EViews Time Series Econometric Forecasting Training Course

Course overview

Forecasts for inflation, interest rates, exchange rates, sales volumes, credit losses and macroeconomic indicators are often produced under time pressure, yet weak model specification, unmanaged data revisions and untested assumptions can make them difficult to defend. This course addresses the practical challenge of building forecasts in EViews that are statistically sound, transparent to reviewers and usable in budgeting, treasury, risk, planning and investment decisions. Participants learn how to move from raw economic data to forecast outputs with documented methods and clear performance evidence.

The course covers the EViews workflow for importing, structuring and transforming time-series data; visualising trends, seasonality and structural breaks; estimating ARIMA, dynamic regression, ARDL, VAR and VECM models; and producing point, interval and scenario forecasts. Participants apply stationarity tests, cointegration testing, residual diagnostics, lag-selection criteria and out-of-sample forecast comparison measures. They gain practical command of EViews workfiles, series objects, equation objects, graphs, programmes and forecast procedures, with attention to model choices that matter in economic and financial reporting.

Teaching combines instructor-led explanation with guided EViews modelling sessions using realistic macroeconomic and financial datasets. Each participant works through a structured forecasting case, investigates competing specifications, records diagnostic evidence and communicates results to a non-technical decision-maker. By the end of the week, participants leave with an EViews forecasting workfile, reproducible programme code, model comparison results, forecast charts and a concise forecasting report template that can be adapted to their organisation's data and reporting cycle.

Course objectives

By the end of this course, participants will be able to:

• Construct EViews workfiles and import time-series data from Excel, CSV and external data sources
• Transform economic series using logarithms, growth rates, deflation and seasonal-adjustment checks
• Test for stationarity using graphical analysis, correlograms, Augmented Dickey-Fuller and Phillips-Perron tests
• Estimate and diagnose ARIMA and dynamic regression forecasting models in EViews
• Specify ARDL models and interpret short-run dynamics and long-run relationships
• Build VAR and VECM models using lag-order selection, impulse responses and cointegration tests
• Evaluate competing forecasts using holdout samples, RMSE, MAE, MAPE and forecast-encompassing evidence
• Produce a reproducible EViews forecast pack containing programme code, charts, scenarios and model documentation

Benefits of attending

Target competencies

Who should attend

• Economists — who prepare macroeconomic projections for policy, strategy or market analysis
• Financial Analysts — who use interest-rate, inflation, exchange-rate or market forecasts in valuation and planning
• FP&A Managers — who need defensible external-driver forecasts for budgets and rolling forecasts
• Treasury Analysts — who assess rate and currency scenarios for funding, liquidity and hedging decisions
• Credit Risk Analysts — who develop macroeconomic inputs for portfolio monitoring and stress testing
• Quantitative Researchers — who need an applied EViews workflow for estimating and validating time-series models

Requirements and prerequisites

Participants should be comfortable working with spreadsheets and interpreting basic descriptive statistics, regression output and economic charts. Prior exposure to concepts such as correlation, ordinary least squares, p-values, confidence intervals and time ordering of data is assumed. Experience with EViews is helpful but not essential: the course starts with workfiles, series objects, commands and menus before moving into model building. Participants should bring a laptop with a current licensed or trial installation of EViews where possible. Advanced calculus, programming experience, prior ARIMA modelling or specialist econometrics software knowledge are not required.

Training methodology

The instructor demonstrates each modelling decision in EViews, then participants replicate it on supplied economic and financial datasets before adapting it to a case question. Short lectures establish the econometric rationale; hands-on labs cover commands, menus, workfile objects and output interpretation. Teams compare alternative model specifications and challenge each other's forecast assumptions using diagnostic evidence. The final day includes an applied forecasting workshop in which participants prepare a model recommendation, forecast charts and a practical plan for transferring the workflow to their own reporting cycle.

Course outline

Day 1: EViews workflow and time-series data preparation

• EViews workfiles, pages, frequency settings and date structures
• Importing Excel and CSV data into EViews series objects
• Managing missing values, outliers and data revisions
• Transforming series with logs, differences, growth rates and index rebasing
• Creating sample ranges, holdout periods and forecast evaluation windows
• Time-series graphs, descriptive statistics and seasonal pattern inspection
• EViews programmes, object naming conventions and reproducible workflow design

Workshop: Participants build an EViews workfile from raw macroeconomic data and produce a documented, analysis-ready dataset with a reserved holdout sample.

Day 2: Univariate forecasting with ARIMA models

• Autocorrelation and partial autocorrelation functions in EViews
• Augmented Dickey-Fuller and Phillips-Perron unit-root testing
• Differencing strategies and integration order assessment
• AR, MA, ARMA and ARIMA model specification
• Seasonal ARIMA components and seasonal differencing
• Information criteria for lag and model-order selection
• Residual diagnostics using correlograms, normality and serial-correlation tests

Workshop: Participants estimate and compare ARIMA models for monthly inflation, then generate point forecasts and forecast intervals for the holdout period.

Day 3: Dynamic regression and long-run relationships

• Regression forecasting with deterministic trends and seasonal dummies
• Distributed-lag and autoregressive distributed-lag specifications
• ARDL bounds testing for long-run relationships
• Error-correction model interpretation and coefficient reporting
• Structural-break testing with Chow tests and breakpoint analysis
• Forecasting with exogenous drivers and conditional assumptions
• Multicollinearity, heteroskedasticity and residual-stability diagnostics

Workshop: Participants develop an ARDL forecast model for a business or economic indicator and document the short-run drivers, long-run relationship and key assumptions.

Day 4: Multivariate models, VAR and VECM forecasting

• Vector autoregression structure and endogenous variable selection
• Lag-length selection using AIC, SIC and Hannan-Quinn criteria
• Johansen cointegration testing in EViews
• Vector error-correction model specification and interpretation
• Granger-causality testing and predictive relevance
• Impulse response functions and forecast error variance decomposition
• VAR and VECM dynamic forecasts under alternative macroeconomic paths

Workshop: Participants construct a small VAR or VECM for interest rates, inflation and output, then interpret the forecast implications of a rate shock.

Day 5: Forecast evaluation, scenarios and reporting

• Static, dynamic and stochastic forecast procedures in EViews
• Out-of-sample design and rolling forecast-origin evaluation
• Forecast accuracy measures including RMSE, MAE, MAPE and Theil inequality coefficient
• Diebold-Mariano testing for comparative forecast accuracy
• Baseline, upside and downside scenario construction
• Forecast charts, tables and model-output export to Excel
• Forecast documentation, model limitations and review-ready reporting

Workshop: Participants complete a forecast pack containing competing-model results, scenario forecasts, performance metrics, EViews programme code and a management recommendation.

Tools & standards covered

EViews 13, Microsoft Excel, Federal Reserve Economic Data (FRED), Statistical Data and Metadata eXchange (SDMX)

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions