Course Outline:

Module 1: Introduction to Time Series Analysis and EViews

• Overview of time series data and its characteristics
• Introduction to EViews and its capabilities for time series analysis
• Importing and managing time series data in EViews
• Practical Exercise: Data management and visualization in EViews

Module 2: Stationarity and Unit Root Testing

• Understanding stationarity and non-stationarity
• Conducting unit root tests (Dickey-Fuller, Augmented Dickey-Fuller)
• Interpreting the results of unit root tests
• Case Study: Analyzing stationarity in economic time series data

Module 3: ARIMA Modeling

• Introduction to ARIMA (Autoregressive Integrated Moving Average) models
• Identifying ARIMA models using ACF and PACF
• Estimating and diagnosing ARIMA models in EViews
• Practical Exercise: Building an ARIMA model for forecasting

Module 4: Cointegration and Error Correction Models

• Introduction to cointegration and its significance in time series analysis
• Conducting cointegration tests (Johansen, Engle-Granger)
• Building and interpreting error correction models (ECM)
• Case Study: Application of cointegration analysis in economic research

Module 5: Forecasting and Model Evaluation

• Techniques for forecasting with time series models
• Evaluating forecast accuracy and model performance
• Practical Exercise: Forecasting and assessing model accuracy in EViews
• Real-life Project: Developing a time series forecasting model using real-world data