**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

**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**

**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