0. Intro

• w1. Intro to R Webpage

1. Autocorrelation

• w2a. Random Sample vs TS
• w2b. ACVF and ACF
• w3a. Cov and Cor Review
• w3b. Stationarity
• w3c. Testing Mean
• w4a. Deterministic and Random Trend
• w4b. Filters to Reveal Trend

2. Autoregressive Model

• w4c. AR(1)
• w5a. Fitting AR(1) and Param. Sig.
• w5b. Causal Representation
• w5c. Yule-Walker and PACF
• w5d. Fitting AR(p) with/wo Mean
• w6a. Order Selection
• w6b. Residual Analysis
• w6c. Randomness.tests
• w7a. Fitting AR(p) Recap
• w7b. Modeling Assignment 1

Modeling Guideline

3. Moving Average Model

• w8a. MA(q) model
• w8b. Invertible Representation

4. ARMA Model

• w8c. ARMA(p,q)
• w8d. Parameter Estimation
• w8e. Initial Values for MLE
• w8f. Fitting ARMA(p,q) Example
• w9a. Forecasting AR and MA
• w9b. Static vs Rolling Forecasting
• w10a. Forecasting ARMA
• w10b. Forecasting Example
• w10c. Box-Cox Power Transformation

5. ARIMA Model

• 5a. Stationarity.tests()
• 5b. Box-Jenkins Differencing
• 5c. ARIMA Model
• 5d. ARIMA Forecasting
• 5e. Picking Trend Types
• 5f. Lake Huron Modeling
• 5g. Forecasting Lake Huron

6. Seasonal ARIMA

• 6a. Seasonality
• 6b. Seasona ARIMA
• 6c. Checks for SARIMA
• 6d. Forecasting sARIMA

7. Cointegration

• 7a. Cointegraton

8. GARCH Model

• 8a. GARCH model
• 8b. Forecasting Sigma
• 8c. ARMA-GARCH