Introductory Econometrics by Barreto, Humberto -- Read -- Imperial Library of Trantor

Index

Cover Half Title Title Page Copyright Dedication Contents Preface User Guide

0.1. Conventions and Organization of Files 0.2. Preparing and Working with Microsoft Excel[sup(®)]

1. Introduction

1.1. Definition of Econometrics 1.2. Regression Analysis

Cig.xls

1.3. Conclusion 1.4. Exercises References

Part 1. Description

2. Correlation

2.1. Introduction 2.2. Correlation Basics

Correlation.xls

2.3. Correlation Dangers

Correlation.xls

2.4. Ecological Correlation

EcolCorr.xls EcolCorrCPS.xls

2.5. Conclusion 2.6. Exercises References

3. PivotTables

3.1. Introduction 3.2. The Basic PivotTable

IndianaFTWorkers.xls (in Basic Tools\InternetData\CPS) Histogram.xla (Excel add-in)

3.3. The Crosstab and Conditional Average

IndianaFTWorkers.xls (in Basic Tools\InternetData\CPS)

3.4. PivotTables and the Conditional Mean Function

EastNorthCentralFTWorkers.xls

3.5. Conclusion 3.6. Exercises References

4. Computing the OLS Regression Line

4.1. Introduction 4.2. Fitting the Ordinary Least Squares Regression Line

Reg.xls

4.3. Least Squares Formulas

Reg.xls

4.4. Fitting the Regression Line in Practice

Reg.xls

4.5. Conclusion 4.6. Exercises References Appendix: Deriving the Least Squares Formulas

5. Interpreting OLS Regression

5.1. Introduction 5.2. Regression as Double Compression

DoubleCompression.xls EastNorthCentralFTWorkers.xls

5.3. Galton and Two Regression Lines

TwoRegressionLines.xls

5.4. Properties of the Sample Average and the Regression Line

OLSFormula.xls

5.5. Residuals and the Root-Mean-Square Error

ResidualPlot.xls RMSE.xls

5.6. R-Squared (R[sup(2)]

RSquared.xls

5.7. Limitations of Data Description with Regression

Anscombe.xls IMRGDPReg.xls SameRegLineDifferentData.xls HourlyEarnings.xls

5.8. Conclusion 5.9. Exercises References Appendix: Proof that the Sample Average is a Least Squares Estimator

6. Functional Form of the Regression

6.1. Introduction 6.2. Understanding Functional Form via an Econometric Fable

Galileo.xls

6.3. Exploring Two Other Functional Forms

IMRGDPFunForm.xls

6.4. The Earnings Function

SemiLogEarningsFn.xls

6.5. Elasticity 6.6. Conclusion 6.7. Exercises References Appendix: A Catalog of Functional Forms

FuncFormCatalog.xls

7. Multiple Regression

7.1. Introduction 7.2. Introducing Multiple Regression

MultiReg.xls

7.3. Improving Description via Multiple Regression

MultiReg.xls

7.4. Multicollinearity

Multicollinearity.xls

7.5. Conclusion 7.6. Exercises References Appendix: The Multivariate Least Squares Formula and the Omitted Variable Rule

8. Dummy Variables

8.1. Introduction 8.2. Defining and Using Dummy Variables

Female.xls

8.3. Properties of Dummy Variables

Female.xls

8.4. Dummy Variables as Intercept Shifters

Female.xls

8.5. Dummy Variable Interaction Terms

Female.xls

8.6. Conclusion 8.7. Exercises References

Part 2. Inference

9. Monte Carlo Simulation

9.1. Introduction 9.2. Random Number Generation Theory

RNGTheory.xls

9.3. Random Number Generation in Practice

RNGPractice.xls

9.4. Monte Carlo Simulation: An Example

MonteCarlo.xls

9.5. The Monte Carlo Simulation Add-In

MonteCarlo.xls MCSim.xla (Excel add-in) MCSimSolver.xla (Excel add-in)

9.6. Conclusion 9.7. Exercises References

10. Review of Statistical Inference

10.1. Introduction 10.2. Introducing Box Models for Chance Processes 10.3. The Coin-Flip Box Model

BoxModel.xls

10.4. The Polling Box Model

PresidentialHeights.xls

10.5. Hypothesis Testing

PValue.xla (Excel add-in)

10.6. Consistent Estimators

Consistency.xls

10.7. The Algebra of Expectations

AlgebraofExpectations.xls

10.8. Conclusion 10.9. Exercises References Appendix: The Normal Approximation

11. The Measurement Box Model

11.1. Introduction 11.2. Introducing the Problem 11.3. The Measurement Box Model 11.4. Monte Carlo Simulation

Measure.xls

11.5. Applying the Box Model

Measure.xls

11.6. Hooke’s Law

HookesLaw.xls

11.7. Conclusion 11.8. Exercises References

12. Comparing Two Populations

12.1. Introduction 12.2. Two Boxes 12.3. Monte Carlo Simulation of a Two Box Model

TwoBoxModel.xls

12.4. A Real Example: Education and Wages

CPS90Workers.xls

12.5. Conclusion 12.6. Exercises

CPS90ExpWorkers.xls

References

13. The Classical Econometric Model

13.1. Introduction 13.2. Introducing the CEM via a Skiing Example

Skiing.xls

13.3. Implementing the CEM via a Skiing Example

Skiing.xls

13.4. CEM Requirements 13.5. Conclusion 13.6. Exercises References

14. The Gauss–Markov Theorem

14.1. Introduction 14.2. Linear Estimators

GaussMarkovUnivariate.xls

14.3. Choosing an Estimator

GaussMarkovUnivariate.xls

14.4. Proving the Gauss–Markov Theorem in the Univariate Case

GaussMarkovUnivariate.xls

14.5. Linear Estimators in Regression Analysis

GaussMarkovBivariate.xls

14.6. OLS is BLUE: The Gauss–Markov Theorem for the Bivariate Case

GaussMarkovBivariate.xls

14.7. Using the Algebra of Expectations

GaussMarkovUnivariate.xls GaussMarkovBivariate.xls

14.8. Conclusion 14.9. Exercises References

15. Understanding the Standard Error

15.1. Introduction 15.2. SE Intuition

SEb1OLS.xls

15.3. The Estimated SE

SEb1OLS.xls

15.4. The Determinants of the SE of the OLS Sample Slope

SEb1OLS.xls

15.5. Estimating the SD of the Errors

EstimatingSDErrors.xls

15.6. The Standard Error of the Forecast and the Standard Error of the Forecast Error

SEForecast.xls

15.7. Conclusion 15.8. Exercises References

16. Confidence Intervals and Hypothesis Testing

16.1. Introduction 16.2. Distributions of OLS Regression Statistics

LinestRandomVariables.xls

16.3. Understanding Confidence Intervals

ConfidenceIntervals.xls

16.4. The Logic of Hypothesis Testing

HypothesisTest.xls

16.5. Z- and T-Tests

ConfidenceIntervals.xls ZandTTests.xls

16.6. A Practical Example

CigDataInference.xls

16.7. Conclusion 16.8. Exercises

SemiLogEarningsFn.xls

References

17. Joint Hypothesis Testing

17.1. Introduction 17.2. Restricted Regression

NoInterceptBug.xls

17.3. The Chi-Square Distribution

ChiSquareDist.xls

17.4. The F-Distribution

FDist.xls

17.5. An F-Test: The Galileo Example

FDistGalileo.xls

17.6. F- and T-Tests for Equality of Two Parameters

FDistFoodStamps.xls

17.7. F-Test for Multiple Parameters

FDistEarningsFn.xls

17.8. The Consequences of Multicollinearity

CorrelatedEstimates.xls

17.9. Conclusion 17.10. Exercises

MyMonteCarlo.xls

References

18. Omitted Variable Bias

18.1. Introduction 18.2. Why Omitted Variable Bias Is Important 18.3. Omitted Variable Bias Defined and Demonstrated

SkiingOVB.xls

18.4. A Real Example of Omitted Variable Bias

ComputerUse1997.xls

18.5. Random X’s: A More Realistic Data Generation Process

ComputerUse1997.xls

18.6. Conclusion 18.7. Exercises References

19. Heteroskedasticity

19.1. Introduction 19.2. A Univariate Example of Heteroskedasticity

Het.xls

19.3. A Bivariate Example of Heteroskedasticity

Het.xls

19.4. Diagnosing Heteroskedasticity with the B-P Test

Het.xls BPSampDist.xls

19.5. Dealing with Heteroskedasticity: Robust Standard Errors

HetRobusSE.xls OLSRegression.xla (Excel add-in)

19.6. Correcting for Heteroskedasticity: Generalized Least Squares

HetGLS.xls

19.7. A Real Example of Heteroskedasticity: The Earnings Function

WagesOct97.xls

19.8. Conclusion 19.9. Exercises References

20. Autocorrelation

20.1. Introduction 20.2. Understanding Autocorrelation

AutoCorr.xls

20.3. Consequences of Autocorrelation

AutoCorr.xls

20.4. Diagnosing Autocorrelation

AutoCorr.xls

20.5. Correcting Autocorrelation

AutoCorr.xls

20.6. Conclusion

CPIMZM.xls Luteinizing.xls

20.7. Exercises

Misspecification.xls FreeThrowAutoCorr.xls

References

21. Topics in Time Series

21.1. Introduction 21.2. Trends in Time Series Models

IndiaPopulation.xls ExpGrowthModel.xls AnnualGDP.xls Spurious.xls

21.3. Dummy Variables in Time Series Models

TimeSeriesDummyVariables.xls CoalMining.xls

21.4. Seasonal Adjustment

SeasonalTheory.xls SeasonalPractice.xls

21.5. Stationarity

Stationarity.xls

21.6. Weak Dependence

Stationarity.xls Spurious.xls

21.7. Lagged Dependent Variables

PartialAdjustment.xls

21.8. Money Demand

MoneyDemand.xls LaggedDepVar.xls

21.9. Comparing Forecasts Using Different Models of the DGP

AnnualGDP.xls ForecastingGDP.xls

21.10. Conclusion 21.11. Exercises References

22. Dummy Dependent Variable Models

22.1. Introduction 22.2. Developing Intuition about Dummy Dependent Variable Models

Raid.xls

22.3. The Campaign Contributions Example

CampCont.xls

22.4. A DDV Box Model

Raid.xls CampCont.xls

22.5. The Linear Probability Model (OLS with a Dummy Dependent Variable)

CampCont.xls LPMMonteCarlo.xls

22.6. Nonlinear Least Squares Applied to Dummy Dependent Variable Models

NLLSFit.xls NLLSMCSim.xls

22.7. Interpreting NLLS Estimates

NLLSFit.xls

22.8. Is There Mortgage Discrimination?

MortDisc.xls MortDiscMCSim.xls DDV.xla (Excel add-in) DDVGN.xla (Excel add-in)

22.9. Conclusion 22.10. Exercises References 23. Bootstrap 23.1. Introduction 23.2. Bootstrapping the Sample Percentage

PercentageBootstrap.xls

23.3. Paired XY Bootstrap

PairedXYBootstrap.xls

23.4. The Bootstrap Add-in

PairedXYBootstrap.xls Bootstrap.xla (Excel add-in)

23.5. Bootstrapping R[sup(2)]

BootstrapR2.xls

23.6. Conclusion 23.7. Exercises References

24. Simultaneous Equations

24.1. Introduction 24.2. Simultaneous Equations Model Example 24.3. Simultaneity Bias with OLS

SimEq.xls

24.4. Two Stage Least Squares

SimEq.xls

24.5. Conclusion 24.6. Exercises References

Glossary Index

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