Essentials of Econometrics / Edition 2

Essentials of Econometrics / Edition 2

by Damodar N. Gujarati
     
 

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ISBN-10: 0073032654

ISBN-13: 9780073032658

Pub. Date: 10/28/1998

Publisher: McGraw-Hill Companies, The

A more intuitive,less comprehensive introductory Econometrics text than Gujarati's Basic Econometrics,2/e,which is the market leader. This text will cover fewer topics in a more patient manner than Basic Econometrics. It also will begin with two chapters reviewing statistics.

Overview

A more intuitive,less comprehensive introductory Econometrics text than Gujarati's Basic Econometrics,2/e,which is the market leader. This text will cover fewer topics in a more patient manner than Basic Econometrics. It also will begin with two chapters reviewing statistics.

Product Details

ISBN-13:
9780073032658
Publisher:
McGraw-Hill Companies, The
Publication date:
10/28/1998
Edition description:
Older Edition
Pages:
534

Table of Contents

Preface xxiii
CHAPTER 1 THE NATURE AND SCOPE OF ECONOMETRICS
1(20)
1.1 WHAT IS ECONOMETRICS?
1(1)
1.2 WHY STUDY ECONOMETRICS?
2(1)
1.3 THE METHODOLOGY OF ECONOMETRICS
3(10)
Creating a Statement of Theory or Hypothesis
4(1)
Collecting Data
4(2)
Specifying the Mathematical Model of Labor Force Participation
6(1)
Specifying the Statistical, or Econometric, Model of Labor Force Participation
7(2)
Estimating the Parameters of the Chosen Econometric Model
9(1)
Checking for Model Adequacy: Model Specification Testing
10(1)
Testing the Hypothesis Derived from the Model
11(1)
Using the Model for Prediction or Forecasting
12(1)
1.4 THE ROAD AHEAD
13(1)
Key Terms and Concepts
14(1)
QUESTIONS
14(1)
PROBLEMS
14(2)
APPENDIX 1A ECONOMIC DATA ON THE WORLD WIDE WEB
16(5)
PART I BASICS OF PROBABILITY AND STATISTICS 21(102)
CHAPTER 2 A REVIEW OF BASIC STATISTICAL CONCEPTS
21(44)
2.1 SOME NOTATION
21(2)
The Summation Notation
21(1)
Properties of the Summation Operator
22(1)
2.2 EXPERIMENT, SAMPLE SPACE, SAMPLE POINT, AND EVENTS
23(1)
Experiment
23(1)
Sample Space or Population
23(1)
Sample Point
24(1)
Events
24(1)
2.3 RANDOM VARIABLES
24(1)
2.4 PROBABILITY
25(5)
Probability of an Event: The Classical or A Priori Definition
25(1)
Relative Frequency or Empirical Definition of Probability
26(4)
Probability of Random Variables
30(1)
2.5 RANDOM VARIABLES AND PROBABILITY DISTRIBUTION FUNCTION (PDF)
30(4)
PDF of a Discrete Random Variable
31(1)
PDF of a Continuous Random Variable
32(1)
Cumulative Distribution Function (CDF)
33(1)
2.6 MULTIVARIATE PROBABILITY DENSITY FUNCTIONS
34(5)
Marginal Probability Density Function
36(1)
Conditional Probability Density Function
37(1)
Statistical Independence
38(1)
2.7 CHARACTERISTICS OF PROBABILITY DISTRIBUTIONS
39(13)
Expected Value: A Measure of Central Tendency
39(3)
Variance: A Measure of Dispersion
42(3)
Covariance
45(1)
Correlation Coefficient
46(3)
Conditional Expectation
49(3)
2.8 FROM POPULATION TO THE SAMPLE
52(5)
Sample Mean
53(1)
Sample Variance
54(1)
Sample Covariance
55(1)
Sample Correlation Coefficient
56(1)
Sample Skewness and Kurtosis
56(1)
2.9 SUMMARY
57(1)
Key Terms and Concepts
58(1)
REFERENCES
58(1)
QUESTIONS
59(1)
PROBLEMS
60(5)
CHAPTER 3 SOME IMPORTANT PROBABILITY DISTRIBUTIONS
65(30)
3.1 THE NORMAL DISTRIBUTION
66(8)
Properties of the Normal Distribution
66(2)
The Standard Normal Distribution
68(4)
Random Sampling from a Normal Population
72(1)
Bootstrap Sampling
72(2)
3.2 THE SAMPLING, OR PROBABILITY, DISTRIBUTION OF THE SAMPLE MEAN X
74(7)
The Central Limit Theorem (CLT)
78(3)
3.3 THE CHI-SQUARE (X)(2) DISTRIBUTION
81(2)
Properties of the Chi-square Distribution
82(1)
3.4 THE t DISTRIBUTION
83(4)
Properties of the t Distribution
84(3)
3.5 THE F DISTRIBUTION
87(3)
Properties of the F Distribution
88(2)
3.6 RELATIONSHIPS AMONG THE t, F, AND X(2) AND THE NORMAL DISTRIBUTIONS
90(1)
3.7 SUMMARY
91(1)
Key Terms and Concepts
91(1)
QUESTIONS
92(1)
PROBLEMS
92(3)
CHAPTER 4 STATISTICAL INFERENCE: ESTIMATION AND HYPOTHESIS TESTING
95(28)
4.1 THE MEANING OF STATISTICAL INFERENCE
95(1)
4.2 ESTIMATION AND HYPOTHESIS TESTING: TWIN BRANCHES OF STATISTICAL INFERENCE
96(1)
4.3 ESTIMATION OF PARAMETERS
97(4)
4.4 PROPERTIES OF POINT ESTIMATORS
101(4)
Linearity
101(1)
Unbiasedness
101(1)
Efficiency
102(1)
Best Linear Unbiased Estimator (BLUE)
103(1)
Consistency
104(1)
4.5 STATISTICAL INFERENCE: HYPOTHESIS TESTING
105(12)
The Confidence Interval Approach to Hypothesis Testing
106(1)
Type I and Type II Errors: A Digression
107(2)
The Test of Significance Approach to Hypothesis Testing
109(4)
A Word on Choosing the Level of Significance, Alpha, and the p Value
113(1)
The X(2) and F Tests of Significance
114(3)
4.6 SUMMARY
117(1)
Key Terms and Concepts
117(1)
QUESTIONS
117(1)
PROBLEMS
118(5)
PART II THE LINEAR REGRESSION MODEL 123(190)
CHAPTER 5 BASIC IDEAS OF LINEAR REGRESSION: THE TWO-VARIABLE MODEL
123(29)
5.1 THE MEANING OF REGRESSION
123(1)
5.2 THE POPULATION REGRESSION FUNCTION (PRF): A HYPOTHETICAL EXAMPLE
124(3)
5.3 STOCHASTIC SPECIFICATION OF THE POPULATION REGRESSION FUNCTION (PRF)
127(2)
5.4 THE NATURE OF THE STOCHASTIC ERROR TERM
129(1)
5.5 THE SAMPLE REGRESSION FUNCTION (SRF)
129(3)
5.6 THE SPECIAL MEANING OF THE TERM "LINEAR" REGRESSION
132(2)
Linearity in the Variables
133(1)
Linearity in the Parameters
134(1)
5.7 TWO-VARIABLE VERSUS MULTIPLE LINEAR REGRESSION
134(1)
5.8 ESTIMATION OF PARAMETERS: THE METHOD OF ORDINARY LEAST SQUARES (OLS)
135(5)
The Method of Ordinary Least Squares (OLS)
135(1)
A Numerical Example: The Demand for Widgets
136(3)
Interpretation of the Estimated Demand Function for Widgets
139(1)
5.9 SOME ILLUSTRATIVE EXAMPLES
140(3)
A Word about Computations
143(1)
5.10 SUMMARY
143(1)
Key Terms and Concepts
144(1)
QUESTIONS
144(1)
PROBLEMS
145(5)
APPENDIX 5A DERIVATION OF LEAST-SQUARES ESTIMATES
150(2)
CHAPTER 6 THE TWO-VARIABLE MODEL: HYPOTHESIS TESTING
152(45)
6.1 THE CLASSICAL LINEAR REGRESSION MODEL (CLRM)
153(3)
6.2 VARIANCES AND STANDARD ERRORS OF ORDINARY LEAST SQUARES (OLS) ESTIMATORS
156(2)
Variances and Standard Errors of the Widget Example
157(1)
Summary of Demand Function for Widgets
157(1)
6.3 WHY ORDINARY LEAST SQUARES (OLS)? THE PROPERTIES OF OLS ESTIMATORS
158(3)
Monte Carlo Experiment
159(2)
6.4 THE SAMPLING, OR PROBABILITY, DISTRIBUTIONS OF OLS ESTIMATORS
161(1)
6.5 HYPOTHESIS TESTING
162(8)
Testing H(o): B(2) = O versus H(1): B(2) # O: The Confidence Interval Approach
164(2)
The Test of Significance Approach to Hypothesis Testing
166(1)
The Demand for Widgets Continued
167(2)
The X(2) Test of Significance
169(1)
6.6 HOW GOOD IS THE FITTED REGRESSION LINE: THE COEFFICIENT OF DETERMINATION, r(2)
170(4)
Formulas to Compute r(2)
173(1)
r(2) for the Widget Example
173(1)
The Coefficient of Correlation, r
173(1)
6.7 REPORTING THE RESULTS OF REGRESSION ANALYSIS
174(1)
6.8 NORMALITY TESTS
175(4)
Histograms of Residuals
176(1)
Normal Probability Plot
176(2)
Jarque-Bera Test
178(1)
6.9 A WORD ON COMPUTATION: COMPUTER REGRESSION PACKAGES
179(1)
6.10 AN ILLUSTRATIVE EXAMPLE: THE U.S. EXPENDITURE ON FOREIGN IMPORTS
179(5)
Interpretation of Regression Results
183(1)
Statistical Significance of Results
183(1)
6.11 A WORD ABOUT FORECASTING
184(3)
6.12 FURTHER NUMERICAL EXAMPLES
187(2)
6.13 SUMMARY
189(1)
Key Terms and Concepts
189(1)
QUESTIONS
189(2)
PROBLEMS
191(6)
CHAPTER 7 MULTIPLE REGRESSION: ESTIMATION AND HYPOTHESIS TESTING
197(42)
7.1 THE THREE-VARIABLE LINEAR REGRESSION MODEL
198(3)
The Meaning of Partial Regression Coefficient
200(1)
7.2 ASSUMPTIONS OF MULTIPLE LINEAR REGRESSION MODEL
201(2)
7.3 ESTIMATION OF PARAMETERS OF MULTIPLE REGRESSION
203(3)
Ordinary Least Squares (OLS) Estimators
203(1)
Variances and Standard Errors of OLS Estimators
204(1)
Properties of OLS Estimattors of Multiple Regression
205(1)
7.4 MORTGAGE DEBT OUTSTANDING: UNITED STATES, 1980-1995-AN ILLUSTRATIVE EXAMPLE
206(2)
Regression Results
207(1)
Interpretation of Regression Results
207(1)
7.5 GOODNESS OF FIT OF ESTIMATED MULTIPLE REGRESSION: MULTIPLE COEFFICIENT OF DETERMINATION, R(2)
208(1)
7.6 HYPOTHESIS TESTING IN A MULTIPLE REGRESSION: GENERAL COMMENTS
209(1)
7.7 TESTING HYPOTHESES ABOUT INDIVIDUAL PARTIAL REGRESSION COEFFICIENTS
210(3)
The Test of Significance Approach
211(1)
The Confidence Interval Approach to Hypothesis Testing
212(1)
7.8 TESTING THE JOINT HYPOTHESIS THAT B(2) = B(3) = O OR R(2) = O
213(5)
An Important Relationship between F and R(2)
217(1)
7.9 TWO-VARIABLE REGRESSION IN CONTEXT OF MULTIPLE REGRESSION: INTRODUCTION TO SPECIFICATION BIAS
218(1)
7.10 COMPARING TWO R(2) VALUES: THE ADJUSTED R(2)
219(1)
7.11 WHEN TO ADD AN ADDITIONAL EXPLANATORY VARIABLE TO THE MODEL
220(1)
7.12 TESTING FOR STRUCTURAL STABILITY OF REGRESSION MODELS: THE CHOW TEST
221(3)
7.13 ILLUSTRATIVE EXAMPLES
224(5)
Discussion of Regression Results
225(4)
7.14 SUMMARY
229(1)
Key Terms and Concepts
230(1)
QUESTIONS
230(1)
PROBLEMS
231(5)
APPENDIX 7A.1 DERIVATIONS OF OLS ESTIMATORS GIVEN IN EQUATION (7.20) TO (7.22)
236(1)
APPENDIX 7A.2 DERIVATION OF EQUATION (7.31)
236(1)
APPENDIX 7A.3 DERIVATION OF EQUATION (7.53)
237(1)
APPENDIX 7A.4 EVIEWS' COMPUTER OUTPUT OF THE MORTGAGE DEBT OUTSTANDING EXAMPLE
238(1)
CHAPTER 8 FUNCTIONAL FORMS OF REGRESSION MODELS
239(36)
8.1 HOW TO MEASURE ELASTICITY: THE LOG-LINEAR MODEL
240(4)
Hypothesis Testing in Log-Linear Models
244(1)
8.2 COMPARING LINEAR AND LOG-LINEAR REGRESSION MODELS
244(3)
8.3 MULTIPLE LOG-LINEAR REGRESSION MODLES
247(3)
8.4 HOW TO MEASURE THE GROWTH RATE: THE SEMILOG MODEL
250(5)
Instantaneous versus Compound Rate of Growth
253(1)
The Linear Trend Model
254(1)
8.5 THE LIN-LOG MODEL: WHEN THE EXPLANATORY VARIABLE IS LOGARITHMIC
255(1)
8.6 RECIPROCAL MODELS
256(5)
8.7 POLYNOMIAL REGRESSION MODELS
261(1)
8.8 SUMMARY OF FUNCTIONAL FORMS
262(1)
8.9 SUMMARY
263(1)
Key Terms and Concepts
264(1)
QUESTIONS
264(2)
PROBLEMS
266(6)
APPENDIX 8A LOGARITHMS
272(3)
CHAPTER 9 REGRESSION ON DUMMY EXPLANATORY VARIABLES
275(38)
9.1 THE NATURE OF DUMMY VARIABLES
275(4)
9.2 REGRESSION WITH ONE QUANTITATIVE VARIABLE AND ONE QUALITATIVE VARIABLE WITH TWO CATEGORIES
279(5)
9.3 REGRESSION ON A QUANTITATIVE VARIABLE AND A QUALITATIVE VARIABLE WITH MORE THAN TWO CLASSES OR CATEGORIES
284(3)
9.4 REGRESSION ON ONE QUANTITATIVE VARIABLE AND TWO QUALITATIVE VARIABLES
287(1)
9.5 A GENERALIZATION
288(2)
9.6 STRUCTURAL STABILITY OF REGRESSION MODELS: THE DUMMY VARIABLE APPROACH
290(6)
9.7 THE USE OF DUMMY VARIABLES IN SEASONAL ANALYSIS
296(4)
9.8 SUMMARY
300(1)
Key Terms and Concepts
301(1)
QUESTIONS
301(2)
PROBLEMS
303(10)
PART III REGRESSION ANALYSIS IN PRACTICE 313(160)
CHAPTER 10 MULTICOLLINEARITY: WHAT HAPPENS IF EXPLANATORY VARIABLES ARE CORRELATED
313(28)
10.1 THE NATURE OF MULTICOLLINEARITY: THE CASE OF PERFECT MULTICOLLINEARITY
314(2)
10.2 THE CASE OF NEAR, OR IMPERFECT, MULTICOLLINEARITY
316(2)
10.3 THEORETICAL CONSEQUENCES OF MULTICOLLINEARITY
318(2)
10.4 PRACTICAL CONSEQUENCES OF MULTICOLLINEARITY
320(2)
10.5 DETECTION OF MULTICOLLINEARITY
322(4)
10.6 IS MULTICOLLINEARITY NECESSARILY BAD?
326(1)
10.7 AN EXTENDED EXAMPLE: THE DEMAND FOR CHICKENS IN THE UNITED STATES, 1960 TO 1982
327(4)
Collinearity Diagnostics for the Demand Function for Chickens
329(2)
10.8 WHAT TO DO WITH MULTICOLLINEARITY: REMEDIAL MEASURES
331(4)
10.9 SUMMARY
335(1)
Key Terms and Concepts
336(1)
QUESTIONS
336(2)
PROBLEMS
338(3)
CHAPTER 11 HETEROSCEDASTICITY: WHAT HAPPENS IF THE ERROR VARIANCE IS NONCONSTANT
341(36)
11.1 THE NATURE OF HETEROSCEDASTICITY
341(7)
11.2 CONSEQUENCES OF HETEROSCEDASTICITY
348(2)
11.3 DETECTION OF HETEROSCEDASTICITY: HOW DO WE KNOW WHEN THERE IS A HETEROSCEDASTICITY PROBLEM?
350(9)
Nature of the Problem
351(1)
Graphical Examination of Residuals
351(2)
Park Test
353(2)
Glejser Test
355(1)
White's General Heteroscedasticity Test
356(2)
Other Tests of Heteroscedasticity
358(1)
11.4 WHAT TO DO IF HETEROSCEDASTICITY IS OBSERVED: REMEDIAL MEASURES
359(6)
When Sigma(2)(i) Is Known: The Method of Weighted Least Squares (WLS)
359(1)
When True Sigma(2)(I) Is Unknown
360(4)
Respecification of the Model
364(1)
11.5 WHITE'S HETEROSCEDASTICITY-CORRECTED STANDARD ERRORS AND t STATISTICS
365(1)
11.6 SOME CONCRETE EXAMPLES OF HETEROSCEDASTICITY
366(2)
11.7 SUMMARY
368(1)
Key Terms and Concepts
369(1)
QUESTIONS
369(1)
PROBLEMS
370(7)
CHAPTER 12 AUTOCORRELATION: WHAT HAPPENS IF ERROR TERMS ARE CORRELATED
377(28)
12.1 THE NATURE OF AUTOCORRELATION
378(3)
Inertia
379(1)
Model Specification Error(s)
380(1)
The Cobweb Phenomenon
380(1)
Data Manipulation
380(1)
12.2 CONSEQUENCES OF AUTOCORRELATION
381(1)
12.3 DETECTING AUTOCORRELATION
382(9)
The Graphical Method
383(2)
The Runs Test
385(1)
The Durbin-Watson d Test
386(5)
12.4 REMEDIAL MEASURES
391(2)
12.5 HOW TO ESTIMATE p
393(5)
p = 1: The First Difference Method
393(1)
p Estimated from Durbin-Watson d Statistic
394(1)
p Estimated from OLS Residuals, e(t)
395(1)
Other Methods of Estimating p
395(3)
12.6 SUMMARY
398(1)
Key Terms and Concepts
398(1)
QUESTIONS
399(1)
PROBLEMS
400(5)
CHAPTER 13 MODEL SELECTION: CRITERIA AND TESTS
405(26)
13.1 THE ATTRIBUTES OF A GOOD MODEL
406(1)
13.2 TYPES OF SPECIFICATION ERRORS
407(9)
Omitting a Relevant Variable: "Underfitting" a Model
407(4)
Inclusion of Irrelevant Variable: "Overfitting" a Model
411(3)
Incorrect Functional Form
414(2)
13.3 DETECTING SPECIFICATION ERRORS: TESTS OF SPECIFICATION ERRORS
416(7)
Detecting the Presence of Unnecessary Variables
416(2)
Tests for Omitted Variables and Incorrect Functional Forms
418(5)
13.4 MODEL SELECTION CRITERIA FOR FORECASTING PURPOSES
423(1)
13.5 SUMMARY
424(2)
Key Terms and Concepts
425(1)
QUESTIONS
426(1)
PROBLEMS
426(5)
CHAPTER 14 SELECTED TOPICS IN SINGLE EQUATION REGRESSION MODELS
431(42)
14.1 RESTRICTED LEAST SQUARES (RLS)
431(5)
14.2 DYNAMIC ECONOMIC MODELS: AUTOREGRESSIVE AND DISTRIBUTED LAG MODELS
436(5)
Estimation of Distributed Lag Models
439(2)
14.3 THE KOYCK, ADAPTIVE EXPECTATIONS, AND STOCK ADJUSTMENT MODELS APPROACHES TO ESTIMATING DISTRIBUTED LAG MODELS
441(4)
14.4 WHAT HAPPENS WHEN THE DEPENDENT VARIABLE IS DUMMY?
445(4)
14.5 THE LOGIT MODEL
449(6)
Estimation of the Logit Model: Individual Data
449(2)
Logit Estimation of Grouped Data
451(4)
14.6 THE PHENOMENON OF SPURIOUS REGRESSION
455(9)
Test of Nonstationarity: The Unit Root Test
459(1)
Cointegrated Time Series
460(1)
The Random Walk Model
461(3)
14.7 SUMMARY
464(2)
Key Terms and Concepts
465(1)
QUESTIONS
466(1)
PROBLEMS
467(6)
PART IV INTRODUCTION TO SIMULTANEOUS EQUATION MODELS 473(26)
CHAPTER 15 SIMULTANEOUS EQUATION MODELS
473(26)
15.1 THE NATURE OF SIMULTANEOUS EQUATION MODELS
474(2)
15.2 THE SIMULTANEOUS EQUATION BIAS: INCONSISTENCY OF OLS ESTIMATORS
476(2)
15.3 THE METHOD OF INDIRECT LEAST SQUARES
478(1)
15.4 INDIRECT LEAST SQUARES: AN ILLUSTRATIVE EXAMPLE
479(2)
15.5 THE IDENTIFICATION PROBLEM: A ROSE BY ANY OTHER NAME MAY NOT BE A ROSE
481(7)
Underidentification
483(1)
Just or Exact Identification
484(2)
Overidentification
486(2)
15.6 RULES FOR IDENTIFICATION: THE ORDER CONDITION OF IDENTIFICATION
488(1)
15.7 ESTIMATION OF AN OVERIDENTIFIED EQUATION: THE METHOD OF TWO-STAGE LEAST SQUARES
488(2)
15.8 2SLS: A NUMERICAL EXAMPLE
490(2)
15.9 SUMMARY
492(2)
Key Terms and Concepts
494(1)
QUESTIONS
494(1)
PROBLEMS
494(2)
APPENDIX 15A INCONSISTENCY OF OLS ESTIMATORS
496(3)
APPENDIX A: STATISTICAL TABLES 499(18)
ANSWERS TO SELECTED PROBLEMS 517(6)
SELECTED BIBLIOGRAPHY 523(4)
INDICES
Name Index 527(2)
Subject Index 529

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