Course Outline:

Module 1: Introduction to Time Series Data and Stata Basics

• Overview of time series data and Stata’s time series capabilities
• Importing, managing, and manipulating time series data in Stata
• Understanding stationarity and non-stationarity
• Practical Exercise: Data management and stationarity testing in Stata

Module 2: ARIMA and SARIMA Modeling

• Introduction to autoregressive integrated moving average (ARIMA) models
• Identifying ARIMA models using autocorrelation and partial autocorrelation functions
• Estimating ARIMA models and performing diagnostics
• Forecasting with ARIMA and SARIMA models
• Case Study: Building an ARIMA model for financial time series data

Module 3: Cointegration and Error Correction Models

• Concept of cointegration and its significance in time series analysis
• Estimating and interpreting cointegrated models
• Building vector error correction models (VECM)
• Practical Exercise: Cointegration testing and VECM estimation in Stata

Module 4: Vector Autoregression (VAR) and Impulse Response Functions

• Introduction to VAR modeling for multivariate time series
• Estimating VAR models and interpreting results
• Impulse response functions and variance decomposition
• Case Study: VAR model application in economic forecasting

Module 5: Volatility Modeling and GARCH Models

• Introduction to volatility and ARCH/GARCH models
• Estimating and interpreting GARCH models for time series volatility
• Practical Exercise: GARCH model estimation and volatility forecasting in Stata
• Real-life Project: Time series modeling and forecasting using real-world economic data