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Introduction to Econometrics
ISBN-13: 9780470015124 / Angielski / Miękka / 2010 / 656 str.
Introduction to Econometrics
ISBN-13: 9780470015124 / Angielski / Miękka / 2010 / 656 str.
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Now in its fourth edition, this landmark text provides a fresh, accessible and well-written introduction to the subject. With a rigorous pedagogical framework, which sets it apart from comparable texts, the latest edition features an expanded website providing numerous real life data sets and examples.
Wydanie ilustrowane
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 Solutions to the multicollinearity problem: Ridge regression 290
7.6 Principal component regression 292
7.7 Dropping variables 297
7.8 Miscellaneous other solutions 300
CHAPTER 8 Dummy Variables and Truncated Variables 313
8.1 Introduction 313
8.2 Dummy variables for changes in the intercept term 314
8.3 Dummy variables for changes in slope coefficients 319
8.4 Dummy variables for cross–equation constraints 322
8.5 Dummy variables for testing stability of regression coefficients 324
8.6 Dummy variables under heteroskedasticity and autocorrelation 327
8.7 Dummy dependent variables 329
8.8 The linear probability model and the linear discriminant function 329
8.9 The probit and logit models 333
8.10 Truncated variables: The tobit model 343
CHAPTER 9 Simultaneous Equation Models 355
9.1 Introduction 355
9.2 Endogenous and exogenous variables 357
9.3 The identification problem: Identification through reduced form 357
9.4 Necessary and sufficient conditions for identification 362
9.5 Methods of estimation: The instrumental variable method 365
9.6 Methods of estimation: The two–stage least squares method 371
9.7 The question of normalization 378
∗9.8 The limited–information maximum likelihood method 379
∗9.9 On the use of OLS in the estimation of simultaneous equation models 380
∗9.10 Exogeneity and causality 386
9.11 Some problems with instrumental variable methods 392
CHAPTER 10 Diagnostic Checking, Model Selection, and Specification Testing 401
10.1 Introduction 401
10.2 Diagnostic tests based on least squares residuals 402
10.3 Problems with least squares residuals 404
10.4 Some other types of residual 405
10.5 DFFITS and bounded influence estimation 411
10.6 Model selection 414
10.7 Selection of regressors 419
10.8 Implied F–ratios for the various criteria 423
10.9 Cross–validation 427
10.10 Hausman s specification error test 428
10.11 The Plosser Schwert White differencing test 435
10.12 Tests for nonnested hypotheses 436
10.13 Nonnormality of errors 440
10.14 Data transformations 441
CHAPTER 11 Errors in Variables 451
11.1 Introduction 451
11.2 The classical solution for a single–equation model with one explanatory variable 452
11.3 The single–equation model with two explanatory variables 455
11.4 Reverse regression 463
11.5 Instrumental variable methods 465
11.6 Proxy variables 468
11.7 Some other problems 471
Part III Special Topics 479
CHAPTER 12 Introduction to Time–Series Analysis 481
12.1 Introduction 481
12.2 Two methods of time–series analysis: Frequency domain and time domain 482
12.3 Stationary and nonstationary time series 482
12.4 Some useful models for time series 485
12.5 Estimation of AR, MA, and ARMA models 492
12.6 The Box Jenkins approach 496
12.7 R2 measures in time–series models 503
CHAPTER 13 Models of Expectations and Distributed Lags 509
13.1 Models of expectations 509
13.2 Naive models of expectations 510
13.3 The adaptive expectations model 512
13.4 Estimation with the adaptive expectations model 514
13.5 Two illustrative examples 516
13.6 Expectational variables and adjustment lags 520
13.7 Partial adjustment with adaptive expectations 524
13.8 Alternative distributed lag models: Polynomial lags 526
13.9 Rational lags 533
13.10 Rational expectations 534
13.11 Tests for rationality 536
13.12 Estimation of a demand and supply model under rational expectations 538
13.13 The serial correlation problem in rational expectations models 544
CHAPTER 14 Vector Autoregressions, Unit Roots, and Cointegration 551
14.1 Introduction 551
14.2 Vector autoregressions 551
14.3 Problems with VAR models in practice 553
14.4 Unit roots 554
14.5 Unit root tests 555
14.6 Cointegration 563
14.7 The cointegrating regression 564
14.8 Vector autoregressions and cointegration 567
14.9 Cointegration and error correction models 571
14.10 Tests for cointegration 571
14.11 Cointegration and testing of the REH and MEH 572
14.12 A summary assessment of cointegration 574
CHAPTER 15 Panel Data Analysis 583
15.1 Introduction 583
15.2 The LSDV or fixed effects model 584
15.3 The random effects model 586
15.4 Fixed effects versus random effects 589
15.5 Dynamic panel data models 591
15.6 Panel data models with correlated effects and simultaneity 593
15.7 Errors in variables in panel data 595
15.8 The SUR model 597
15.9 The random coefficient model 597
CHAPTER 16 Small–Sample Inference: Resampling Methods 601
16.1 Introduction 601
16.2 Monte Carlo methods 602
16.3 Resampling methods: Jackknife and bootstrap 603
16.4 Bootstrap confidence intervals 605
16.5 Hypothesis testing with the bootstrap 606
16.6 Bootstrapping residuals versus bootstrapping the data 607
16.7 Non–IID errors and nonstationary models 607
Appendix 611
Index 621
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.
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
New features for the fourth edition:
- Chapters 5 and 6, on Heteroscedasticity and Autocorrelation, now reflect the latest professional practice in dealing with these common variations of the basic regression model.
- Chapter 10 includes extensive discussion on diagnostic checking in linear models, various nested and non–nested model selection procedures, specification testing, data transformations, and tests for non–normality.
- The first three chapters of Part III cover an introduction to time–series analysis, including the Box Jenkins approach, forecasting and seasonality, models of expectations and distributed lag models, and vector auto–regressions, unit roots, and cointegration.
- Chapters 15 and 16 cover, respectively, the latest developments in panel data analysis and various re–sampling methods for use in small sample inference.
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