EViews Helper-AI econometrics assistant

Example

Import price series from CSV/Excel, Bloomberg or online providers; construct adjusted close if splits/dividends available; compute log returns and rolling realized measures (rolling SD, realized variance) and create calendar/frequency conversions (e.g., convert intraday to daily).

Scenario

A researcher receives 10 years of daily OHLC data. Steps: (1) use EViews' Workfile wizard or wfopen to load the CSV; (2) create adjusted price if necessary and create series logret = log(close) - log(close(-1)); (3) create month and year indicators and handle missing days due to holidays by forward/back-filling only where appropriate; (4) create rolling 20-day volatility series using a simple series: rollsd20 = @movstd(logret,20) or equivalent; (5) flag outliers and winsorize if justified before estimation. This ensures the dataset entering ARIMA/GARCH/VAR models is clean and documented.

Example

Estimate ARIMA for short-horizon return forecasting; specify and estimate GARCH(1,1) or GJR/GARCH for asymmetric volatility; run unit root tests (ADF, PP), Engle-Granger or Johansen cointegration tests, and estimate VECM for long-run relationships.

Scenario

A portfolio manager wants volatility forecasts to size positions and a researcher wants to model short-term return predictability. Steps: (1) specify and estimate a mean model for returns (e.g., AR(1), or AR(1)+exogenous macro regressor); (2) perform ARCH-LM on residuals to check for conditional heteroskedasticity; (3) if present, estimate a GARCH(1,1) or a skewed-t innovation GARCH, inspect persistence (alpha+beta) and leverage terms if using GJR/TGARCH; (4) for two or more price series, run ADF tests, then Johansen to detect cointegration, and estimate a VECM with the appropriate lag length determined by information criteria. Practical notes: choose error distribution by inspecting residual kurtosis; test robustness by re-estimating under different lags and by using rolling-window estimation to detect parameter instability.

Example

Run Ljung-Box and ARCH tests, plot standardized residuals, generate h-step-ahead forecasts with confidence intervals, perform Monte Carlo simulation for forecast uncertainty, and automate repeated estimation across a basket of assets with program scripts and batch export to Excel and Word-ready tables/graphs.

Scenario

A risk officer must deliver monthly VaR and backtest results to compliance. Steps: (1) estimate conditional volatility model and produce 1- and 10-day sigma forecasts; (2) convert sigma forecasts into VaR (e.g., VaR = mu_forecast + z_alpha * sigma_forecast where z_alpha is the quantile appropriate for the assumed innovation distribution); (3) run backtests (Kupiec’s proportion of failures and Christoffersen’s independence test) and produce a results table; (4) if required, run stress scenarios and Monte Carlo shock paths to produce distributional fan-charts; (5) automate the whole pipeline using an EViews program that loops over tickers, saves results as Excel sheets, and produces snapshot PowerPoint/Word outputs for board reporting. Diagnostics help identify model misspecification before reporting.