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

Dedication
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
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