time-series-econometrics

To test Granger causality and Johansen cointegration in a multivariate time series, you can use this Skill to run tests via statsmodels, producing causality tables and identifying shared long-run trends among economic variables.

You estimate a VAR model and generate impulse response functions using Python by fitting VAR(p) with statsmodels, selecting lag order via information criteria, and producing IRF plots to interpret how shocks propagate through your economic dataset.

You should use VECM instead of VAR for analyzing economic and financial datasets when Johansen cointegration tests confirm shared long-run trends, requiring VECM estimation to capture dynamic adjustment alongside short-run dynamics.

Yes, you can perform forecast error variance decomposition with statsmodels and pandas by fitting a multivariate time-series model and applying the Skill's workflow to quantify the contribution of shocks to forecast errors.

The best way to check VAR stability and interpret forecast error variance decomposition is to fit VAR(p) using statsmodels, verify stability conditions, and generate FEVD outputs to understand shock impacts on variable variance.

Yes, this Skill supports bootstrap-style interpretation readiness for impulse response functions by estimating VAR dynamics with statsmodels and providing outputs designed for advanced shock interpretation in financial datasets.