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Introduction to Econometrics

by ;
Edition:
2nd
ISBN13:

9780321278876

ISBN10:
0321278879
Format:
Hardcover
Pub. Date:
1/1/2007
Publisher(s):
Addison Wesley

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Summary

Introduction to Econometrics, Second Edition, continues its tradition of explaining econometric methods and theory through the use of extensive, fully worked, real-world empirical applications. These examples include ones drawn from the economics of education, racial discrimination in the market for home loans, the demand for cigarettes, and macroeconomic forecasting.

Table of Contents

Preface xxvii
PART ONE Introduction and Review
1(108)
Economic Questions and Data
3(14)
Economic Questions We Examine
4(4)
Question #1: Does Reducing Class Size Improve Elementary School Education?
4(1)
Question #2: Is There Racial Discrimination in the Market for Home Loans?
5(1)
Question #3: How Much Do Cigarette Taxes Reduce Smoking?
5(1)
Question #4: What Will the Rate of Inflation Be Next Year?
6(1)
Quantitative Questions, Quantitative Answers
7(1)
Causal Effects and Idealized Experiments
8(2)
Estimation of Causal Effects
8(1)
Forecasting and Causality
9(1)
Data: Sources and Types
10(7)
Experimental versus Observational Data
10(1)
Cross-Sectional Data
11(1)
Time Series Data
11(2)
Panel Data
13(4)
Review of Probability
17(48)
Random Variables and Probability Distributions
18(5)
Probabilities, the Sample Space, and Random Variables
18(1)
Probability Distribution of a Discrete Random Variable
19(2)
Probability Distribution of a Continuous Random Variable
21(2)
Expected Values, Mean, and Variance
23(6)
The Expected Value of a Random Variable
23(1)
The Standard Deviation and Variance
24(1)
Mean and Variance of a Linear Function of a Random Variable
25(1)
Other Measures of the Shape of a Distribution
26(3)
Two Random Variables
29(10)
Joint and Marginal Distributions
29(1)
Conditional Distributions
30(4)
Independence
34(1)
Covariance and Correlation
34(1)
The Mean and Variance of Sums of Random Variables
35(4)
The Normal, Chi-Squared, Student t, and F Distributions
39(6)
The Normal Distribution
39(4)
The Chi-Squared Distribution
43(1)
The Student t Distribution
44(1)
The F Distribution
44(1)
Random Sampling and the Distribution of the Sample Average
45(3)
Random Sampling
45(1)
The Sampling Distribution of the Sample Average
46(2)
Large-Sample Approximations to Sampling Distributions
48(17)
The Law of Large Numbers and Consistency
49(3)
The Central Limit Theorem
52(11)
Appendix 2.1 Derivation of Results in Key Concept 2.3
63(2)
Review of Statistics
65(44)
Estimation of the Population Mean
66(5)
Estimators and Their Properties
67(1)
Properties of Y
68(2)
The Importance of Random Sampling
70(1)
Hypothesis Tests Concerning the Population Mean
71(10)
Null and Alternative Hypotheses
72(1)
The p-Value
72(2)
Calculating the p-Value When σy Is Known
74(1)
The Sample Variance, Sample Standard Deviation, and Standard Error
75(1)
Calculating the p-Value When σy Is Unknown
76(1)
The t-Statistic
77(1)
Hypothesis Testing with a Prespecified Significance Level
78(2)
One-Sided Alternatives
80(1)
Confidence Intervals for the Population Mean
81(2)
Comparing Means from Different Populations
83(2)
Hypothesis Tests for the Difference Between Two Means
83(1)
Confidence Intervals for the Difference Between Two Population Means
84(1)
Differences-of-Means Estimation of Causal Effects Using Experimental Data
85(3)
The Causal Effect as a Difference of Conditional Expectations
85(2)
Estimation of the Causal Effect Using Differences of Means
87(1)
Using the t-Statistic When the Sample Size Is Small
88(4)
The t-Statistic and the Student t Distribution
88(4)
Use of the Student t Distribution in Practice
92(1)
Scatterplot, the Sample Covariance, and the Sample Correlation
92(17)
Scatterplots
93(1)
Sample Covariance and Correlation
94(11)
Appendix 3.1 The U.S. Current Population Survey
105(1)
Appendix 3.2 Two Proofs That Y Is the Least Squares Estimator of μy
106(1)
Appendix 3.3 A Proof That the Sample Variance Is Consistent
107(2)
PART TWO Fundamentals of Regression Analysis
109(238)
Linear Regression with One Regressor
111(37)
The Linear Regression Model
112(4)
Estimating the Coefficients of the Linear Regression Model
116(7)
The Ordinary Least Squares Estimator
118(2)
OLS Estimates of the Relationship Between Test Scores and the Student-Teacher Ratio
120(1)
Why Use the OLS Estimator?
121(2)
Measures of Fit
123(3)
The R2
123(1)
The Standard Error of the Regression
124(1)
Application to the Test Score Data
125(1)
The Least Squares Assumptions
126(5)
Assumption #1: The Conditional Distribution of ui Given Xi Has a Mean of Zero
126(2)
Assumption #2: (Xi, Yi), i = I, . . . , n Are Independently and Identically Distributed
128(1)
Assumption #3: Large Outliers Are Unlikely
129(1)
Use of the Least Squares Assumptions
130(1)
The Sampling Distribution of the OLS Estimators
131(4)
The Sampling Distribution of the OLS Estimators
132(3)
Conclusion
135(13)
Appendix 4.1 The California Test Score Data Set
143(1)
Appendix 4.2 Derivation of the OLS Estimators
143(1)
Appendix 4.3 Sampling Distribution of the OLS Estimator
144(4)
Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals
148(38)
Testing Hypotheses About One of the Regression Coefficients
149(6)
Two-Sided Hypotheses Concerning β1
149(4)
One-Sided Hypotheses Concerning β1
153(2)
Testing Hypotheses About the Intercept β0
155(1)
Confidence Intervals for a Regression Coefficient
155(3)
Regression When X Is a Binary Variable
158(2)
Interpretation of the Regression Coefficients
158(2)
Heteroskedasticity and Homoskedasticity
160(6)
What Are Heteroskedasticity and Homoskedasticity?
160(3)
Mathematical Implications of Homoskedasticity
163(1)
What Does This Mean in Practice?
164(2)
The Theoretical Foundations of Ordinary Least Squares
166(3)
Linear Conditionally Unbiased Estimators and the Gauss-Markov Theorem
167(1)
Regression Estimators Other Than OLS
168(1)
Using the t-Statistic in Regression When the Sample Size Is Small
169(2)
The t-Statistic and the Student t Distribution
170(1)
Use of the Student t Distribution in Practice
170(1)
Conclusion
171(15)
Appendix 5.1 Formulas for OLS Standard Errors
180(2)
Appendix 5.2 The Gauss-Markov Conditions and a Proof of the Gauss-Markov Theorem
182(4)
Linear Regression with Multiple Regressors
186(34)
Omitted Variable Bias
186(7)
Definition of Omitted Variable Bias
187(2)
A Formula for Omitted Variable Bias
189(2)
Addressing Omitted Variable Bias by Dividing the Data into Groups
191(2)
The Multiple Regression Model
193(3)
The Population Regression Line
193(1)
The Population Multiple Regression Model
194(2)
The OLS Estimator in Multiple Regression
196(4)
The OLS Estimator
197(1)
Application to Test Scores and the Student-Teacher Ratio
198(2)
Measures of Fit in Multiple Regression
200(2)
The Standard Error of the Regression (SER)
200(1)
The R2
200(1)
The ``Adjusted R2''
201(1)
Application to Test Scores
202(1)
The Least Squares Assumptions in Multiple Regression
202(3)
Assumption #1: The Conditional Distribution of ui Given X1i, X2i, . . . , Xki Has a Mean of Zero
203(1)
Assumption #2: (X1i, X2i, . . . , Xki, Yi) i = 1, . . . , n Are i.i.d.
203(1)
Assumption #3: Large Outliers Are Unlikely
203(1)
Assumption #4: No Perfect Multicollinearity
203(2)
The Distribution of the OLS Estimators in Multiple Regression
205(1)
Multicollinearity
206(4)
Examples of Perfect Multicollinearity
206(3)
Imperfect Multicollinearity
209(1)
Conclusion
210(10)
Appendix 6.1 Derivation of Equation (6.1)
218(1)
Appendix 6.2 Distribution of the OLS Estimators When There Are Two Regressors and Homoskedastic Errors
218(2)
Hypothesis Tests and Confidence Intervals in Multiple Regression
220(34)
Hypothesis Tests and Confidence Intervals for a Single Coefficient
221(4)
Standard Errors for the OLS Estimators
221(1)
Hypothesis Tests for a Single Coefficient
221(2)
Confidence Intervals for a Single Coefficient
223(1)
Application to Test Scores and the Student-Teacher Ratio
223(2)
Tests of Joint Hypotheses
225(7)
Testing Hypotheses on Two or More Coefficients
225(2)
The F-Statistic
227(2)
Application to Test Scores and the Student-Teacher Ratio
229(1)
The Homoskedasticity-Only F-Statistic
230(2)
Testing Single Restrictions Involving Multiple Coefficients
232(2)
Confidence Sets for Multiple Coefficients
234(1)
Model Specification for Multiple Regression
235(4)
Omitted Variable Bias in Multiple Regression
236(1)
Model Specification in Theory and in Practice
236(1)
Interpreting the R2 and the Adjusted R2 in Practice
237(2)
Analysis of the Test Score Data Set
239(5)
Conclusion
244(10)
Appendix 7.1 The Bonferroni Test of a Joint Hypotheses
251(3)
Nonlinear Regression Functions
254(58)
A General Strategy for Modeling Nonlinear Regression Functions
256(8)
Test Scores and District Income
256(4)
The Effect on Y of a Change in X in Nonlinear Specifications
260(4)
A General Approach to Modeling Nonlinearities Using Multiple Regression
264(1)
Nonlinear Functions of a Single Independent Variable
264(13)
Polynomials
265(2)
Logarithms
267(8)
Polynomial and Logarithmic Models of Test Scores and District Income
275(2)
Interactions Between Independent Variables
277(13)
Interactions Between Two Binary Variables
277(3)
Interactions Between a Continuous and a Binary Variable
280(6)
Interactions Between Two Continuous Variables
286(4)
Nonlinear Effects on Test Scores of the Student-Teacher Ratio
290(6)
Discussion of Regression Results
291(4)
Summary of Findings
295(1)
Conclusion
296(16)
Appendix 8.1 Regression Functions That Are Nonlinear in the Parameters
307(5)
Assessing Studies Based on Multiple Regression
312(35)
Internal and External Validity
313(3)
Threats to Internal Validity
313(1)
Threats to External Validity
314(2)
Threats to Internal Validity of Multiple Regression Analysis
316(11)
Omitted Variable Bias
316(3)
Misspecification of the Functional Form of the Regression Function
319(1)
Errors-in-Variables
319(3)
Sample Selection
322(2)
Simultaneous Causality
324(1)
Sources of Inconsistency of OLS Standard Errors
325(2)
Internal and External Validity When the Regression Is Used for Forecasting
327(2)
Using Regression Models for Forecasting
327(1)
Assessing the Validity of Regression Models for Forecasting
328(1)
Example: Test Scores and Class Size
329(9)
External Validity
329(7)
Internal Validity
336(1)
Discussion and Implications
337(1)
Conclusion
338(9)
Appendix 9.1 The Massachusetts Elementary School Testing Data
344(3)
PART THREE Further Topics in Regression Analysis
347(176)
Regression with Panel Data
349(34)
Panel Data
350(3)
Example: Traffic Deaths and Alcohol Taxes
351(2)
Panel Data with Two Time Periods: ``Before and After'' Comparisons
353(3)
Fixed Effects Regression
356(5)
The Fixed Effects Regression Model
356(3)
Estimation and Inference
359(1)
Application to Traffic Deaths
360(1)
Regression with Time Fixed Effects
361(3)
Time Effects Only
361(1)
Both Entity and Time Fixed Effects
362(2)
The Fixed Effects Regression Assumptions and Standard Errors for Fixed Effects Regression
364(3)
The Fixed Effects Regression Assumptions
364(2)
Standard Errors for Fixed Effects Regression
366(1)
Drunk Driving Laws and Traffic Deaths
367(4)
Conclusion
371(12)
Appendix 10.1 The State Traffic Fatality Data Set
378(1)
Appendix 10.2 Standard Errors for Fixed Effects Regression with Serially Correlated Errors
379(4)
Regression with a Binary Dependent Variable
383(38)
Binary Dependent Variables and the Linear Probability Model
384(5)
Binary Dependent Variables
385(2)
The Linear Probability Model
387(2)
Probit and Logit Regression
389(7)
Probit Regression
389(5)
Logit Regression
394(2)
Comparing the Linear Probability, Probit, and Logit Models
396(1)
Estimation and Inference in the Logit and Probit Models
396(4)
Nonlinear Least Squares Estimation
397(1)
Maximum Likelihood Estimation
398(1)
Measures of Fit
399(1)
Application to the Boston HMDA Data
400(7)
Summary
407(14)
Appendix 11.1 The Boston HMDA Data Set
415(1)
Appendix 11.2 Maximum Likelihood Estimation
415(3)
Appendix 11.3 Other Limited Dependent Variable Models
418(3)
Instrumental Variables Regression
421(47)
The IV Estimator with a Single Regressor and a Single Instrument
422(10)
The IV Model and Assumptions
422(1)
The Two Stage Least Squares Estimator
423(1)
Why Does IV Regression Work?
424(4)
The Sampling Distribution of the TSLS Estimator
428(2)
Application to the Demand for Cigarettes
430(2)
The General IV Regression Model
432(7)
TSLS in the General IV Model
433(1)
Instrument Relevance and Exogeneity in the General IV Model
434(1)
The IV Regression Assumptions and Sampling Distribution of the TSLS Estimator
434(3)
Inference Using the TSLS Estimator
437(1)
Application to the Demand for Cigarettes
437(2)
Checking Instrument Validity
439(6)
Assumption #1: Instrument Relevance
439(4)
Assumption #2: Instrument Exogeneity
443(2)
Application to the Demand for Cigarettes
445(5)
Where Do Valid Instruments Come From?
450(5)
Three Examples
451(4)
Conclusion
455(13)
Appendix 12.1 The Cigarette Consumption Panel Data Set
462(1)
Appendix 12.2 Derivation of the Formula for the TSLS Estimator in Equation (12.4)
462(1)
Appendix 12.3 Large-Sample Distribution of the TSLS Estimator
463(1)
Appendix 12.4 Large-Sample Distribution of the TSLS Estimator When the Instrument Is Not Valid
464(2)
Appendix 12.5 Instrumental Variables Analysis with Weak Instruments
466(2)
Experiments and Quasi-Experiments
468(55)
Idealized Experiments and Causal Effects
470(2)
Ideal Randomized Controlled Experiments
470(1)
The Differences Estimator
471(1)
Potential Problems with Experiments in Practice
472(5)
Threats to Internal Validity
472(3)
Threats to External Validity
475(2)
Regression Estimators of Causal Effects Using Experimental Data
477(9)
The Differences Estimator with Additional Regressors
477(3)
The Differences-in-Differences Estimator
480(4)
Estimation of Causal Effects for Different Groups
484(1)
Estimation When There Is Partial Compliance
484(1)
Testing for Randomization
485(1)
Experimental Estimates of the Effect of Class Size Reductions
486(8)
Experimental Design
486(1)
Analysis of the STAR Data
487(5)
Comparison of the Observational and Experimental Estimates of Class Size Effects
492(2)
Quasi-Experiments
494(6)
Examples
495(2)
Econometric Methods for Analyzing Quasi-Experiments
497(3)
Potential Problems with Quasi-Experiments
500(2)
Threats to Internal Validity
500(2)
Threats to External Validity
502(1)
Experimental and Quasi-Experimental Estimates in Heterogeneous Populations
502(5)
Population Heterogeneity: Whose Causal Effect?
502(1)
OLS with Heterogeneous Causal Effects
503(1)
IV Regression with Heterogeneous Causal Effects
504(3)
Conclusion
507(16)
Appendix 13.1 The Project STAR Data Set
516(1)
Appendix 13.2 Extension of the Differences-in-Differences Estimator to Multiple Time Periods
517(1)
Appendix 13.3 Conditional Mean Independence
518(2)
Appendix 13.4 IV Estimation When the Causal Effect Varies Across Individuals
520(3)
PART FOUR Regression Analysis of Economic Time Series Data
523(152)
Introduction to Time Series Regression and Forecasting
525(66)
Using Regression Models for Forecasting
527(1)
Introduction to Time Series Data and Serial Correlation
528(7)
The Rates of Inflation and Unemployment in the United States
528(1)
Lags, First Differences, Logarithms, and Growth Rates
528(4)
Autocorrelation
532(1)
Other Examples of Economic Time Series
533(2)
Autoregressions
535(6)
The First Order Autoregressive Model
535(3)
The pth Order Autoregressive Model
538(3)
Time Series Regression with Additional Predictors and the Autoregressive Distributed Lag Model
541(8)
Forecasting Changes in the Inflation Rate Using Past Unemployment Rates
541(3)
Stationarity
544(1)
Time Series Regression with Multiple Predictors
545(3)
Forecast Uncertainty and Forecast Intervals
548(1)
Lag Length Selection Using Information Criteria
549(5)
Determining the Order of an Autoregression
551(2)
Lag Length Selection in Time Series Regression with Multiple Predictors
553(1)
Nonstationarity I: Trends
554(11)
What Is a Trend?
555(2)
Problems Caused by Stochastic Trends
557(3)
Detecting Stochastic Trends: Testing for a Unit AR Root
560(4)
Avoiding the Problems Caused by Stochastic Trends
564(1)
Nonstationarity II: Breaks
565(12)
What Is a Break?
565(1)
Testing for Breaks
566(5)
Pseudo Out-of-Sample Forecasting
571(5)
Avoiding the Problems Caused by Breaks
576(1)
Conclusion
577(14)
Appendix 14.1 Time Series Data Used in Chapter 14
586(1)
Appendix 14.2 Stationarity in the AR(1) Model
586(2)
Appendix 14.3 Lag Operator Notation
588(1)
Appendix 14.4 ARMA Models
589(1)
Appendix 14.5 Consistency of the BIC Lag Length Estimator
589(2)
Estimation of Dynamic Causal Effects
591(46)
An Initial Taste of the Orange Juice Data
593(2)
Dynamic Causal Effects
595(5)
Causal Effects and Time Series Data
596(2)
Two Types of Exogeneity
598(2)
Estimation of Dynamic Causal Effects with Exogenous Regressors
600(4)
The Distributed Lag Model Assumptions
601(1)
Autocorrelated ut, Standard Errors, and Inference
601(1)
Dynamic Multipliers and Cumulative Dynamic Multipliers
602(2)
Heteroskedasticity- and Autocorrelation-Consistent Standard Errors
604(4)
Distribution of the OLS Estimator with Autocorrelated Errors
604(2)
HAC Standard Errors
606(2)
Estimation of Dynamic Causal Effects with Strictly Exogenous Regressors
608(10)
The Distributed Lag Model with AR(1) Errors
609(3)
OLS Estimation of the ADL Model
612(1)
GLS Estimation
613(2)
The Distributed Lag Model with Additional Lags and AR(p) Errors
615(3)
Orange Juice Prices and Cold Weather
618(6)
Is Exogeneity Plausible? Some Examples
624(3)
U.S. Income and Australian Exports
625(1)
Oil Prices and Inflation
626(1)
Monetary Policy and Inflation
626(1)
The Phillips Curve
627(1)
Conclusion
627(10)
Appendix 15.1 The Orange Juice Data Set
634(1)
Appendix 15.2 The ADL Model and Generalized Least Squares in Lag Operator Notation
634(3)
Additional Topics in Time Series Regression
637(38)
Vector Autoregressions
638(4)
The VAR Model
638(3)
A VAR Model of the Rates of Inflation and Unemployment
641(1)
Multiperiod Forecasts
642(6)
Iterated Multiperiod Forecasts
643(2)
Direct Multiperiod Forecasts
645(2)
Which Method Should You Use?
647(1)
Orders of Integration and the DF-GLS Unit Root Test
648(7)
Other Models of Trends and Orders of Integration
648(2)
The DF-GLS Test for a Unit Root
650(3)
Why Do Unit Root Tests Have Non-normal Distributions?
653(2)
Cointegration
655(9)
Cointegration and Error Correction
655(3)
How Can You Tell Whether Two Variables Are Cointegrated?
658(2)
Estimation of Cointegrating Coefficients
660(1)
Extension to Multiple Cointegrated Variables
661(1)
Application to Interest Rates
662(2)
Volatility Clustering and Autoregressive Conditional Heteroskedasticity
664(5)
Volatility Clustering
665(1)
Autoregressive Conditional Heteroskedasticity
666(1)
Application to Stock Price Volatility
667(2)
Conclusion
669(6)
Appendix 16.1 U.S. Financial Data Used in Chapter 16
674(1)
PART FIVE The Econometric Theory of Regression Analysis
675(80)
The Theory of Linear Regression with One Regressor
677(27)
The Extended Least Squares Assumptions and the OLS Estimator
678(2)
The Extended Least Squares Assumptions
678(2)
The OLS Estimator
680(1)
Fundamentals of Asymptotic Distribution Theory
680(6)
Convergence in Probability and the Law of Large Numbers
681(2)
The Central Limit Theorem and Convergence in Distribution
683(2)
Slutsky's Theorem and the Continuous Mapping Theorem
685(1)
Application to the t-Statistic Based on the Sample Mean
685(1)
Asymptotic Distribution of the OLS Estimator and t-Statistic
686(2)
Consistency and Asymptotic Normality of the OLS Estimators
686(1)
Consistency of Heteroskedasticity-Robust Standard Errors
686(2)
Asymptotic Normality of the Heteroskedasticity-Robust t-Statistic
688(1)
Exact Sampling Distributions When the Errors Are Normally Distributed
688(3)
Distribution of β1 with Normal Errors
688(2)
Distribution of the Homoskedasticity-only t-Statistic
690(1)
Weighted Least Squares
691(13)
WLS with Known Heteroskedasticity
691(1)
WLS with Heteroskedasticity of Known Functional Form
692(3)
Heteroskedasticity-Robust Standard Errors or WLS?
695(5)
Appendix 17.1 The Normal and Related Distributions and Moments of Continuous Random Variables
700(2)
Appendix 17.2 Two Inequalities
702(2)
The Theory of Multiple Regression
704(51)
The Linear Multiple Regression Model and OLS Estimator in Matrix Form
706(4)
The Multiple Regression Model in Matrix Notation
706(1)
The Extended Least Squares Assumptions
707(1)
The OLS Estimator
708(2)
Asymptotic Distribution of the OLS Estimator and t-Statistic
710(3)
The Multivariate Central Limit Theorem
710(1)
Asymptotic Normality of β
710(1)
Heteroskedasticity-Robust Standard Errors
711(1)
Confidence Intervals for Predicted Effects
712(1)
Asymptotic Distribution of the t-Statistic
713(1)
Tests of Joint Hypotheses
713(2)
Joint Hypotheses in Matrix Notation
713(1)
Asymptotic Distribution of the F-Statistic
714(1)
Confidence Sets for Multiple Coefficients
714(1)
Distribution of Regression Statistics with Normal Errors
715(4)
Matrix Representations of OLS Regression Statistics
715(1)
Distribution of β with Normal Errors
716(1)
Distribution of s2/4
717(1)
Homoskedasticity-Only Standard Errors
717(1)
Distribution of the t-Statistic
718(1)
Distribution of the F-Statistic
718(1)
Efficiency of the OLS Estimator with Homoskedastic Errors
719(2)
The Gauss-Markov Conditions for Multiple Regression
719(1)
Linear Conditionally Unbiased Estimators
719(1)
The Gauss-Markov Theorem for Multiple Regression
720(1)
Generalized Least Squares
721(6)
The GLS Assumptions
722(2)
GLS When Ω Is Known
724(1)
GLS When Ω Contains Unknown Parameters
725(1)
The Zero Conditional Mean Assumption and GLS
725(2)
Instrumental Variables and Generalized Method of Moments Estimation
727(28)
The IV Estimator in Matrix Form
728(1)
Asymptotic Distribution of the TSLS Estimator
729(1)
Properties of TSLS When the Errors Are Homoskedastic
730(3)
Generalized Method of Moments Estimation in Linear Models
733(10)
Appendix 18.1 Summary of Matrix Algebra
743(4)
Appendix 18.2 Multivariate Distributions
747(1)
Appendix 18.3 Derivation of the Asymptotic Distribution of β
748(1)
Appendix 18.4 Derivations of Exact Distributions of OLS Test Statistics with Normal Errors
749(2)
Appendix 18.5 Proof of the Gauss-Markov Theorem for Multiple Regression
751(1)
Appendix 18.6 Proof of Selected Results for IV and GMM Estimation
752(3)
Appendix 755(8)
References 763(4)
Answers to ``Review the Concepts'' Questions 767(8)
Glossary 775(8)
Index 783


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