time-series

Stationarity testing uses ADF and KPSS tests to determine the integration order of your data before fitting an ARIMA model. This ensures your univariate time-series meets the necessary statistical assumptions for accurate forecasting.

Handling cointegration in multivariate time-series analysis requires using VAR or VECM models. This approach identifies long-run equilibrium relationships between non-stationary variables and enables accurate impulse response analysis.

Selecting the right lag order for VAR or VECM models involves using information criteria like AIC and BIC. These metrics evaluate model fit to help you choose the optimal lag length for your econometric time-series.

Yes, you can perform Granger causality tests and residual diagnostics in Python. The workflow supports evaluating forecast accuracy and checking model residuals to validate your econometric time-series assumptions.

VECM should be used instead of VAR for econometric forecasting when your multivariate time-series variables are cointegrated. This handles non-stationary data with long-run equilibrium relationships, while VAR requires stationary data.

Yes, this time-series workflow supports out-of-sample forecasting and model evaluation. It provides end-to-end guidance for generating forecasts and applying residual diagnostics to assess predictive accuracy in Python or R.