises 200

CHAPTER 5 TOPICS IN REGRESSION ANALYSIS 214

5.1 Interaction 215

5.2 An F-Test for a Portion of a Model 226

5.3 Using Dummy Variables to Model Qualitative Independent Variables 230

5.4 Advanced Concepts of Multicollinearity 240

5.5 Advanced Model Comparison Methods 248

5.6 Stepwise Regression,Forward Selection,Backward Elimination,and Maximum R2 Improvement 255

5.7 Outlying and Influential Observations 260

5.8 Handling Unequal Variances 266

5.9 Using the Computer 270

Exercises 273

PART Ⅲ FORECASTING BY USING TIME SERIES REGRESSION,DECOMPOSITION METHODS,AND EXPONENTIAL SMOOTHING 289

CHAPTER 6 TIME SERIES REGRESSION 290

6.1 Modeling Trend by Using Polynomial Functions 291

6.2 Detecting Autocorrelation 301

6.3 Types of Seasonal Variation 308

6.4 Modeling Seasonal Variation by Using Dummy Variables and Trigonometric Functions 316

6.5 Growth Curve Models 325

6.6 Handling First-Order Autocorrelation 330

6.7 Using the Computer 337

Exercises 342

CHAPTER 7 DECOMPOSITION METHODS 354

7.1 Multiplicative Decomposition 355

7.2 Additive Decomposition 368

7.3 Shifting Seasonal Patterns 370

7.4 The Census II Decomposition Method and SAS PROC X11 373

7.5 Using the Computer 374

Exercises 375

CHAPTER 8 Exponential Smoothing 379

8.1 Simple Exponential Smoothing 380

8.2 Adaptive Control Procedures 386

8.3 Double Exponential Smoothing 389

8.4 Winters'Method 403

8.5 Exponential and Damped Trends 421

8.6 Prediction Intervals 427

8.7 Concluding Comments 430

8.8 Using the Computer 431

Exercises 431

PART Ⅳ FORECASTING BY USING BASIC TECHNIQUES OF THE BOX-JENKINS METHODOLOGY 435

CHAPTER 9 NONSEASONAL BOX-JENKINS MODELS AND THEIR TENTATIVE IDENTIFICATION 436

9.1 Stationary and Nonstationary Time Series 437

9.2 The Sample Autocorrelation and Partial Autocorrelation Functions:The SAC and SPAC 441

9.3 An Introduction to Nonseasonal Modeling and Forecasting 457

9.4 Tentative Identification of Nonseasonal Box-Jenkins Models 467

9.5 Using the Computer 477

Exercises 478

CHAPTER 10 ESTIMATION,DIAGNOSTIC CHECKING,AND FORECASTING FOR NONSEASONAL BOX-JENKINS MODELS 487

10.1 Estimation 488

10.2 Diagnostic Checking 496

10.3 Forecasting 502

10.4 A Case Study 504

10.5 Using the Computer 512

Exercises 514

CHAPTER 11 AN INTRODUCTION TO BOX-JENKINS SEASONAL MODELING 521

11.1 Transforming a Seasonal Time Series into a Stationary Time Series 521

11.2 Two Examples of Seasonal Modeling and Forecasting 533

11.3 Using the Computer 550

Exercises 552

PART Ⅴ FORECASTING BY USING ADVANCED TECHNIQUES OF THE BOX-JENKINS METHODOLOGY 566

CHAPTER 12 GENERAL BOX-JENKINS SEASONAL MODELING 567

12.1 The General Seasonal Model and Guidelines for Tentative Identification 568

12.2 Improving an Inadequate Seasonal Model 581

12.3 Using the Computer 595

Exercises 596

CHAPTER 13 USING THE BOX-JENKINS METHODOLOGY TO IMPROVE TIME SERIES REGRESSION MODELS AND TO IMPLEMENT EXPONENTIAL SMOOTHING 606

13.1 Box-Jenkins Error Term Models in Time Series Regression 607

13.2 Seasonal Intervention Models 618

13.3 Box-Jenkins Implementation of Exponential Smoothing 625

13.4 Using the Computer 639

Exercises 643

CHAPTER 14 TRANSFER FUNCTIONS AND INTERVENTION MODELS 657

14.1 A Three-Step Procedure for Building a Transfer Function Model 658

14.2 Intervention Models 677

14.3 Using the Computer 689

Exercises 694

APPENDIX A STATISTICAL TABLES 706

APPENDIX B REFERENCES 716

Index 719