Course Content

This intensive five-day course combines lectures with hands-on implementation in EViews. Each day introduces new concepts and practical exercises that enable participants to build, estimate, evaluate, and use macroeconometric models for forecasting and policy analysis.

Monday – Foundations of Macroeconometric Modeling

What you will learn and implement

• Introduction to macroeconometric modeling
• Setting up EViews workfiles
• Time series data: nature and frequency
• Data collection, processing, and transformations
• Importing data into EViews
• Descriptive statistics in EViews
• Least squares estimation
• Assumptions of least squares estimation and hypothesis testing
• Model misspecification testing
• Correcting standard errors for heteroskedasticity and serial correlation
• Detecting omitted and redundant variables
• Identifying and accounting for outliers
• If time permits: Structural break analysis

Tuesday – Time Series Models and Vector Autoregressions

What you will learn and implement

• Modeling stationary variables
• Autoregressive (AR) models: specification and lag selection
• Forecasting with autoregressive models
• Introduction to Vector Autoregressive (VAR) models
• VAR representation and interpretation
• Lag selection and testing for intervening lags
• General-to-Specific approach to VAR estimation
• Granger causality and block exogeneity tests

Wednesday – Nonstationarity, Cointegration, and Error Correction

What you will learn and implement

• Seasonality and seasonal adjustment techniques in EViews
• Nonstationarity and unit roots
• Unit root testing
• Order of integration and differencing
• Detrending trend-stationary variables
• Why cointegration matters: long-run relationships versus spurious regression
• Error correction models
• Residual-based cointegration tests
• Engle–Granger cointegration testing in EViews
• Single-equation cointegration methods
• Estimating Autoregressive Distributed Lag (ARDL) models
• ARDL Bounds testing
• System-based cointegration methods
• Estimating Vector Autoregressive (VAR) and Vector Error Correction (VEC) models
• Johansen cointegration testing
• Short-run estimation using error correction models

Thursday – Building a Macroeconometric Model

What you will learn and implement

• Estimating behavioral equations
• Theory-driven (structural) modeling
• Data-driven (statistical) modeling
• Hybrid modeling approaches
• Correcting for outliers and structural breaks
• Building a prototype macroeconometric model in EViews
• Using the EViews Model object
• Model estimation and diagnostic testing
• Evaluating predictive performance
• In-sample and out-of-sample forecasting
• Model calibration and adjustment
• Updating models with new data and revised equations

Friday – Forecasting, Simulation, and Policy Analysis

What you will learn and implement

• Understanding model inputs and outputs
• Designing forecasting scenarios
• Deterministic versus stochastic simulations
• Creating forecasts in EViews
• Developing paths for exogenous variables
• Solving models using alternative solution methods
• Using add factors in model solutions
• Conducting "what-if" policy simulations
• Iterative simulations to achieve policy targets
• Reporting simulation results using levels and growth rates
• Interpretation of simulation results and policy implications
• Forecast evaluation in theory and in EViews