Methods for Business Analysis and Forecasting Text and Cases

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ISBN: 9780471123842 | 0471123846
Cover: Hardcover
Copyright: 12/7/2005

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Focusing on the principal methods for analysis and forecasting, the text is designed to fit two types of course design, the traditional approach for a technical course, or the approach frequently used in business courses. Examples, exercises, problems, and small and large cases are provided to fit into either or both approaches. An instructor's manual is available which includes solutions, author's notes of each case and possible alternatives, and the programs used by the author.

PREFACE

PART ONE: TEXT

(438)

CHAPTER 1 INTRODUCTION

(21)

1.1 Analysis and forecasting

(1)

1.2 Case: Life insurance

(1)

1.3 Case: The Metropolitan Transit Commission

(1)

1.4 Case: Salary and experience

(2)

1.5 Case: The City of West York

(2)

1.6 Case: Stock prices

(1)

1.7 Case: Mortgage loans

(1)

1.8 Exact and approximate relationships

(2)

1.9 Case: Gas consumption

(8)

1.10 To carry forward

(1)

1.11 The book in outline

(2)

CHAPTER 2 REGRESSION

(41)

2.1 Introduction and summary

(1)

2.2 Linear relationships

(2)

2.3 Estimation criteria

(3)

2.4 Least squares estimates

(1)

2.5 Special cases: One or two explanatory variables

(3)

2.6 An interpretation of the OLS estimates

(1)

2.7 Computer programs

(5)

2.8 Measuring the fit of a model

(3)

2.9 The contribution of explanatory variables

(6)

Special case: The contribution of all explanatory variables, R(2)

(2)

Special case: The contribution of one explanatory variable, t-ratios

(2)

2.10 More examples

(6)

2.11 To sum up

(1)

Problems

(10)

CHAPTER 3 INFERENCE IN REGRESSION

(30)

3.1 Introduction and summary

(1)

3.2 Additional properties of OLS estimators

(4)

3.3 A population with special features

(5)

3.4 A population with different features

(1)

3.5 Confidence intervals and tests

(6)

A confidence interval for a parameter

(1)

A test for a parameter

(2)

A confidence interval for a forecast

(1)

A test for a subset of parameters

(1)

3.6 Rough checks of the assumptions of the classical linear model

(1)

3.7 An illustration

(6)

3.8 To sum up

(1)

Problems

(6)

CHAPTER 4 ATTRIBUTES AS EXPLANATORY VARIABLES

(41)

4.1 Introduction and summary

(1)

4.2 Dummy variables

(3)

4.3 A digression: Extreme collinearity

(3)

4.4 Dummy variables, continued

102

(5)

4.5 One-way analysis of variance(+)

107

(7)

4.6 Two-way analysis of variance(+)

114

(8)

4.7 Interactions in analysis of variance(+)

122

(1)

4.8 Multi-way analysis of variance(+)

122

(1)

4.9 To sum up

123

(1)

Problems

123

(13)

CHAPTER 5 NONLINEAR RELATIONSHIPS

136

(37)

5.1 Introduction and summary

136

(1)

5.2 Polynomials

136

(8)

5.3 Inverse relationships

144

(2)

5.4 Piecewise linear models

146

(4)

5.5 Logarithmic models

150

(5)

5.6 Nonlinear regression

155

(3)

5.7 To sum up

158

(1)

Problems

159

(14)

CHAPTER 6 REGRESSION AND TIME SERIES

173

(35)

6.1 Introduction and summary

173

(1)

6.2 Trend

173

(4)

6.3 Trend and seasonality

177

(8)

6.4 Seasonal adjustment of time series

185

(1)

6.5 Method of moving averages

186

(6)

6.6 Time series models in perspective

192

(1)

6.7 To sum up

193

(1)

Problems

193

(15)

CHAPTER 7 LAGGED VARIABLES

208

(27)

7.1 Introduction and summary

208

(1)

7.2 Distributed lags

208

(3)

7.3 The polynomial lag model

211

(5)

7.4 The geometric lag model

216

(7)

7.5 The adaptive expectations model

223

(3)

7.6 The stock adjustment model

226

(2)

7.7 To sum up

228

(1)

Problems

229

(6)

CHAPTER 8 REGRESSION MISCELLANEA

235

(36)

8.1 Introduction and summary

235

(1)

8.2 Regression with zero intercept

236

(2)

8.3 Linear equality constraints

238

(4)

8.4 Linear equality and inequality constraints

242

(4)

8.5 Least absolute deviations

246

(4)

8.6 Multicollinearity

250

(3)

8.7 Stepwise and all subsets regression

253

(2)

8.8 Elements of experimental design

255

(6)

8.9 To sum up

261

(1)

Problems

262

(9)

CHAPTER 9 MORE ON INFERENCE IN REGRESSION(+)

271

(35)

9.1 Introduction and summary

271

(1)

9.2 Nonlinearity

272

(2)

9.3 Nonconstant variance

274

(6)

9.4 Nonindependence, serial correlation

280

(7)

Testing for serial correlation

282

(1)

Consequences of ignoring serial correlation

283

(1)

Estimation procedure

284

(3)

9.5 Specification error

287

(3)

Inclusion of irrelevant variables

289

(1)

Omission of relevant variables

289

(1)

Overlooking a nonlinear relationship

289

(1)

9.6 Generalized least squares (GLS)

290

(1)

9.7 Nonnormality, the generalized linear model (GLM)

291

(4)

9.8 Ridge regression

295

(4)

9.9 To sum up

299

(1)

Problems

300

(6)

CHAPTER 10 AUTOREGRESSIVE MODELS

306

(32)

10.1 Introduction and summary

306

(1)

10.2 Autoregression

306

(4)

10.3 Series with trend and no seasonality

310

(5)

10.4 Series with seasonality

315

(5)

10.5 Autoregressive models in perspective

320

(4)

10.6 AR models(+)

324

(4)

10.7 Nonstationary series, ARI models(+)

328

(1)

10.8 Nonindependence, ARMA and ARIMA models(+)

329

(2)

10.9 To sum up

331

(1)

Problems

332

(6)

CHAPTER 11 THE CLASSIFICATION PROBLEM

338

(26)

11.1 Introduction and summary

338

(1)

11.2 An illustration

339

(4)

11.3 Classification into two categories

343

(3)

11.4 Measuring the quality of a classification rule

346

(3)

11.5 More on the conditional probabilities

349

(3)

11.6 Classification into more than two categories

352

(7)

11.7 To sum up

359

(1)

Problems

360

(4)

CHAPTER 12 MORE ON CLASSIFICATION

364

(45)

12.1 Introduction and summary

364

(1)

12.2 Probability models, two categories

364

(8)

12.3 Joint normal distributions, two categories

372

(9)

12.4 An alternative justification of the linear discriminant function

381

(6)

12.5 A logit model for classification into more than two categories

387

(3)

12.6 Multiple categories and normality

390

(6)

12.7 An alternative discriminant criterion

396

(2)

12.8 To sum up

398

(1)

Problems

399

(10)

CHAPTER 13 MODELS OF SYSTEMS

409

(30)

13.1 Introduction and summary

409

(1)

13.2 The Norgas case revisited

409

(5)

13.3 First generalization

414

(2)

13.4 Systems of equations

416

(1)

13.5 Structural or reduced form?

417

(1)

13.6 Estimation of reduced form parameters

418

(5)

13.7 Estimation of structural form parameters(+)

423

(5)

Ordinary least squares (OLS)

424

(1)

Indirect least squares (ILS)

424

(2)

Two-stage least squares (2SLS)

426

(1)

Three-stage least squares (3SLS)

427

(1)

Full information maximum likelihood (FIML)

427

(1)

13.8 Case: The Canadian meat processing industry

428

(5)

13.9 To sum up

433

(1)

Problems

434

(5)

PART TWO: CASES

439

(100)

The Equal Billing Plan

441

(2)

Corporate compensation and discrimination

443

(3)

The ABC Manufacturing Company

446

(2)

Used car prices

448

(6)

The City of West York

454

(7)

The Energuide program

461

(6)

Carlsen's Brewery

467

(3)

Automobile insurance

470

(10)

Population projections

480

(11)

Electric power consumption

491

(5)

The Emergency Department

496

(2)

Northern Hydro

498

(9)

ACCS Inc.

507

(10)

The Great Wall Bank

517

(7)

Johnson & Phillips

524

(5)

Norgas Distributing Company

529

(10)

PART THREE: APPENDIXES

539

(30)

APPENDIX A: STATISTICAL ESSENTIALS

541

(18)

A.1 The summation notation

541

(2)

A.2 The mean, variance, and standard deviation of a variable

543

(2)

A.3 The mean and variance of a linear function of a variable

545

(1)

A.4 Frequency and relative frequency distributions

546

(1)

A.5 Probability distributions

547

(3)

A.6 Covariance and correlation coefficient of two variables

550

(1)

A.7 The mean and variance of a linear function of two variables

550

(1)

A.8 Joint distributions

551

(2)

A.9 Conditional distributions and independence

553

(1)

A.10 The covariance matrix of a set of variables

554

(1)

A.11 The mean and variance of a linear function of a set of variables

555

(2)

A.12 The covariance of linear functions of uncorrelated variables

557

(2)

APPENDIX B: MATHEMATICAL NOTES

559

(10)

B.1 Derivation of the least squares estimators

559

(1)

B.2 The general linear model in matrix notation

560

(1)

B.3 Covariance of OLS estimators and forecasts

560

(1)

B.4 A regression model with no explanatory variables

561

(1)

B.5 Analysis of variance

562

(1)

B.6 The OLS estimator of a simple model with zero intercept

563

(1)

B.7 The maximum likelihood method of estimation

563

(2)

B.8 Generalized least squares (GLS)

565

(1)

B.9 Derivation of the classification rule under normality

565

(2)

B.10 Structural and reduced form equations

567

(2)

SELECTED REFERENCES

569

(2)

INDEX

571

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Methods for Business Analysis and Forecasting Text and Cases

Summary

Author Biography

Table of Contents

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