Forecasting and Time Series An Applied

(Thomson Learning) Text illustrating the process of forecasting and the statistical techniques that can be used to produce forecasts. Hallmark features include comprehensive treatment of Box-Jenkins methodology and exercise sets for realistic practice. Previous edition: c1993. Softcover. DLC: Time series analysis.

PART I INTRODUCTION

1

(75)

An Introduction to Forecasting

2

(24)

Introduction

2

(1)

Forecasting and Time Series

3

(5)

Forecasting Methods

8

(4)

Errors in Forecasting

12

(5)

Choosing a Forecasting Techniques

17

(2)

An Overview of Quantitative Forecasting Techniques

19

(4)

Computer Packages: Minitab and SAS

23

(3)

Exercises

23

(3)

Basic Statistical Concepts

26

(50)

Populations

27

(2)

Probability

29

(2)

Random Samples and Sample Statistics

31

(3)

Continuous Probability Distributions

34

(2)

The Normal Probability Distribution

36

(9)

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

45

(3)

Confidence Intervals for a Population Mean

48

(10)

Hypothesis Testing for a Population Mean

58

(18)

Exercises

72

(4)

PART II FORECASTING BY USING REGRESSION ANALYSIS

76

(213)

Simple Linear Regression

77

(54)

The Simple Linear Regression Model

78

(8)

The Least Squares Point Estimates

86

(4)

Point Estimates and Point Predictions

90

(3)

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

93

(4)

Testing the Significance of the Independent Variable

97

(7)

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

104

(8)

Simple Coefficients of Determination and Correlation

112

(6)

An F-Test for the Simple Linear Regression Model

118

(3)

Using the Computer

121

(10)

Exercises

122

(9)

Multiple Regression

131

(83)

The Linear Regression Model

132

(12)

The Least Squares Point Estimates

144

(5)

Point Estimates and Point Predictions

149

(4)

The Regression Assumptions and the Standard Error

153

(3)

Multiple Coefficients of Determination and Correlation

156

(3)

An F-Test for the Overall Model

159

(2)

Statistical Inference for βj and Multicollinerity

161

(5)

Confidence Intervals and Prediction Intervals

166

(6)

An Introduction to Model Building

172

(7)

Residual Analysis

179

(19)

Using the Computer

198

(16)

Exercises

200

(14)

Topics in Regression Analysis

214

(75)

Interaction

215

(11)

An F-Test for a Portion of a Model

226

(4)

Using Dummy Variables to Model Qualitative Independent Variables

230

(10)

Advanced Concepts of Multicollinearity

240

(8)

Advanced Model Comparison Methods

248

(7)

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

255

(5)

Outlying and Influential Observations

260

(6)

Handling Unequal Variances

266

(4)

Using the Computer

270

(19)

Exercises

273

(16)

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

289

(146)

Time Series Regression

290

(64)

Modeling Trend by Using Polynomial Functions

291

(10)

Detecting Autocorrelation

301

(7)

Types of Seasonal Variation

308

(8)

Modeling Seasonal Variation by Using Dummy Variables and Trigonometric Functions

316

(9)

Growth Curve Models

325

(5)

Handling First-Order Autocorrelation

330

(7)

Using the Computer

337

(17)

Exercises

342

(12)

Decomposition Methods

354

(25)

Multiplicative Decomposition

355

(13)

Additive Decomposition

368

(2)

Shifting Seasonal Patterns

370

(3)

The Census II Decomposition Method and SAS PROC X11

373

(1)

Using the Computer

374

(5)

Exercises

375

(4)

Exponential Smoothing

379

(56)

Simple Exponential Smoothing

380

(6)

Adaptive Control Procedures

386

(3)

Double Exponential Smoothing

389

(14)

Winters' Method

403

(18)

Exponential and Damped Trends

421

(6)

Prediction Intervals

427

(3)

Concluding Comments

430

(1)

Using the Computer

431

(4)

Exercises

431

(4)

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

435

(131)

Nonseasonal Box-Jenkins Models and Their Tentative Identification

436

(51)

Stationary and Nonstationary Time Series

437

(4)

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

441

(16)

An Introduction to Nonseasonal Modeling and Forecasting

457

(10)

Tentative Identification of Nonseasonal Box-Jenkins Models

467

(10)

Using the Computer

477

(10)

Exercises

478

(9)

Estimation, Diagnostic Checking, and Forecasting For Nonseasonal Box-Jenkins Models

487

(34)

Estimation

488

(8)

Diagnostic Checking

496

(6)

Forecasting

502

(2)

A Case Study

504

(8)

Using the Computer

512

(9)

Exercises

514

(7)

An Introduction to Box-Jenkins Seasonal Modeling

521

(45)

Transforming a Seasonal Time Series into a Stationary Time Series

521

(12)

Two Examples of Seasonal Modeling and Forecasting

533

(17)

Using the Computer

550

(16)

Exercises

552

(14)

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

566

(140)

General Box-Jenkins Seasonal Modeling

567

(39)

The General Seasonal Model and Guidelines for Tentative Identification

568

(13)

Improving an Inadequate Seasonal Model

581

(14)

Using the Computer

595

(1)

Using the Computer

595

(11)

Exercises

596

(10)

Using the Box-Jenkins Methodology to Improve Time Series Regression Models and to Implement Exponential Smoothing

606

(51)

Box-Jenkins Error Term Models in Time Series Regression

607

(11)

Seasonal Intervention Models

618

(7)

Box-Jenkins Implementation of Exponential Smoothing

625

(14)

Using the Computer

639

(18)

Exercises

643

(14)

Transfer Functions and Intervention Models

657

(49)

A Three-Step Procedure for Building a Transfer Function Model

658

(19)

Intervention Models

677

(12)

Using the Computer

689

(17)

Exercises

694

(12)

Appendix A Statistical Tables

706

(10)

Appendix B References

716

(3)

Index

719

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Forecasting and Time Series An Applied Approach

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