Introduction to Econometrics 4 - Lahiri

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Auteur(s) : Lahiri - Maddala

Editeur : John Wiley & Sons

Langue : Anglais

Parution : 01/12/2009

Format : Moyen, de 350g à 1kg

Nombre de pages : 656.0

ISBN : 0470015128

Résumé :
Maintaining G.S. Maddala's brilliant expository style of cutting through the technical superstructure to reveal only essential details, while retaining the nerve centre of the subject matter, Professor Kajal Lahiri has brought forward this new edition of one of the most important textbooks in its field.?

The new edition continues to provide a large number of worked examples, and some shorter data sets.? Further data sets and additional supplementary material to assist both the student and lecturer are available on the companion website www.wileyeurope.com/college/maddala

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Biographie:
G.S.Maddala was one of the leading figures in field of econometrics for more than 30 years until he passed away in 1999. At the time of his death, he held the University Eminent Scholar Professorship in the Department of Economics at Ohio State University. His previous affiliations include Stanford University, University of Rochester and University of Florida.

Kajal Lahiri is Distinguished Professor of Economics, and Health Policy, and Management and Behaviour at the State University of New York, Albany where he is also Director of the Econometric Research Institute. Professor Lahiri is an Honorary Fellow of the International Institute of Forecasters.

Sommaire:
Foreword xvii

Preface to the Fourth Edition xix

Part I Introduction and the Linear Regression Model 1

CHAPTER 1 What is Econometrics? 3

1.1 What is econometrics? 3

1.2 Economic and econometric models 4

1.3 The aims and methodology of econometrics 6

1.4 What constitutes a test of an economic theory? 8

CHAPTER 2 Statistical Background and Matrix Algebra 11

2.1 Introduction 11

2.2 Probability 12

2.3 Random variables and probability distributions 17

2.4 The normal probability distribution and related distributions 18

2.5 Classical statistical inference 21

2.6 Properties of estimators 22

2.7 Sampling distributions for samples from a normal population 26

2.8 Interval estimation 26

2.9 Testing of hypotheses 28

2.10 Relationship between confidence interval procedures and tests of hypotheses 31

2.11 Combining independent tests 32

CHAPTER 3 Simple Regression 59

3.1 Introduction 59

3.2 Specification of the relationships 61

3.3 The method of moments 65

3.4 The method of least squares 68

3.5 Statistical inference in the linear regression model 76

3.6 Analysis of variance for the simple regression model 83

3.7 Prediction with the simple regression model 85

3.8 Outliers 88

3.9 Alternative functional forms for regression equations 95

*3.10 Inverse prediction in the least squares regression model1 99

*3.11 Stochastic regressors 102

*3.12 The regression fallacy 102

CHAPTER 4 Multiple Regression 127

4.1 Introduction 127

4.2 A model with two explanatory variables 129

4.3 Statistical inference in the multiple regression model 134

4.4 Interpretation of the regression coefficients 143

4.5 Partial correlations and multiple correlation 146

4.6 Relationships among simple, partial, and multiple correlation coefficients 147

4.7 Prediction in the multiple regression model 153

4.8 Analysis of variance and tests of hypotheses 155

4.9 Omission of relevant variables and inclusion of irrelevant variables 160

4.10 Degrees of freedom and R2 165

4.11 Tests for stability 169

4.12 The LR, W, and LM tests 176

Part II Violation of the Assumptions of the Basic Regression Model 209

CHAPTER 5 Heteroskedasticity 211

5.1 Introduction 211

5.2 Detection of heteroskedasticity 214

5.3 Consequences of heteroskedasticity 219

5.4 Solutions to the heteroskedasticity problem 221

5.5 Heteroskedasticity and the use of deflators 224

5.6 Testing the linear versus log-linear functional form 228

CHAPTER 6 Autocorrelation 239

6.1 Introduction 239

6.2 The Durbin-Watson test 240

6.3 Estimation in levels versus first differences 242

6.4 Estimation procedures with autocorrelated errors 246

6.5 Effect of AR(1) errors on OLS estimates 250

6.6 Some further comments on the DW test 254

6.7 Tests for serial correlation in models with lagged dependent variables 257

6.8 A general test for higher-order serial correlation: The LM test 259

6.9 Strategies when the DW test statistic is significant 261

*6.10 Trends and random walks 266

*6.11 ARCH models and serial correlation 271

6.12 Some comments on the DW test and Durbin's h-test and t-test 272

CHAPTER 7 Multicollinearity 279

7.1 Introduction 279

7.2 Some illustrative examples 280

7.3 Some measures of multicollinearity 283

7.4 Problems with measuring multicollinearity 286

7.5 Solutio...