TY  - BOOK
AU  - Gujarati, Damodra N
AU  - Damodra N Gujarati
TI  - Basic Econometrics
SN  - 9780071333450
U1  - 330.015195 GUJ-B 
PY  - 2009///
PB  - Mcgraw -Hill
KW  - Economics
N1  - Contents
Preface vii
Acknowledgments xi
Introduction 1
 I.1 What is Econometrics? 1
 I.2 Why a Separate Discipline? 2
 I.3 Methodology of Econometrics 2
 I.4 Types of Econometrics 9
 I.5 Mathematical and Statistical Prerequisites 10
 I.6 The Role of the Computer 10
 I.7 Suggestions for Further Reading 10
PART 1 Single-Equation Regression Models
1. The Nature of Regression Analysis 15
 1.1 Historical Origin of the Term Regression 15
 1.2 The Modern Interpretation of Regression 15
 1.3 Statistical versus Deterministic Relationships 19
 1.4 Regression versus Causation 19
 1.5 Regression versus Correlation 20
 1.6 Terminology and Notation 20
 1.7 The Nature and Sources of Data for Economic Analysis 21
 Summary and Conclusions 28
 Multiple Choice Questions 29
 Exercises 32
 Key to Multiple Choice Questions 37
2. Two-Variable Regression Analysis: Some Basic Ideas 38
 2.1 A Hypothetical Example 38
 2.2 The Concept of Population Regression Function (PRF) 41
 2.3 The Meaning of the Term Linear 42
 2.4 Stochastic Specifi cation of PRF 43
xvi Contents
 2.5 The Signifi cance of the Stochastic Disturbance Term 45
 2.6 The Sample Regression Function (SRF) 46
 2.7 Illustrative Examples 49
 Summary and Conclusions 51
 Multiple Choice Questions 51
 Exercises 54
 Key to Multiple Choice Questions 60
3. Two-Variable Regression Model: The Problem of Estimation 61
 3.1 The Method of Ordinary Least Squares 61
 3.2 The Classical Linear Regression Model: The Assumptions Underlying the
Method of Least Squares 67
 3.3 Precision or Standard Errors of Least-Squares Estimates 74
 3.4 Properties of Least-Squares Estimators: The Gauss–Markov Theorem 76
 3.5 The Coeffi cient of Determination r2
: A Measure of “Goodness of Fit” 78
 3.6 A Numerical Example 83
 3.7 Illustrative Examples 86
 3.8 A Note on Monte Carlo Experiments 88
 Summary and Conclusions 89
 Multiple Choice Questions 90
 Exercises 93
 Key to Multiple Choice Questions 99
 Appendix 3A 100
4. Classical Normal Linear Regression Model (CNLRM) 105
 4.1 The Probability Distribution of Disturbances ui
 105
 4.2 The Normality Assumption for ui
 106
 4.3 Properties of OLS Estimators under the Normality Assumption 107
 4.4 The Method of Maximum Likelihood (ML) 109
 Summary and Conclusions 110
 Appendix 4A 113
5. Two-Variable Regression: Interval Estimation and Hypothesis Testing 115
 5.1 Statistical Prerequisites 115
 5.2 Interval Estimation: Some Basic Ideas 115
 5.3 Confi dence Intervals for Regression Coeffi cients b1 and b2 117
 5.4 Confi dence Interval for s2 119
 5.5 Hypothesis Testing: General Comments 120
 5.6 Hypothesis Testing: The Confi dence-Interval Approach 121
 5.7 Hypothesis Testing: The Test-of-Signifi cance Approach 122
 5.8 Hypothesis Testing: Some Practical Aspects 127
 5.9 Regression Analysis and Analysis of Variance 131
 5.10 Application of Regression Analysis: The Problem of Prediction 133
 5.11 Reporting the Results of Regression Analysis 136
 5.12 Evaluating the Results of Regression Analysis 137
 Summary and Conclusions 140
 Multiple Choice Questions 141
 Exercises 146
Contents xvii
Key to Multiple Choice Questions 154
 Appendix 5A 155
6. Extensions of the Two-Variable Linear Regression Model 159
 6.1 Regression through the Origin 159
 6.2 Scaling and Units of Measurement 166
 6.3 Regression on Standardized Variables 170
 6.4 Functional Forms of Regression Models 171
 6.5 How to Measure Elasticity: The Log-Linear Model 172
 6.6 Semilog Models: Log–Lin and Lin–Log Models 175
 6.7 Reciprocal Models 179
 6.8 Choice of Functional Form 184
 6.9 A Note on the Nature of the Stochastic Error Term: Additive versus
Multiplicative Stochastic Error Term 186
 Summary and Conclusions 187
 Multiple Choice Questions 188
 Exercises 190
 Key to Multiple Choice Questions 196
 Appendix 6A 197
7. Multiple Regression Analysis: The Problem of Estimation 203
 7.1 The Three-Variable Model: Notation and Assumptions 203
 7.2 Interpretation of Multiple Regression Equation 205
 7.3 The Meaning of Partial Regression Coeffi cients 205
 7.4 OLS and ML Estimation of the Partial Regression Coeffi cients 207
 7.5 The Multiple Coeffi cient of Determination R2
 and the
Multiple Coeffi cient of Correlation R 210
 7.6 An Illustrative Example 212
 7.7 Simple Regression in the Context of Multiple Regression:
Introduction to Specifi cation Bias 214
 7.8 R2
 and the Adjusted R2 215
 7.9 The Cobb–Douglas Production Function: More on Functional Form 220
 7.10 Polynomial Regression Models 223
 *7.11 Partial Correlation Coeffi cients 226
 Summary and Conclusions 228
 Multiple Choice Questions 228
 Exercises 231
 Key to Multiple Choice Questions 243
 Appendix 7A 243
8. Multiple Regression Analysis: The Problem of Inference 249
 8.1 The Normality Assumption Once Again 249
 8.2 Hypothesis Testing in Multiple Regression: General Comments 250
 8.3 Hypothesis Testing about Individual Regression Coeffi cients 251
 8.4 Testing the Overall Signifi cance of the Sample Regression 253
 8.5 Testing the Equality of Two Regression Coeffi cients 262
 8.6 Restricted Least Squares: Testing Linear Equality Restrictions 264
 8.7 Testing for Structural or Parameter Stability of Regression Models: The Chow Test 270
xviii Contents
 8.8 Prediction with Multiple Regression 275
 8.9 The Troika of Hypothesis Tests: The Likelihood Ratio (LR), Wald (W), and
Lagrange Multiplier (LM) Tests 275
8.10 Testing the Functional Form of Regression: Choosing between
Linear and Log–Linear Regression Models 276
 Summary and Conclusions 278
 Multiple Choice Questions 278
 Exercises 281
 Key to Multiple Choice Questions 292
 Appendix 8A 292
9. Dummy Variable Regression Models 295
 9.1 The Nature of Dummy Variables 295
 9.2 ANOVA Models 296
 9.3 ANOVA Models with Two Qualitative Variables 300
 9.4 Regression with a Mixture of Quantitative and Qualitative Regressors:
The ANCOVA Models 302
 9.5 The Dummy Variable Alternative to the Chow Test 303
 9.6 Interaction Effects Using Dummy Variables 306
 9.7 The Use of Dummy Variables in Seasonal Analysis 307
 9.8 Piecewise Linear Regression 311
 9.9 Panel Data Regression Models 314
 9.10 Some Technical Aspects of the Dummy Variable Technique 314
 9.11 Topics for Further Study 316
 9.12 A Concluding Example 316
 Summary and Conclusions 320
 Multiple Choice Questions 320
 Exercises 324
 Key to Multiple Choice Questions 332
 Appendix 9A 332
PART 2 Relaxing the Assumptions of the Classical Model
10. Multicollinearity: What Happens If the Regressors are Correlated? 339
 10.1 The Nature of Multicollinearity 340
 10.2 Estimation in the Presence of Perfect Multicollinearity 342
 10.3 Estimation in the Presence of “High” but “Imperfect” Multicollinearity 344
 10.4 Multicollinearity: Much Ado about Nothing? Theoretical Consequences of
Multicollinearity 344
 10.5 Practical Consequences of Multicollinearity 346
 10.6 An Illustrative Example 351
 10.7 Detection of Multicollinearity 356
 10.8 Remedial Measures 360
 10.9 Is Multicollinearity Necessarily Bad? Maybe Not, If the Objective
Is Prediction Only 365
 10.10 An Extended Example: The Longley Data 365
 Summary and Conclusions 368
Contents xix
 Multiple Choice Questions 369
 Exercises 372
 Key to Multiple Choice Questions 385
11. Heteroscedasticity: What Happens if the Error Variance is Nonconstant? 386
 11.1 The Nature of Heteroscedasticity 386
 11.2 OLS Estimation in the Presence of Heteroscedasticity 391
 11.3 The Method of Generalized Least Squares (GLS) 392
 11.4 Consequences of Using OLS in the Presence of Heteroscedasticity 395
 11.5 Detection of Heteroscedasticity 397
 11.6 Remedial Measures 410
 11.7 Concluding Examples 416
 11.8 A Caution about Overreacting to Heteroscedasticity 420
 Summary and Conclusions 421
 Multiple Choice Questions 421
 Exercises 424
 Key to Multiple Choice Questions 432
 Appendix 11A 432
12. Autocorrelation: What Happens if the Error Terms are Correlated? 436
 12.1 The Nature of the Problem 437
 12.2 OLS Estimation in the Presence of Autocorrelation 443
 12.3 The BLUE Estimator in the Presence of Autocorrelation 445
 12.4 Consequences of Using OLS in the Presence of Autocorrelation 446
 12.5 Relationship between Wages and Productivity in the Business Sector of the
United States, 1960–2005 451
 12.6 Detecting Autocorrelation 453
 12.7 What to do when you fi nd Autocorrelation: Remedial Measures 463
 12.8 Model Mis-Specifi cation versus Pure Autocorrelation 463
 12.9 Correcting for (Pure) Autocorrelation: The Method of Generalized
Least Squares (GLS) 464
 12.10 The Newey–West Method of Correcting the OLS Standard Errors 470
 12.11 OLS versus FGLS and HAC 470
 12.12 Additional Aspects of Autocorrelation 471
 12.13 A Concluding Example 472
 Summary and Conclusions 474
 Multiple Choice Questions 475
 Exercises 478
 Key to Multiple Choice Questions 490
 Appendix 12A 491
13. Econometric Modeling: Model Specifi cation and Diagnostic Testing 492
 13.1 Model Selection Criteria 493
 13.2 Types of Specifi cation Errors 493
 13.3 Consequences of Model Specifi cation Errors 495
 13.4 Tests of Specifi cation Errors 499
 13.5 Errors of Measurement 506
xx Contents
 13.6 Incorrect Specifi cation of the Stochastic Error Term 510
 13.7 Nested versus Non-Nested Models 510
 13.8 Tests of Non-Nested Hypotheses 511
 13.9 Model Selection Criteria 516
 13.10 Additional Topics in Econometric Modeling 520
 13.11 Concluding Examples 524
 13.12 Non-Normal Errors and Stochastic Regressors 533
 13.13 A Word to the Practitioner 535
 Summary and Conclusions 536
 Multiple Choice Questions 537
 Exercises 540
 Key to Multiple Choice Questions 546
 Appendix 13A 546
PART 3 Topics in Econometrics
14. Nonlinear Regression Models 553
 14.1 Intrinsically Linear and Intrinsically Nonlinear Regression Models 553
 14.2 Estimation of Linear and Nonlinear Regression Models 555
 14.3 Estimating Nonlinear Regression Models: The Trial-and-Error Method 555
 14.4 Approaches to Estimating Nonlinear Regression Models 557
 14.5 Illustrative Examples 558
 Summary and Conclusions 562
 Multiple Choice Questions 563
 Exercises 565
 Key to Multiple Choice Questions 566
 Appendix 14A 567
15. Qualitative Response Regression Models 570
 15.1 The Nature of Qualitative Response Models 570
 15.2 The Linear Probability Model (LPM) 572
 15.3 Applications of LPM 578
 15.4 Alternatives to LPM 581
 15.5 The Logit Model 582
 15.6 Estimation of the Logit Model 584
 15.7 The Grouped Logit (Glogit) Model: A Numerical Example 587
 15.8 The Logit Model for Ungrouped or Individual Data 590
 15.9 The Probit Model 594
 15.10 Logit and Probit Models 599
 15.11 The Tobit Model 602
 15.12 Modeling Count Data: The Poisson Regression Model 604
 15.13 Further Topics in Qualitative Response Regression Models 607
 Summary and Conclusions 609
 Multiple Choice Questions 610
 Exercises 613
 Key to Multiple Choice Questions 620
 Appendix 15A 620
Contents xxi
16. Panel Data Regression Models 622
 16.1 Why Panel Data? 623
 16.2 Panel Data: An Illustrative Example 624
 16.3 Pooled OLS Regression or Constant Coeffi cients Model 625
 16.4 The Fixed Effect Least-Squares Dummy Variable (LSDV) Model 627
 16.5 The Fixed-Effect Within-Group (WG) Estimator 630
 16.6 The Random Effects Model (REM) 633
 16.7 Properties of Various Estimators 637
 16.8 Fixed Effects versus Random Effects Model: Some Guidelines 637
 16.9 Panel Data Regressions: Some Concluding Comments 638
 16.10 Some Illustrative Examples 639
 Summary and Conclusions 644
 Multiple Choice Questions 645
 Exercises 648
 Key to Multiple Choice Questions 651
17. Dynamic Econometric Models: Autoregressive and Distributed-Lag Models 652
 17.1 The Role of “Time,” or “Lag,” in Economics 653
 17.2 The Reasons for Lags 657
 17.3 Estimation of Distributed-Lag Models 658
 17.4 The Koyck Approach to Distributed-Lag Models 659
 17.5 Rationalization of the Koyck Model: The Adaptive Expectations Model 664
 17.6 Another Rationalization of the Koyck Model: The Stock
Adjustment, or Partial Adjustment, Model 666
 17.7 Combination of Adaptive Expectations and Partial Adjustment Models 668
 17.8 Estimation of Autoregressive Models 669
 17.9 The Method of Instrumental Variables (IV) 670
 17.10 Detecting Autocorrelation in Autoregressive Models: Durbin h Test 671
 17.11 A Numerical Example: The Demand for Money in Canada,
1979–I to 1988–IV 673
 17.12 Illustrative Examples 676
 17.13 The Almon Approach to Distributed-Lag Models:
The Almon or Polynomial Distributed Lag (PDL) 679
 17.14 Causality in Economics: The Granger Causality Test 686
 Summary and Conclusions 692
 Multiple Choice Questions 693
 Exercises 696
 Key to Multiple Choice Questions 705
 Appendix 17A 705
PART 4 Simultaneous-Equation Models and Time Series Econometrics
18. Simultaneous-Equation Models 709
 18.1 The Nature of Simultaneous-Equation Models 709
 18.2 Examples of Simultaneous-Equation Models 710
 18.3 The Simultaneous-Equation Bias: Inconsistency of OLS Estimators 715
 18.4 The Simultaneous-Equation Bias: A Numerical Example 718
 Summary and Conclusions 720
xxii Contents
 Multiple Choice Questions 720
 Exercises 721
 Key to Multiple Choice Questions 725
19. The Identifi cation Problem 726
 19.1 Notations and Defi nitions 726
 19.2 The Identifi cation Problem 729
 19.3 Rules for Identifi cation 736
19.4 A Test of Simultaneity 740
19.5 Tests for Exogeneity 743
 Summary and Conclusions 743
 Multiple Choice Questions 744
 Exercises 746
 Key to Multiple Choice Questions 750
20. Simultaneous-Equation Methods 751
 20.1 Approaches to Estimation 751
 20.2 Recursive Models and Ordinary Least Squares 753
 20.3 Estimation of a Just Identifi ed Equation: The Method of Indirect Least Squares (ILS) 755
 20.4 Estimation of an Overidentifi ed Equation: The Method of
Two-Stage Least Squares (2SLS) 758
 20.5 2SLS: A Numerical Example 761
 20.6 Illustrative Examples 764
 Summary and Conclusions 770
 Multiple Choice Questions 771
 Exercises 773
 Key to Multiple Choice Questions 777
 Appendix 20A 777
21. Time Series Econometrics: Some Basic Concepts 780
 21.1 A Look at Selected U.S. Economic Time Series 781
 21.2 Key Concepts 782
 21.3 Stochastic Processes 783
 21.4 Unit Root Stochastic Process 787
 21.5 Trend Stationary (TS) and Difference Stationary (DS)
Stochastic Processes 788
 21.6 Integrated Stochastic Processes 789
 21.7 The Phenomenon of Spurious Regression 790
 21.8 Tests of Stationarity 791
 21.9 The Unit Root Test 797
 21.10 Transforming Nonstationary Time Series 802
 21.11 Cointegration: Regression of a Unit Root Time Series on
Another Unit Root Time Series 805
 21.12 Some Economic Applications 808
 Summary and Conclusions 811
 Multiple Choice Questions 812
 Exercises 815
 Key to Multiple Choice Questions 819
Contents xxiii
22. Time Series Econometrics: Forecasting 820
 22.1 Approaches to Economic Forecasting 820
 22.2 AR, MA, and ARIMA Modeling of Time Series Data 822
 22.3 The Box–Jenkins (BJ) Methodology 824
 22.4 Identifi cation 825
 22.5 Estimation of the ARIMA Model 829
 22.6 Diagnostic Checking 829
 22.7 Forecasting 830
 22.8 Further Aspects of the BJ Methodology 831
 22.9 Vector Autoregression (VAR) 831
 22.10 Measuring Volatility in Financial Time Series: The ARCH and GARCH Models 838
 22.11 Concluding Examples 843
 Summary and Conclusions 845
 Multiple Choice Questions 846
 Exercises 848
 Key to Multiple Choice Questions 850
Appendix D* Statistical Tables 851
Selected Bibliography 868
Index 873
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