Statistics and econometrics

· Economic Questions and Data
- Economic Questions We Examine
- Causal Effects and Idealized Experiments
- Data: Sources and Types

· Review of Probability: Basic Notions

· Review of Statistics
- Estimation of the Population Mean
- Hypothesis Tests Concerning the Population Mean
- Confidence Intervals for the Population Mean
- Comparing Means from Different Populations
- Differences-of-Means Estimation of Causal Effects Using Experimental Data
- Using the t-Statistic When the Sample Size Is Small
- Scatterplot, the Sample Covariance, and the Sample Correlation

· Linear Regression with One Regressor
- The Linear Regression Model
- Estimating the Coefficients of the Linear Regression Model
- Measures of Fit
- The Least Squares Assumptions
- Appendix 2: Derivation of the OLS Estimators
- Sampling Distribution of the OLS Estimator

· Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals
- Testing Hypothesis about one of the Regression Coefficients
- Confidence Intervals for a Regression Coefficient
- Regression when X Is a Binary Variable
- Heteroskedasticity and Homoskedasticity
- The Theoretical Foundation of Ordinary Least Squares
- Using the t-Statistic in Regression When the Sample Size Is Small
- Appendix 1: Formulas for OLS Standard Errors

· Linear Regression with Multiple Regressors
- Omitted Variable Bias
- The Multiple Regression Model
- The OLS Estimator in Multiple Regression
- Measure of Fit in Multiple Regression
- The Least Squares Assumptions in Multiple Regression
- The Distribution of the OLS Estimators in Multiple Regression
- Multicollinearity

· Hypothesis Tests and Confidence Intervals in Multiple Regression
- Hypothesis Tests and Confidence Intervals for a Single Coefficient
- Tests of Joint Hypotheses
- Testing Single Restrictions Involving Multiple Coefficients
- Model Specification for Multiple Regression
- Analysis of the Test Score Data Set

· Assessing Studies Based on Multiple Regression (to read only)
- Internal and External Validity
- Threats to Internal Validity of Multiple Regression Analysis
- Internal and External Validity when the Regression is Used for Forecasting
- Example: Test Scores and Class Size

· Regression with a Binary Dependent Variable
- Binary Dependent Variables and the Linear Probability Model
- Probit and Logit Regression
- Estimation and Inference in the Logit and Probit Models
- Some applications

· Instrumental Variable Regression
- The IV Estimator with a Single Regressor and a Single Instrument
- The General IV Regression Model
- Checking Instrument Validity
- Where Do Valid Instruments Come From?
- Appendix 2: Derivation of the Formula for the TSLS Estimator
- Appendix 3: Large-Sample Distribution of the TSLS Estimator

· Introduction to Time Series Regression and Forecasting
- Using Regression Model for Forecasting
- Introduction to Time Series Data and Serial Correlation
- Autoregressions
- Time Series Regression with Additional Predictors and ADL Model
- Lag Length Selection Using Information Criteria

· The Theory of Linear Regression with One Regressor (to read only)
- The Extended Least Squares Assumptions and the OLS Estimator
- Fundamentals of Asymptotic Distribution Theory (basic notions only)
- Asymptotic Distribution of the OLS Estimator and t-Statistic
- Exact Sampling Distributions When the Errors Are Normally Distributed
- Weighted Least Squares (basic notions only)

· The Theory of Multiple Regression (to read only)
- The Linear Multiple Regression Model and the OLS Estimator in Matrix Form
- Asymptotic Distribution of the OLS Estimator and t-Statistic
- Test of Joint Hypotheses