Cross sectional data

Week1
Simple linear regression
Week2
Geometric demonstration and Interpretation
Week3
Interpretation, model selection, and rescaling
Assumptions
Week4
T-test F-test
Week5
Confidence Intervals for the Conditional Mean and Prediction Intervals
Dummy variable
Week 6 Heteroskedasticity
Week 7 Large Sample Properties of OLS

Time series

Topic I

1. Properties of time series data 2. Stationary
3. Autocorrelation and Partial Autocorrelation
4. White Noise and i.i.d.
5. Stationary Autoregressive Time Series
a. Properties of stationary autoregressions
b. Lag length selection criteria
c. Testing for autocorrelation
d. Estimation
6. Forecasting Stationary Autoregressive Time Series
a. Point forecasts using the AR(1) model
b. Interval forecasts using the AR(1) model
c. Forecasting using the AR(p) model
7. Autoregressive Distributed Lag Models
8. Finite Distributed Lag and Static Time Series Models

Topic II

1. Finite Sample Inference with Time Series Data
a. Unbiasedness of the OLS estimator
b. Efficiency of the OLS estimator
c. Hypothesis testing with time series data
2. Consistency of the OLS Estimator
3. Testing for Autocorrelation in the Errors
a. A t test for first-order autocorrelation
b. The Breusch-Godfrey test for autocorrelation
4. Correcting for Autocorrelation in the Errors
a. HAC standard errors
b. Change the model specification
c. Estimate by FGLS
5. Time Series Data with Deterministic Trends
a. Linear deterministic trends
b. Exponential deterministic trends
c. Spurious regressions
d. A detrending interpretation of regressions with a time trend