## Description

Practical Econometrics: data collection, analysis, and application / Edition 1 by Michael J. Hilmer EBOOK PDF Instant Download

Table of Contents

Dedication

About the Authors

Preface

Acknowledgments

Chapter One. An Introduction to Econometrics and Statistical Inference

Chapter Objectives

A Student’s Perspective

Big Picture Overview

1.1 Understand the Steps Involved in Conducting an Empirical Research Project

1.2 Understand the Meaning of the Term Econometrics

1.3 Understand the Relationship among Populations, Samples, and Statistical Inference

Populations and Samples

A Real-World Example of Statistical Inference: The Nielsen Ratings

1.4 Understand the Important Role that Sampling Distributions Play in Statistical Inference

Additions to Our Empirical Research Toolkit

Our New Empirical Tools in Practice: Using What We Have Learned in This Chapter

Looking Ahead to Chapter 2

Problems

Chapter Two. Collection and Management of Data

Chapter Objectives

A Student’s Perspective

Big Picture Overview

2.1 Consider Potential Sources of Data

2.2 Work Through an Example of the First Three Steps in Conducting an Empirical Research Project

2.3 Develop Data Management Skills

2.4 Understand Some Useful Excel Commands

Installing the Data Analysis ToolPak

Importing Data from the Web

Creating New Worksheets

Sorting Data from Lowest to Highest and Highest to Lowest

Cut, Copy, and Paste Columns and Rows

Use the Function Tool in Excel

Copy Cell Entries Down a Column

Use the Paste Special Command to Copy Values

Use the Paste Special Command to Transpose Columns

Additions to Our Empirical Research Toolkit

Our New Empirical Tools in Practice: Using What We Have Learned in This Chapter

Looking Ahead to Chapter 3

Problems

Exercises

Chapter Three. Summary Statistics

Chapter Objectives

A Student’s Perspective

Big Picture Overview

3.1 Construct Relative Frequency Histograms for a Given Variable

Constructing a Relative Frequency Histogram

3.2 Calculate Measures of Central Tendency for a Given Variable

The Sample Mean

The Sample Median

3.3 Calculate Measures of Dispersion for a Given Variable

Variance and Standard Deviation

Percentiles

The Five-Number Summary

3.4 Use Measures of Central Tendency and Dispersion for a Given Variable

3.5 Detect Whether Outliers for a Given Variable Are Present in Our Sample

Detecting Outliers if the Data Set Is Symmetric

Detecting Outliers if the Data Set Is Skewed

3.6 Construct Scatter Diagrams for the Relationship between Two Variables

3.7 Calculate the Covariance and the Correlation Coefficient for the Linear Relationship between y a

Additions to Our Empirical Research Toolkit

Our New Empirical Tools in Practice: Using What We Have Learned in This Chapter

Looking Ahead to Chapter 4

Problems

Exercises

Chapter Four. Simple Linear Regression

Chapter Objectives

A Student’s Perspective

Big Picture Overview

Data to Be Analyzed: Our City Property Crime and CEO Compensation Samples

Data Analyzed in the Text

Data Analyzed in the Excel Boxes

4.1 Understand the Goals of Simple Linear Regression Analysis

4.2 Consider What the Random Error Component Contains

4.3 Define the Population Regression Model and the Sample Regression Function

4.4 Estimate the Sample Regression Function

4.5 Interpret the Estimated Sample Regression Function

4.6 Predict Outcomes Based on Our Estimated Sample Regression Function

4.7 Assess the Goodness-of-Fit of the Estimated Sample Regression Function

Measure the Explained and Unexplained Variation in y

Two Potential Measures of the Relative Goodness-of-Fit of Our Estimated Sample Regression Function

4.8 Understand How to Read Regression Output in Excel

4.9 Understand the Difference between Correlation and Causation

Additions to Our Empirical Research Toolkit

Our New Empirical Tools in Practice: Using What We Have Learned in This Chapter

Looking Ahead to Chapter 5

Problems

Exercises

References

Chapter Five. Hypothesis Tests for Linear Regression Analysis

Chapter Objectives

A Student’s Perspective

Big Picture Overview

5.1 Construct Sampling Distributions

5.2 Understand Desirable Properties of Simple Linear Regression Estimators

5.3 Understand the Simple Linear Regression Assumptions Required for OLS to be the Best Linear Unbia

5.4 Understand How to Conduct Hypothesis Tests in Linear Regression Analysis

Method 1: Construct Confidence Intervals around the Population Parameter

Method 2: Compare Calculated Test Statistics with Predetermined Critical Values

Method 3: Calculate and Compare p-values with Predetermined Levels of Significance

5.5 Conduct Hypothesis Tests for the Overall Statistical Significance of the Sample Regression Funct

5.6 Conduct Hypothesis Tests for the Statistical Significance of the Slope Coefficient

Calculate the Standard Error of the Estimated Slope Coefficient

Test for the Individual Significance of the Slope Coefficient

5.7 Understand How to Read Regression Output in Excel for the Purpose of Hypothesis Testing

5.8 Construct Confidence Intervals around the Predicted Value of y

Additions to Our Empirical Research Toolkit

Our New Empirical Tools in Practice: Using What We Have Learned in This Chapter

Looking Ahead to Chapter 6

Problems

Exercises

Appendix 5A: Common Theoretical Probability Distributions

Chapter Six. Multiple Linear Regression Analysis

Chapter Objectives

A Student’s Perspective

Big Picture Overview

Data to Be Analyzed: Our MLB Position Player and International GDP Samples

Data Analyzed in the Text

Data Analyzed in the Excel Boxes

6.1 Understand the Goals of Multiple Linear Regression Analysis

6.2 Understand the “Holding All Other Independent Variables Constant” Condition in Multiple Line

6.3 Understand the Multiple Linear Regression Assumptions Required for OLS to Be Blue

6.4 Interpret Multiple Linear Regression Output in Excel

6.5 Assess the Goodness-of-Fit of the Sample Multiple Linear Regression Function

The Coefficient of Determination (R²)

The Adjusted R² (R?²)

Standard Error of the Sample Regression Function

6.6 Perform Hypothesis Tests for the Overall Significance of the Sample Regression Function

6.7 Perform Hypothesis Tests for the Individual Significance of a Slope Coefficient

6.8 Perform Hypothesis Tests for the Joint Significance of a Subset of Slope Coefficients

6.9 Perform the Chow Test for Structural Differences Between Two Subsets of Data

Additions to Our Empirical Research Toolkit

Our New Empirical Tools in Practice: Using What We Have Learned in This Chapter

Looking Ahead to Chapter 7

Problems

Exercises

Chapter Seven. Qualitative Variables and Nonlinearities in Multiple Linear Regression Analysis

Chapter Objectives

A Student’s Perspective

Big Picture Overview

7.1 Construct and Use Qualitative Independent Variables

Binary Dummy Variables

Categorical Variables

Categorical Variables as a Series of Dummy Variables

7.2 Construct and Use Interaction Effects

7.3 Control for Nonlinear Relationships

Quadratic Effects

Interaction Effects between Two Quantitative Variables

7.4 Estimate Marginal Effects as Percent Changes and Elasticities

The Log-Linear Model

The Log-Log Model

7.5 Estimate a More Fully Specified Model

Additions to Our Empirical Research Toolkit

Our New Empirical Tools in Practice: Using What We Have Learned in This Chapter

Looking Ahead to Chapter 8

Problems

Exercises

Chapter Eight. Model Selection in Multiple Linear Regression Analysis

Chapter Objectives

A Student’s Perspective

Big Picture Overview

8.1 Understand the Problem Presented by Omitted Variable Bias

8.2 Understand the Problem Presented by Including an Irrelevant Variable

8.3 Understand the Problem Presented by Missing Data

8.4 Understand the Problem Presented by Outliers

8.5 Perform the Reset Test for the Inclusion of Higher-Order Polynomials

8.6 Perform the Davidson-MacKinnon Test for Choosing among Non-Nested Alternatives

8.7 Consider How to Implement the “Eye Test” to Judge the Sample Regression Function

8.8 Consider What It Means for a p-value to be Just Above a Given Significance Level

Additions to Our Empirical Research Toolkit

Our New Empirical Tools in Practice: Using What We Have Learned in This Chapter

Looking Ahead to Chapter 9

Problems

Exercises

Chapter Nine. Heteroskedasticity

Chapter Objectives

A Student’s Perspective

Big Picture Overview

Our Empirical Example: The Relationship between Income and Expenditures

Data to Be Analyzed: Our California Home Mortgage Application Sample

9.1 Understand Methods for Detecting Heteroskedasticity

The Informal Method for Detecting Heteroskedasticity

Formal Methods for Detecting Heteroskedasticity

9.2 Correct for Heteroskedasticity

Weighted Least Squares

A Different Assumed Form of Heteroskedasticity

White’s Heteroskedastic Consistent Standard Errors

Additions to Our Empirical Research Toolkit

Our New Empirical Tools in Practice: Using What We Have Learned in This Chapter

Looking Ahead to Chapter 10

Problems

Exercises

Chapter Ten. Time-Series Analysis

Chapter Objectives

A Student’s Perspective

Big Picture Overview

Data to Be Analyzed: Our U.S. Houses Sold Data, 1986Q2–2005Q4

10.1 Understand the Assumptions Required for OLS to Be the Best Linear Unbiased Estimator for Time-S

10.2 Understand Stationarity and Weak Dependence

Stationarity in Time Series

Weakly Dependent Time Series

10.3 Estimate Static Time-Series Models

10.4 Estimate Distributed Lag Models

10.5 Understand and Account for Time Trends and Seasonality

Time Trends

Seasonality

10.6 Test for Structural Breaks in the Data

10.7 Understand the Problem Presented by Spurious Regression

10.8 Learn to Perform Forecasting

Additions to Our Empirical Research Toolkit

Our New Empirical Tools in Practice: Using What We Have Learned in This Chapter

Looking Ahead to Chapter 11

Problems

Exercises

Reference

Chapter Eleven. Autocorrelation

Chapter Objectives

A Student’s Perspective

Big Picture Overview

11.1 Understand the Autoregressive Structure of the Error Term

The AR(1) Process

The AR(2) Process

The AR(1,4) Process

11.2 Understand Methods for Detecting Autocorrelation

Informal Methods for Detecting Autocorrelation

Formal Methods for Detecting Autocorrelation

11.3 Understand How to Correct for Autocorrelation

The Cochrane-Orcutt Method for AR(1) Processes

The Prais-Winsten Method for AR(1) Processes

Newey-West Robust Standard Errors

11.4 Understand Unit Roots and Cointegration

Unit Roots

Cointegration

Additions to Our Empirical Research Toolkit

Our New Empirical Tools in Practice: Using What We Have Learned in This Chapter

Looking Ahead to Chapter 12

Problems

Exercises

Chapter Twelve. Limited Dependent Variable Analysis

Chapter Objectives

A Student’s Perspective

Big Picture Overview

Data to Be Analyzed: Our 2010 House Election Data

12.1 Estimate Models with Binary Dependent Variables

The Linear Probability Model

The Logit Model

The Probit Model

Comparing the Three Estimators

12.2 Estimate Models with Categorical Dependent Variables

A New Data Set: Analyzing Educational Attainment Using Our SIPP Education Data

The Multinomial Logit

The Multinomial Probit

The Ordered Probit

Additions to Our Empirical Research Toolkit

Our New Empirical Tools in Practice: Using What We Have Learned in This Chapter

Looking Ahead to Chapter 13

Problems

Exercises

Chapter Thirteen. Panel Data

Chapter Objectives

A Student’s Perspective

Big Picture Overview

13.1 Understand the Nature of Panel Data

Data to Be Analyzed: Our NFL Team Value Panel

13.2 Employ Pooled Cross-Section Analysis

Pooled Cross-Section Analysis with Year Dummies

13.3 Estimate Panel Data Models

First-Differenced Data in a Two-Period Model

Fixed-Effects Panel Data Models

Random-Effects Panel Data Models

Additions to Our Empirical Research Toolkit

Our New Empirical Tools in Practice: Using What We Have Learned in This Chapter

Looking Ahead to Chapter 14

Problems

Exercises

Chapter Fourteen. Instrumental Variables for Simultaneous Equations, Endogenous Independent Variable

Chapter Objectives

A Student’s Perspective

Big Picture Overview

14.1 Use Two-Stage Least Squares to Identify Simultaneous Demand and Supply Equations

Data to Be Analyzed: Our U.S. Gasoline Sales Data

14.2 Use Two-Stage Least Squares to Correct for Endogeneity of an Independent Variable

Our Empirical Example: The Effect of a Doctor’s Advice to Reduce Drinking

Data to Be Analyzed: Our Doctor Advice Data

14.3 Use Two-Stage Least Squares to Correct for Measurement Error

Measurement Error in the Dependent Variable

Measurement Error in an Independent Variable

Our Empirical Example: Using a Spouse’s Responses to Control for Measurement Error in an Individual’

Additions to Our Empirical Research Toolkit

Our New Empirical Tools in Practice: Using What We Have Learned in This Chapter

Looking Ahead to Chapter 15

Problems

Exercises

Chapter Fifteen. Quantile Regression, Count Data, Sample Selection Bias, and Quasi-Experimental Meth

Chapter Objectives

A Student’s Perspective

Big Picture Overview

15.1 Estimate Quantile Regression

15.2 Estimate Models with Non-Negative Count Data

Our Empirical Example: Early-Career Publications by Economics PhDs

Data to Be Analyzed: Our Newly Minted Economics PhD Publication Data

The Poisson Model

The Negative Binomial Model

Choosing between the Poisson and the Negative Binomial Models

15.3 Control for Sample-Selection Bias

Data to Be Analyzed: Our CPS Salary Data

15.4 Use Quasi-Experimental Methods

Our Empirical Example: Changes in State Speed Limits

Data to Be Analyzed: Our State Traffic Fatality Data

Additions to Our Empirical Research Toolkit

Our New Empirical Tools in Practice: Using What We Have Learned in This Chapter

Looking Ahead to Chapter 16

Problems

Exercises

Chapter Sixteen. How to Conduct and Write Up an Empirical Research Project

Chapter Objectives

A Student’s Perspective

Big Picture Overview

16.1 General Approach to Conducting an Empirical Research Project

Collecting Data for the Dependent Variables

Collecting Data for the Independent Variables

16.2 General Approach to Writing Up an Empirical Research Project

16.3 An Example Write-Up of Our Movie Box-Office Project

Lights, Camera, Ticket Sales: An Analysis of the Determinants of Domestic Box-Office Gross

1. Introduction

2. Data Description

3. Empirical Results

4. Conclusion

References

Appendix A. Data Collection

Appendix B. Stata Commands

Appendix C. Statistical Tables

Index