Basic econometrics

Title:

Basic econometrics

Author:

Gujarati, Damodar N.

ISBN:

9780072335422

9780071123426

9780071123433

Personal Author:

Gujarati, Damodar N.

Edition:

4th ed.

Publication Information:

Boston : McGraw Hill, c2003.

Physical Description:

xxix, 1002 p. : ill. ; 24 cm. + 1 computer optical disc (4 3/4 in.)

Subject Term:

Ekonometri.

Econometrics.

Econometrâia.

Available:*

Library	Material Type	Item Barcode	Shelf Number	Status
Searching... Pamukkale Central Library	Book	0058872	HB139 G84 2003	Searching... Unknown
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Gujarati's Basic Econometrics provides an elementary but comprehensive introduction to econometrics without resorting to matrix algebra, calculus, or statistics beyond the elementary level. Because of the way the book is organized, it may be used at a variety of levels of rigor. For example, if matrix algebra is used, theoretical exercises may be omitted. A CD of data sets is provided with the text.

Preface	p. xxi
Introduction	p. 1
Part 1 Single-Equation Regression Models
1 The Nature of Regression Analysis	p. 15
1.1 Historical Origin of the Term "Regression"	p. 15
1.2 The Modern Interpretation of Regression	p. 16
Examples	p. 16
1.3 Statistical vs. Deterministic Relationships	p. 19
1.4 Regression vs. Causation	p. 20
1.5 Regression vs. Correlation	p. 21
1.6 Terminology and Notation	p. 22
1.7 The Nature and Sources of Data for Econometric Analysis	p. 23
Types of Data	p. 23
The Sources of Data	p. 24
The Accuracy of Data	p. 26
1.8 Summary and Conclusions	p. 27
Exercises	p. 28
Appendix 1A	p. 29
1A.1 Sources of Economic Data	p. 29
1A.2 Sources of Financial Data	p. 31
2 Two-Variable Regression Analysis: Some Basic Ideas	p. 32
2.1 A Hypothetical Example	p. 32
2.2 The Concept of Population Regression Function (PRF)	p. 36
2.3 The Meaning of the Term "Linear"	p. 36
Linearity in the Variables	p. 37
Linearity in the Parameters	p. 37
2.4 Stochastic Specification of PRF	p. 38
2.5 The Significance of the Stochastic Disturbance Term	p. 39
2.6 The Sample Regression Function (SRF)	p. 41
2.7 Summary and Conclusions	p. 45
Exercises	p. 45
3 Two-Variable Regression Model: The Problem of Estimation	p. 52
3.1 The Method of Ordinary Least Squares	p. 52
3.2 The Classical Linear Regression Model: The Assumptions Underlying the Method of Least Squares	p. 59
How Realistic Are These Assumptions?	p. 68
3.3 Precision or Standard Errors of Least-Squares Estimates	p. 69
3.4 Properties of Least-Squares Estimators: The Gauss-Markov Theorem	p. 72
3.5 The Coefficient of Determination r2: A Measure of "Goodness of Fit"	p. 74
3.6 A Numerical Example	p. 80
3.7 Illustrative Examples	p. 83
Coffee Consumption in the United States, 1970-1980	p. 83
Keynesian Consumption Function for the United States, 1980-1991	p. 84
3.8 Computer Output for the Coffee Demand Function	p. 85
3.9 A Note on Monte Carlo Experiments	p. 85
3.10 Summary and Conclusions	p. 86
Exercises	p. 87
Questions	p. 87
Problems	p. 89
Appendix 3A	p. 94
3A.1 Derivation of Least-Squares Estimates	p. 94
3A.2 Linearity and Unbiasedness Properties of Least-Squares Estimators	p. 94
3A.3 Variances and Standard Errors of Least-Squares Estimators	p. 95
3A.4 Covariance between B1 and B2	p. 96
3A.5 The Least-Squares Estimator of o2	p. 96
3A.6 Minimum-Variance Property of Least-Squares Estimators	p. 97
3A.7 SAS Output of the Coffee Demand Function (3.7.1)	p. 99
4 The Normality Assumption: Classical Normal Linear Regression Model (CNLRM)	p. 101
4.1 The Probability Distribution of Disturbances ui	p. 101
4.2 The Normality Assumption	p. 102
4.3 Properties of OLS Estimators under the Normality Assumption	p. 104
4.4 The Method of Maximum Likelihood (ML)	p. 107
4.5 Probability Distributions Related to the Normal Distribution: The t, Chi-square (X2), and F Distributions	p. 107
4.6 Summary and Conclusions	p. 109
Appendix 4A	p. 110
Maximum Likelihood Estimation of Two-Variable Regression Model	p. 110
Maximum Likelihood Estimation of the Consumption-Income Example	p. 113
Appendix 4A Exercises	p. 113
5 Two-Variable Regression: Interval Estimation and Hypothesis Testing	p. 115
5.1 Statistical Prerequisites	p. 115
5.2 Interval Estimation: Some Basic Ideas	p. 116
5.3 Confidence Intervals for Regression Coefficients B1 and B2	p. 117
Confidence Interval for B2	p. 117
Confidence Interval for B1	p. 119
Confidence Interval for B1 and B2 Simultaneously	p. 120
5.4 Confidence Interval for o2	p. 120
5.5 Hypothesis Testing: General Comments	p. 121
5.6 Hypothesis Testing: The Confidence-Interval Approach	p. 122
Two-Sided or Two-Tail Test	p. 122
One-Sided or One-Tail Test	p. 124
5.7 Hypothesis Testing: The Test-of-Significance Approach	p. 124
Testing the Significance of Regression Coefficients: The t-Test	p. 124
Testing the Significance of o2: the X2 Test	p. 128
5.8 Hypothesis Testing: Some Practical Aspects	p. 129
The Meaning of "Accepting" or "Rejecting" a Hypothesis	p. 129
The "Zero" Null Hypothesis and the "2-t" Rule of Thumb	p. 129
Forming the Null and Alternative Hypotheses	p. 130
Choosing a, the Level of Significance	p. 131
The Exact Level of Significance: The p Value	p. 132
Statistical Significance versus Practical Significance	p. 133
The Choice between Confidence-Interval and Test-of-Significance Approaches to Hypothesis Testing	p. 134
5.9 Regression Analysis and Analysis of Variance	p. 134
5.10 Application of Regression Analysis: The Problem of Prediction	p. 137
Mean Prediction	p. 137
Individual Prediction	p. 138
5.11 Reporting the Results of Regression Analysis	p. 140
5.12 Evaluating the Results of Regression Analysis	p. 140
Normality Test	p. 141
Other Tests of Model Adequacy	p. 144
5.13 Summary and Conclusions	p. 144
Exercises	p. 145
Questions	p. 145
Problems	p. 147
Appendix 5A	p. 152
5A.1 Derivation of Equation (5.3.2)	p. 152
5A.2 Derivation of Equation (5.9.1)	p. 152
5A.3 Derivation of Equations (5.10.2) and (5.10.6)	p. 153
Variance of Mean Prediction	p. 153
Variance of Individual Prediction	p. 153
6 Extensions of the Two-Variable Linear Regression Model	p. 155
6.1 Regression through the Origin	p. 155
r2 for Regression-through-Origin Model An Illustrative Example: The Characteristic Line of Portfolio Theory	p. 159
6.2 Scaling and Units of Measurement	p. 161
A Numerical Example: The Relationship between GPDI and GNP, United States, 1974-1983	p. 163
A Word about Interpretation	p. 164
6.3 Functional Forms of Regression Models	p. 165
6.4 How to Measure Elasticity: The Log-Linear Model	p. 165
An Illustrative Example: The Coffee Demand Function Revisited	p. 167
6.5 Semilog Models: Log-Lin and Lin-Log Models	p. 169
How to Measure the Growth Rate: The Log-Lin Model	p. 169
The Lin-Log Model	p. 172
6.6 Reciprocal Models	p. 173
An Illustrative Example: The Phillips Curve for the United Kingdom, 1950-1966	p. 176
6.7 Summary of Functional Forms	p. 176
6.8 A Note on the Nature of the Stochastic Error Term: Additive versus Multiplicative Stochastic Error Term	p. 178
6.9 Summary and Conclusions	p. 179
Exercises	p. 180
Questions	p. 180
Problems	p. 183
Appendix 6A	p. 186
6A.1 Derivation of Least-Squares Estimators for Regression through the Origin	p. 186
6A.2 SAS Output of the Characteristic Line (6.1.12)	p. 189
6A.3 SAS Output of the United Kingdom Phillips Curve Regression (6.6.2)	p. 190
7 Multiple Regression Analysis: The Problem of Estimation	p. 191
7.1 The Three-Variable Model: Notation and Assumptions	p. 192
7.2 Interpretation of Multiple Regression Equation	p. 194
7.3 The Meaning of Partial Regression Coefficients	p. 195
7.4 OLS and ML Estimation of the Partial Regression Coefficients	p. 197
OLS Estimators	p. 197
Variances and Standard Errors of OLS Estimators	p. 198
Properties of OLS Estimators	p. 199
Maximum Likelihood Estimators	p. 201
7.5 The Multiple Coefficient of Determination R2 and the Multiple Coefficient of Correlation R	p. 201
7.6 Example 7.1: The Expectations-Augmented Phillips Curve for the United States, 1970-1982	p. 203
7.7 Simple Regression in the Context of Multiple Regression: Introduction to Specification Bias	p. 204
7.8 R2 and the Adjusted R2	p. 207
Comparing Two R2 Values	p. 209
Example 7.2: Coffee Demand Function Revisited	p. 210
The "Game" of Maximizing R2	p. 211
7.9 Partial Correlation Coefficients	p. 211
Explanation of Simple and Partial Correlation Coefficients	p. 211
Interpretation of Simple and Partial Correlation Coefficients	p. 213
7.10 Example 7.3: The Cobb-Douglas Production Function: More on Functional Form	p. 214
7.11 Polynomial Regression Models	p. 217
Example 7.4: Estimating the Total Cost Function	p. 218
Empirical Results	p. 220
7.12 Summary and Conclusions	p. 221
Exercises	p. 221
Questions	p. 221
Problems	p. 224
Appendix 7A	p. 231
7A.1 Derivation of OLS Estimators Given in Equations (7.4.3) and (7.4.5)	p. 231
7A.2 Equality between a1 of (7.3.5) and B2 of (7.4.7)	p. 232
7A.3 Derivation of Equation (7.4.19)	p. 232
7A.4 Maximum Likelihood Estimation of the Multiple Regression Model	p. 233
7A.5 The Proof that E(b12) = B2 + B3b32 (Equation 7.7.4)	p. 234
7A.6 SAS Output of the Expectations-Augmented Phillips Curve (7.6.2)	p. 236
7A.7 SAS Output of the Cobb-Douglas Production Function (7.10.4)	p. 237
8 Multiple Regression Analysis: The Problem of Inference	p. 238
8.1 The Normality Assumption Once Again	p. 238
8.2 Example 8.1: U.S. Personal Consumption and Personal Disposal Income Relation, 1956-1970	p. 239
8.3 Hypothesis Testing in Multiple Regression: General Comments	p. 242
8.4 Hypothesis Testing about Individual Partial Regression Coefficients	p. 242
8.5 Testing the Overall Significance of the Sample Regression	p. 244
The Analysis of Variance Approach to Testing the Overall Significance of an Observed Multiple Regression: The F Test	p. 245
An Important Relationship between R2 and F	p. 248
The "Incremental," or "Marginal," Contribution of an Explanatory Variable	p. 250
8.6 Testing the Equality of Two Regression Coefficients	p. 254
Example 8.2: The Cubic Cost Function Revisited	p. 255
8.7 Restricted Least Squares: Testing Linear Equality Restrictions	p. 256
The t Test Approach	p. 256
The F Test Approach: Restricted Least Squares	p. 257
Example 8.3: The Cobb-Douglas Production Function for Taiwanese Agricultural Sector, 1958-1972	p. 259
General F Testing	p. 260
8.8 Comparing Two Regressions: Testing for Structural Stability of Regression Models	p. 262
8.9 Testing the Functional Form of Regression: Choosing between Linear and Log-Linear Regression Models	p. 265
Example 8.5: The Demand for Roses	p. 266
8.10 Prediction with Multiple Regression	p. 267
8.11 The Troika of Hypothesis Tests: The Likelihood Ratio (LR), Wald (W), and Lagrange Multiplier (LM) Tests	p. 268
8.12 Summary and Conclusions	p. 269
The Road Ahead	p. 269
Exercises	p. 270
Questions	p. 270
Problems	p. 273
Appendix 8A	p. 280
Likelihood Ratio (LR) Test	p. 280
9 The Matrix Approach to Linear Regression Model	p. 282
9.1 The k-Variable Linear Regression Model	p. 282
9.2 Assumptions of the Classical Linear Regression Model in Matrix Notation	p. 284
9.3 OLS Estimation	p. 287
An Illustration	p. 289
Variance-Covariance Matrix of B	p. 290
Properties of OLS Vector B	p. 291
9.4 The Coefficient of Determination R2 in Matrix Notation	p. 292
9.5 The Correlation Matrix	p. 292
9.6 Hypothesis Testing about Individual Regression Coefficients in Matrix Notation	p. 293
9.7 Testing the Overall Significance of Regression: Analysis of Variance in Matrix Notation	p. 294
9.8 Testing Linear Restrictions: General F Testing Using Matrix Notation	p. 295
9.9 Prediction Using Multiple Regression: Matrix Formulation	p. 296
Mean Prediction	p. 296
Individual Prediction	p. 296
Variance of Mean Prediction	p. 297
Variance of Individual Prediction	p. 298
9.10 Summary of the Matrix Approach: An Illustrative Example	p. 298
9.11 Summary and Conclusions	p. 303
Exercises	p. 304
Appendix 9A	p. 309
9A.1 Derivation of k Normal or Simultaneous Equations	p. 309
9A.2 Matrix Derivation of Normal Equations	p. 310
9A.3 Variance-Covariance Matrix of B	p. 310
9A.4 Blue Property of OLS Estimators	p. 311
Part 2 Relaxing the Assumptions of the Classical Model
10 Multicollinearity and Micronumerosity	p. 319
10.1 The Nature of Multicollinearity	p. 320
10.2 Estimation in the Presence of Perfect Multicollinearity	p. 323
10.3 Estimation in the Presence of "High" but "Imperfect" Multicollinearity	p. 325
10.4 Multicollinearity: Much Ado about Nothing? Theoretical Consequences of Multicollinearity	p. 325
10.5 Practical Consequences of Multicollinearity	p. 327
Large Variances and Covariances of OLS Estimators	p. 328
Wider Confidence Intervals	p. 329
"Insignificant" t Ratios	p. 330
A High R2 but Few Significant t Ratios	p. 330
Sensitivity of OLS Estimators and Their Standard Errors to Small Changes in Data	p. 331
Consequences of Micronumerosity	p. 332
10.6 An Illustrative Example: Consumption Expenditure in Relation to Income and Wealth	p. 332
10.7 Detection of Multicollinearity	p. 335
10.8 Remedial Measures	p. 339
10.9 Is Multicollinearity Necessarily Bad? Maybe Not If the Objective Is Prediction Only	p. 344
10.10 Summary and Conclusions	p. 345
Exercises	p. 346
Questions	p. 346
Problems	p. 351
11 Heteroscedasticity	p. 355
11.1 The Nature of Heteroscedasticity	p. 355
11.2 OLS Estimation in the Presence of Heteroscedasticity	p. 359
11.3 The Method of Generalized Least Squares (GLS)	p. 362
Difference between OLS and GLS	p. 364
11.4 Consequences of Using OLS in the Presence of Heteroscedasticity	p. 365
OLS Estimation Allowing for Heteroscedasticity	p. 365
OLS Estimation Disregarding Heteroscedasticity	p. 366
11.5 Detection of Heteroscedasticity	p. 367
Informal Methods	p. 368
Formal Methods	p. 369
11.6 Remedial Measures	p. 381
When oi2 Is Known: The Method of Weighted Least Squares	p. 381
When o12 Is Not Known	p. 382
11.7 A Concluding Example	p. 387
11.8 Summary and Conclusions	p. 389
Exercises	p. 390
Questions	p. 390
Problems	p. 392
Appendix 11A	p. 398
11A.1 Proof of Equation (11.2.2)	p. 398
11A.2 The Method of Weighted Least Squares	p. 399
12 Autocorrelation	p. 400
12.1 The Nature of the Problem	p. 400
12.2 OLS Estimation in the Presence of Autocorrelation	p. 406
12.3 The BLUE Estimator in the Presence of Autocorrelation	p. 409
12.4 Consequences of Using OLS in the Presence of Autocorrelation	p. 410
OLS Estimation Allowing for Autocorrelation	p. 410
OLS Estimation Disregarding Autocorrelation	p. 411
12.5 Detecting Autocorrelation	p. 415
Graphical Method	p. 415
The Runs Test	p. 419
Durbin-Watson d Test	p. 420
Additional Tests of Autocorrelation	p. 425
12.6 Remedial Measures	p. 426
When the Structure of Autocorrelation Is Known	p. 427
When p Is Not Known	p. 428
12.7 An Illustrative Example: The Relationship between Help-Wanted Index and the Unemployment Rate, United States: Comparison of the Methods	p. 433
12.8 Autoregressive Conditional Heteroscedasticity (ARCH) Model	p. 436
What to Do If ARCH Is Present?	p. 438
A Word on the Durbin-Watson d Statistic and the ARCH Effect	p. 438
12.9 Summary and Conclusions	p. 439
Exercises	p. 440
Questions	p. 440
Problems	p. 446
Appendix 12A	p. 449
12A.1 TSP Output of United States Wages (Y)-Productivity (X) Regression, 1960-1991	p. 449
13 Econometric Modeling I: Traditional Econometric Methodology	p. 452
13.1 The Traditional View of Econometric Modeling: Average Economic Regression (AER)	p. 452
13.2 Types of Specification Errors	p. 455
13.3 Consequences of Specification Errors	p. 456
Omitting a Relevant Variable (Underfitting a Model)	p. 456
Inclusion of an Irrelevant Variable (Overfitting a Model)	p. 458
13.4 Tests of Specification Errors	p. 459
Detecting the Presence of Unnecessary Variables	p. 460
Tests for Omitted Variables and Incorrect Functional Form	p. 461
13.5 Errors of Measurement	p. 467
Errors of Measurement in the Dependent Variable Y	p. 468
Errors of Measurement in the Explanatory Variable X	p. 469
An Example	p. 470
Measurement Errors in the Dependent Variable Y Only	p. 471
Errors of Measurement in X	p. 472
13.6 Summary and Conclusions	p. 472
Exercises	p. 473
Questions	p. 473
Problems	p. 476
Appendix 13A	p. 477
13A.1 The Consequences of Including an Irrelevant Variable: The Unbiasedness Property	p. 477
13A.2 Proof of (13.5.10)	p. 478
14 Econometric Modeling II: Alternative Econometric Methodologies	p. 480
14.1 Learner's Approach to Model Selection	p. 481
14.2 Hendry's Approach to Model Selection	p. 485
14.3 Selected Diagnostic Tests: General Comments	p. 486
14.4 Tests of Nonnested Hypothesis	p. 487
The Discrimination Approach	p. 487
The Discerning Approach	p. 488
14.5 Summary and Conclusions	p. 494
Exercises	p. 494
Questions	p. 494
Problems	p. 495
Part 3 Topics in Econometrics
15 Regression on Dummy Variables	p. 499
15.1 The Nature of Dummy Variables	p. 499
Example 15.1: Professor's Salary by Sex	p. 500
15.2 Regression on One Quantitative Variable and One Qualitative Variable with Two Classes, or Categories	p. 502
Example 15.2: Are Inventories Sensitive to Interest Rates?	p. 505
15.3 Regression on One Quantitative Variable and One Qualitative Variable with More than Two Classes	p. 505
15.4 Regression on One Quantitative Variable and Two Qualitative Variables	p. 507
15.5 Example 15.3: The Economics of "Moonlighting"	p. 508
15.6 Testing for Structural Stability of Regression Models: Basic Ideas	p. 509
Example 15.4: Savings and Income, United Kingdom, 1946-1963	p. 509
15.7 Comparing Two Regressions: The Dummy Variable Approach	p. 512
15.8 Comparing Two Regressions: Further Illustration	p. 514
Example 15.5: The Behavior of Unemployment and Unfilled Vacancies: Great Britain, 1958-1971	p. 514
15.9 Interaction Effects	p. 516
15.10 The Use of Dummy Variables in Seasonal Analysis	p. 517
Example 15.6: Profits-Sales Behavior in U.S. Manufacturing	p. 517
15.11 Piecewise Linear Regression	p. 519
Example 15.7: Total Cost in Relation to Output	p. 521
15.12 The Use of Dummy Variables in Combining Time Series and Cross-Sectional Data	p. 522
Pooled Regression: Pooling Time Series and Cross-Sectional Data	p. 522
Example 15.8: Investment Functions for General Motors and Westinghouse Companies	p. 524
15.13 Some Technical Aspects of Dummy Variable Technique	p. 525
The Interpretation of Dummy Variables in Semilogarithmic Regressions	p. 525
Example 15.9: Semilogarithmic Regression with Dummy Variable	p. 525
Another Method of Avoiding the Dummy Variable Trap	p. 526
Dummy Variables and Heteroscedasticity	p. 527
Dummy Variables and Autocorrelation	p. 527
15.14 Topics for Further Study	p. 528
15.15 Summary and Conclusions	p. 529
Exercises	p. 530
Questions	p. 530
Problems	p. 535
Appendix 15A	p. 538
15A.1 Data Matrix for Regression (15.8.2)	p. 538
15A.2 Data Matrix for Regression (15.10.2)	p. 539
16 Regression on Dummy Dependent Variable: The LPM, Logit, Probit, and Tobit Models	p. 540
16.1 Dummy Dependent Variable	p. 540
16.2 The Linear Probability Model (LPM)	p. 541
16.3 Problems in Estimation of LPM	p. 542
Nonnormality of the Disturbances ui	p. 542
Heteroscedastic Variances of the Disturbances	p. 543
Nonfulfillment of 0 [= E(Yi\X) [= 1	p. 544
Questionable Value of R2 as a Measure of Goodness of Fit	p. 545
16.4 LPM: A Numerical Example	p. 546
16.5 Applications of LPM	p. 548
Example 16.1: Cohen-Rea-Lerman study	p. 548
Example 16.2: Predicting a Bond Rating	p. 551
Example 16.3: Predicting Bond Defaults	p. 552
16.6 Alternatives to LPM	p. 552
16.7 The Logit Model	p. 554
16.8 Estimation of the Logit Model	p. 556
16.9 The Logit Model: A Numerical Example	p. 558
16.10 The Logit Model: Illustrative Examples	p. 561
Example 16.4: "An Application of Logit Analysis to Prediction of Merger Targets"	p. 561
Example 16.5: Predicting a Bond Rating	p. 562
16.11 The Probit Model	p. 563
16.12 The Probit Model: A Numerical Example	p. 567
Logit versus Probit	p. 567
Comparing Logit and Probit Estimates	p. 568
The Marginal Effect of a Unit Change in the Value of a Regressor	p. 569
16.13 The Probit Model: Example 16.5	p. 569
16.14 The Tobit Model	p. 570
16.15 Summary and Conclusions	p. 575
Exercises	p. 576
Questions	p. 576
Problems	p. 578
17 Dynamic Econometric Model: Autoregressive and Distributed-Lag Models	p. 584
17.1 The Role of "Time," or "Lag," in Economics	p. 585
17.2 The Reasons for Lags	p. 589
17.3 Estimation of Distributed-Lag Models	p. 590
Ad Hoc Estimation of Distributed-Lag Models	p. 590
17.4 The Koyck Approach to Distributed-Lag Models	p. 592
The Median Lag	p. 595
The Mean Lag	p. 595
17.5 Rationalization of the Koyck Model: The Adaptive Expectations Model	p. 596
17.6 Another Rationalization of the Koyck Model: The Stock Adjustment, or Partial Adjustment, Model	p. 599
17.7 Combination of Adaptive Expectations and Partial Adjustment Models	p. 601
17.8 Estimation of Autoregressive Models	p. 602
17.9 The Method of Instrumental Variables (IV)	p. 604
17.10 Detecting Autocorrelation in Autoregressive Models: Durbin h Test	p. 605
17.11 A Numerical Example: The Demand for Money in India	p. 607
17.12 Illustrative Examples	p. 609
Example 17.7: The Fed and the Real Rate of Interest	p. 609
Example 17.8: The Short- and Long-Run Aggregate Consumption Functions for the United States, 1946-1972	p. 611
17.13 The Almon Approach to Distributed-Lag Models: The Almon or Polynomial Distributed Lag (PDL)	p. 612
17.14 Causality in Economics: The Granger Test	p. 620
The Granger Test	p. 620
Empirical Results	p. 622
17.15 Summary and Conclusions	p. 624
Exercises	p. 624
Questions	p. 624
Problems	p. 630
Part 4 Simultaneous-Equation Models
18 Simultaneous-Equation Models	p. 635
18.1 The Nature of Simultaneous-Equation Models	p. 635
18.2 Examples of Simultaneous-Equation Models	p. 636
Example 18.1: Demand-and-Supply Model	p. 636
Example 18.2: Keynesian Model of Income Determination	p. 638
Example 18.3: Wage-Price Models	p. 639
Example 18.4: The IS Model of Macroeconomics	p. 639
Example 18.5: The LM Model	p. 640
Example 18.6: Econometric Models	p. 641
18.3 The Simultaneous-Equation Bias: Inconsistency of OLS Estimators	p. 642
18.4 The Simultaneous-Equation Bias: A Numerical Example	p. 645
18.5 Summary and Conclusions	p. 647
Exercises	p. 648
Questions	p. 648
Problems	p. 651
19 The Identification Problem	p. 653
19.1 Notations and Definitions	p. 653
19.2 The Identification Problem	p. 657
Underidentification	p. 657
Just, or Exact, Identification	p. 660
Overidentification	p. 663
19.3 Rules for Identification	p. 664
The Order Condition of Identifiability	p. 665
The Rank Condition of Identifiability	p. 666
19.4 A Test of Simultaneity	p. 669
Hausman Specification Test	p. 670
Example 19.5: Pindyck-Rubinfeld Model of Public Spending	p. 671
19.5 Tests for Exogeneity	p. 672
A Note on Causality and Exogeneity	p. 673
19.6 Summary and Conclusions	p. 673
Exercises	p. 674
20 Simultaneous-Equation Methods	p. 678
20.1 Approaches to Estimation	p. 678
20.2 Recursive Models and Ordinary Least Squares	p. 680
20.3 Estimation of a Just Identified Equation: The Method of Indirect Least Squares (ILS)	p. 682
An Illustrative Example	p. 683
Properties of ILS Estimators	p. 686
20.4 Estimation of an Overidentified Equation: The Method of Two-Stage Least Squares (2SLS)	p. 686
20.5 2SLS: A Numerical Example	p. 690
20.6 Illustrative Examples	p. 693
Example 20.1: Advertising, Concentration, and Price Margins	p. 693
Example 20.2: Klein's Model I	p. 694
Example 20.3: The Capital Asset Pricing Model Expressed as a Recursive System	p. 694
Example 20.4: Revised Form of St. Louis Model	p. 697
20.7 Summary and Conclusions	p. 699
Exercises	p. 700
Questions	p. 700
Problems	p. 703
Appendix 20A	p. 704
20A.1 Bias in the Indirect Least-Squares Estimators	p. 704
20A.2 Estimation of Standard Errors of 2SLS Estimators	p. 705
Part 5 Time Series Econometrics
21 Time Series Econometrics I: Stationarity, Unit Roots, and Cointegration	p. 709
21.1 A Look at Selected U.S. Economic Time Series	p. 710
21.2 Stationary Stochastic Process	p. 710
21.3 Test of Stationarity Based on Correlogram	p. 714
21.4 The Unit Root Test of Stationarity	p. 718
Is the U.S. GDP Time Series Stationary?	p. 720
Is the First-Differenced GDP Series Stationary?	p. 721
21.5 Trend-Stationary (TS) and Difference-Stationary (DS) Stochastic Process	p. 722
21.6 Spurious Regression	p. 724
21.7 Cointegration	p. 725
Engle-Granger (EG) or Augmented Engle-Granger (AEG) Test	p. 726
Cointegrating Regression Durbin-Watson (CRDW) Test	p. 727
21.8 Cointegration and Error Correction Mechanism (ECM)	p. 728
21.9 Summary and Conclusions	p. 729
Exercises	p. 730
Questions	p. 730
Problems	p. 731
Appendix 21A	p. 732
21A.1 A Random Walk Model	p. 732
22 Time Series Econometrics II: Forecasting with ARIMA and VAR Models	p. 734
22.1 Approaches to Economic Forecasting	p. 734
22.2 AR, MA, and ARIMA Modeling of Time Series Data	p. 736
An Autoregressive (AR) Process	p. 736
A Moving Average (MA) Process	p. 737
An Autoregressive and Moving Average (ARMA) Process	p. 737
An Autoregressive Integrated Moving Average (ARIMA) Process	p. 737
22.3 The Box-Jenkins (BJ) Methodology	p. 738
22.4 Identification	p. 739
22.5 Estimation of the ARIMA Model	p. 742
22.6 Diagnostic Checking	p. 743
22.7 Forecasting	p. 744
22.8 Further Aspects of the BJ Methodology	p. 745
22.9 Vector Autoregression (VAR)	p. 746
Estimation of VAR	p. 746
Forecasting with VAR	p. 747
Some Problems with VAR Modeling	p. 747
An Application of VAR: A VAR Model of the Texas Economy	p. 750
22.10 Summary and Conclusions	p. 752
Exercises	p. 753
Questions	p. 753
Problems	p. 753
Appendixes
A A Review of Some Statistical Concepts	p. 755
B Rudiments of Matrix Algebra	p. 791
C A List of Statistical Computer Packages	p. 804
D Statistical Tables	p. 807
Table D.1 Areas under the Standardized Normal Distribution	p. 808
Table D.2 Percentage Points of the t Distribution	p. 809
Table D.3 Upper Percentage Points of the F Distribution	p. 810
Table D.4 Upper Percentage Points of the X2 Distribution	p. 816
Table D.5 Durbin-Watson d Statistic: Significant Points of dL and dU at 0.05 and 0.01 Levels of Significance	p. 818
Table D.6 Critical Values of Runs in the Runs Test	p. 822
Selected Bibliography	p. 824
Indexes
Name Index	p. 827
Subject Index	p. 831

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