List of Figures
List of Tables
Preface
1 Analysis of Economics Data
1.1 Statistical Methods
1.2 Types of Data
1.3 Regression Analysis
1.4 Key Concepts
1.5 Exercises
2 Univariate Data Summary
2.1 Summary Statistics for Numerical Data
2.2 Charts for Numerical Data
2.3 Charts for Numerical Data by Category
2.4 Summary and Charts for Categorical Data
2.5 Data Transformation
2.6 Data Transformations for Time Series Data
2.7 Key Concepts
2.8 Exercises
3 The Sample Mean
3.1 Random Variables
3.2 Random Samples
3.3 Sample Generated by an Experiment: Coin Tosses
3.4 Properties of the Sample Mean
3.5 Sampling from a Finite Population: 1880 Census
3.6 Estimation of the Population Mean
3.7 Nonrepresenative Samples
3.8 Computer Generation of Random Samples
3.9 Key Concepts
3.10 Exercises
4 Statistical Inference for the Mean
4.1 Example: Mean Annual Earnings
4.2 t Statistics and t Distribution
4.3 Confidence Intervals
4.4 Two-Sided Hypothesis Tests
4.5 Two-sided Hypothesis Test Examples
4.6 One-Sided Directional Hypothesis Tests
4.7 Generalization of Confidence Intervals and Hypothesis Tests
4.8 Proportions Data
4.9 Key Concepts
4.10 Exercises
5 Bivariate Data Summary
5.1 Example: House Price and Size
5.2 Two-way Tabulation
5.3 Two-way Scatter Plot
5.4 Sample Correlation
5.5 Regression Line
5.6 Measure of Model Fit
5.7 Computer Output following OLS Regression
5.8 Predictions and Outlying Observations
5.9 Regression and Correlation
5.10 Causation
5.11 Computations for Correlation and Regression
5.12 Nonparametric Regression
5.13 Key Concepts
5.14 Exercises
6 The Least Squares Estimator
6.1 Population Model for Bivariate Regression
6.2 Examples of Sampling from a Population
6.3 Properties of the Least Squares Estimator
6.4 Estimators of Model Parameters
6.5 Key Concepts
6.6 Exercises
7 Statistical Inference for Bivariate Regression
7.1 Example: House Price and Size
7.2 t Statistic
7.3 Confidence Intervals
7.4 Tests of Statistical Significance
7.5 Two-Sided Hypothesis Tests
7.6 One-Sided Directional Hypothesis Tests
7.7 Robust Standard Errors
7.8 Key Concepts
7.9 Exercises
8 Case Studies for Bivariate Regression
8.1 Health Outcomes across Countries
8.2 Health Expenditures across Countries
8.3 Capital Asset Pricing Model
8.4 Output and Unemployment in the U.S.
8.5 Exercises
9 Models with Natural Logarithms
9.1 Natural Logarithm Function
9.2 Semi-elasticties and Elasticities
9.3 Log-linear, Log-log and Linear-log models
9.4 Example: Earnings and Education
9.5 Further Uses of the Natural Logarithm
9.6 Exponential Function
9.7 Key Concepts
9.8 Exercises
10 Data Summary with Multiple Regression
10.1 Example: House Price and Characteristics
10.2 Two-way Scatter Plots
10.3 Correlation
10.4 Regression Line
10.5 Interpretation of Slope Coefficients
10.6 Model Fit
10.7 Computer Output Following Multiple Regression
10.8 Inestimable Models
10.9 Key Concepts
10.10 Exercises
11 Statistical Inference for Multiple Regression
11.1 Properties of the Least Squares Estimator
11.2 Estimatorys of Model Parameters
11.3 Confidence Intervals
11.4 Hypothesis Tests on a Single Parameter
11.5 Joint Hypothesis Tests
11.6 F Statistics under Assumptions 1-4
11.7 Presentation of Regression Results
11.8 Key Concepts
11.9 Exercises
12 Further Topics in Multiple Regression
12.1 Inference with Robust Standard Errors
12.2 Prediction
12.3 Nonrepresentative Samples
12.4 Best Estimation
12.5 Best Confidence Intervals
12.6 Best Hypothesis Tests
12.7 Data Science and Big Data: An Overview
12.8 Bayesian Methods: An Overview
12.9 A Brief History of Statistics, Regression and Econometrics
12.10 Key Concepts
12.11 Exercises
13 Case Studies for Multiple Regression
13.1 School Academic Performance
13.2 Cobb-Douglas Production Function
13.3 Phillips Curve
13.4 Automobile Fuel Efficency
13.5 Rand Health Insurance Experiement
13.6 Access to Health Care and Health Outcomes
13.7 Gains from Political Incumbency
13.8 Institutions and Country GDP
13.9 From Raw Data to Final Data
13.10 Exercises
14 Regression with Indicator Varibales
14.1 Example: Earnings, Gender, Education and Type of Worker
14.2 Regression on just a Single Indicator Variable
14.3 Regression on an Indicator Variable and Additional Regressors
14.4 Regression with Sets of Indicator Variables
14.5 Key Concepts
14.6 Exercises
15 Regression with Transformed Variables
15.1 Example: Earnings, Gender, Education and Type of Worker
15.2 Marginal Effects for Nonlinear Models
15.3 Quadratic Model and Polynomial Models
15.4 Interacted Regressors
15.5 Log-linear and Log-log Models
15.6 Prediction from Log-Linear and Log-Log Models
15.7 Models with a Mix of Regressor Types
15.8 Key Concepts
15.9 Exercises
16 Checking the Model and Data
16.1 Multicollinear Data
16.2 Model Assumptions Revisited
16.3 Incorrect Population Model
16.4 Regressors Correlated with Errors
16.5 Heteroskedastic Errors
16.6 Correlated Errors
16.7 Example: Democracy and Growth
16.8 Diagnostics
16.9 Key Concepts
16.10 Exercises
17 Special Topics
17.1 Cross-Section Data
17.2 Panel Data
17.3 Panel Data Example: NBA Team Revenue
17.4 Instrumental Variables
17.5 Causal Inference: An Overview
17.6 Nonlinear Regression Models
17.7 Time-Series Data
17.8 Time-Series Example: U.S. Treasury Security Interest Rates
17.9 Further Reading
17.10 Key Concepts
17.11 Exercises
A Using Statistical Packages
A.1 General Issues
A.2 Stata Essentials
A.3 R Essentials
A.4 Gretl Essentials
A.5 Eviews Essentials
A.6 Excel and Google Sheets Spreadsheet Applications
A.7 Critical values and p-values
B Some Essentials of Probability Theory
B.1 Probability Theory for a Single Random Variable
B.2 Probability Theory for the Sample Mean
B.3 Probability Theory for Two Related Random Variables
C Properties of OLS, IV, and ML Estimators
C.1 OLS with Independent Homoskedastic Errors
C.2 Robust Standard Errors
C.3 Instrumental Variables Estimation
C.4 OLS with Matrix Algebra
C.5 Maximum Likelihood Estimation
C.6 Exercises
D Solutions to Selected Exercises
D.1 Solutions: Analysis of Economic Data
D.2 Solutions: Univariate Data Summary
D.3 Solutions: The Sample Mean
D.4 Solutions: Statistical Inference for the Mean
D.5 Solutions: Bivariate Data Summary
D.6 Solutions: The Least Squares Estimator
D.7 Solutions: Statistical Inference for Bivariate Regression
D.8 Solutions: Case Studies for Bivariate Regression
D.9 Solutions: Models with Natural Logarithm
D.10 Solutions: Data Summary with Multiple Regression
D.11 Solutions: Statistical Inference for Multiple Regression
D.12 Solutions: Further Topics in Multiple Regression
D.13 Solutions: Case Studies for Multiple Regression
D.14 Solutions: Models with Indicator Variables
D.15 Solutions: Models with Transformed Variables
D.16 Solutions: Checking the Model and Data
D.17 Solutions: Special Topics
E Tables for Key Distributions
F References
Index
