Data Analysis Using EViews Training Course

Participants can import source data and prepare analysis-ready variables in EViews.

• Importing Excel, CSV and text files with correct variable labels
• Checking observations, dates, units, missing values and duplicate records
• Generating ratios, growth rates, logarithms and lagged variables correctly
• Date alignment across series with different frequencies and reporting periods
• Missing-data treatment using deletion, interpolation and explicit data flags
• Data dictionaries recording source, definition, transformation and measurement units

Participants can summarise distributions and identify data patterns requiring investigation.

• Summary statistics for central tendency, dispersion, skewness and kurtosis
• Frequency distributions and histograms for continuous financial and economic variables
• Boxplots identifying outliers, data-entry errors and unusual observations
• Line charts revealing trends, cycles, seasonality and structural breaks
• Scatterplots and correlation matrices for preliminary relationship assessment
• Jarque-Bera normality tests interpreted alongside graphical distribution evidence

Participants can use hypothesis tests and confidence intervals to assess sample evidence.

• Null and alternative hypotheses framed for business and economic questions
• t-tests for coefficients, means and differences between population groups
• Confidence intervals communicating plausible ranges around estimated parameters
• p-values, significance levels and Type I versus Type II errors
• F-tests for joint restrictions across multiple regression coefficients
• Statistical significance distinguished from material economic or financial significance

Participants can estimate and interpret multiple linear regression models in EViews.

• Specifying dependent variables, regressors, intercepts and functional forms
• Estimating ordinary least squares equations through EViews equation objects
• Coefficient interpretation for level-level, log-level and log-log specifications
• R-squared, adjusted R-squared and standard error of regression
• Reading t-statistics, p-values and confidence intervals from EViews output
• Translating regression results into conditional business and policy conclusions

Participants can choose specifications that reflect theory, data structure and decision use.

• Economic theory translating into testable regression relationships and expected signs
• Linear, logarithmic, reciprocal and polynomial functional-form choices
• Dummy variables representing categories, events, seasons and policy periods
• Interaction terms measuring conditional effects across groups or regimes
• Ramsey RESET tests for omitted nonlinear relationships and specification errors
• Comparing nested models using adjusted R-squared and information criteria

Participants can diagnose common regression failures and apply appropriate corrective methods.

• Residual plots examining linearity, variance patterns and unexplained observations
• Breusch-Pagan and White tests for heteroskedasticity detection
• Variance inflation factors identifying problematic multicollinearity among regressors
• Durbin-Watson and Breusch-Godfrey tests for serial correlation
• Robust standard errors correcting inference under heteroskedastic residuals
• Influential observations assessed through leverage, residuals and Cook's distance

Participants can assess whether estimated relationships are stable enough for intended use.

• In-sample fit compared with out-of-sample predictive performance
• Training and holdout samples for honest forecast evaluation
• Chow breakpoint tests for known structural changes and policy events
• CUSUM and CUSUM of squares stability diagnostics
• Recursive coefficient estimates tracking parameter changes through time
• Model limitations documented with assumptions, data constraints and risk statements

Participants can identify non-stationary series and prepare them for time-series modelling.

• Trend, seasonality, cycles and stochastic shocks in economic time series
• Weak stationarity and consequences for regression inference and forecasting
• Augmented Dickey-Fuller tests with intercept, trend and lag choices
• Phillips-Perron and KPSS tests as complementary stationarity evidence
• First differences, log differences and growth-rate transformations
• Correlograms showing autocorrelation and partial autocorrelation structures

Participants can build and evaluate univariate forecasts using autoregressive and moving-average models.

• Autoregressive, moving-average and ARMA model components explained
• Box-Jenkins identification using autocorrelation and partial autocorrelation functions
• ARIMA model estimation with differencing and lag-order selection
• Akaike, Schwarz and Hannan-Quinn criteria for competing model comparison
• Dynamic forecasts, static forecasts and forecast confidence intervals
• RMSE, MAE and MAPE used for forecast-accuracy evaluation

Participants can model seasonal patterns and quantify the effects of discrete events.

• Seasonal dummy variables for monthly, quarterly and weekly reporting cycles
• Seasonal ARIMA terms modelling recurring autocorrelation at fixed intervals
• Trading-day, holiday and calendar effects in higher-frequency data
• Pulse dummies capturing one-off shocks such as strikes or disasters
• Step interventions measuring permanent shifts after regulatory or policy changes
• Forecast adjustments documented separately from statistically estimated model components

Participants can estimate short-run and long-run relationships using distributed lag models.

• Lagged explanatory variables representing delayed economic and financial effects
• Finite distributed lag models estimated with interpretable coefficient patterns
• Autoregressive distributed lag specifications for mixed integration orders
• ARDL bounds testing for long-run level relationships
• Long-run multipliers and short-run dynamics extracted from ARDL output
• Lag selection guided by theory, diagnostics and information criteria

Participants can test long-run relationships and estimate error-correction dynamics.

• Spurious regression risks when non-stationary series move independently
• Engle-Granger two-step cointegration testing for paired economic series
• Johansen trace and maximum-eigenvalue tests for multiple cointegrating vectors
• Error-correction terms measuring adjustment toward long-run equilibrium
• Short-run coefficients separated from long-run equilibrium relationships
• Cointegration results interpreted with economic theory and structural context

Participants can analyse dynamic interactions among jointly determined time-series variables.

• Vector autoregression models treating key variables as jointly endogenous
• Lag-order selection using information criteria and residual diagnostics
• Granger causality tests assessing predictive precedence between variables
• Impulse response functions tracing shock effects through future periods
• Forecast error variance decomposition attributing uncertainty across shocks
• VAR stability conditions checked before interpreting dynamic results

Participants can estimate and select panel-data models for entities observed over time.

• Balanced and unbalanced panels combining cross-sectional and time-series observations
• Pooled ordinary least squares assumptions and limitations for panel datasets
• Fixed-effects models controlling for time-invariant entity characteristics
• Random-effects models estimating entity variation under exogeneity assumptions
• Hausman tests supporting fixed-effects versus random-effects model selection
• Time effects, clustered errors and panel-specific diagnostic considerations

Participants can select and interpret models for binary, censored and count outcomes.

• Binary dependent variables requiring probabilities rather than linear predictions
• Logit and probit models estimated for default, adoption and event outcomes
• Marginal effects translating probability-model coefficients into practical impacts
• Classification tables, sensitivity and specificity for binary model assessment
• Tobit models for censored variables such as constrained expenditure values
• Poisson and negative binomial models for count-data outcomes

Participants can create forecast scenarios and select models using transparent evaluation criteria.

• Baseline forecasts generated from validated regression and time-series models
• Scenario assumptions changing exchange rates, inflation, demand or policy inputs
• Forecast intervals communicating uncertainty rather than single-point estimates
• Actual-versus-forecast tracking identifying bias and model deterioration
• Forecast encompassing tests comparing information contained in competing models
• Model-selection memoranda recording purpose, performance and implementation constraints

Participants can produce reproducible outputs and communicate econometric findings to stakeholders.

• EViews programs automating imports, transformations, estimation and output updates
• Spools organising tables, charts and equations into report-ready output
• Coefficient tables formatted with estimates, standard errors and significance notation
• Charts selected to communicate trends, forecasts, residuals and scenario ranges
• Executive findings separating evidence, interpretation, recommendation and uncertainty
• Technical appendices documenting datasets, equations, diagnostics and replication steps

Participants can build, review and present a complete EViews analysis addressing a defined economic, financial or business decision.

• Problem statements linking a decision question to measurable analytical outcomes
• Source data imported, cleaned, transformed and documented within EViews workfiles
• Model specification justified through theory, exploratory evidence and diagnostic results
• Forecasts or estimated effects evaluated using appropriate validation measures
• Peer review challenges assumptions, interpretation, limitations and decision relevance
• Final presentation communicates findings, uncertainty, recommendations and implementation actions