R: Dynamic Inferences from Time Series (with Interactions)

# Dynamic Inferences from Time Series (with Interactions)

## Documentation for package ‘tseffects’ version 0.2.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

approval | Data on US Presidential Approval
GDRF.adl.plot | Evaluate (and possibly plot) the General Dynamic Response Function (GDRF) for an autoregressive distributed lag (ADL) model
GDRF.gecm.plot | Evaluate (and possibly plot) the General Dynamic Treatment Effect (GDRF) for a Generalized Error Correction Model (GECM)
GDTE.adl.plot | Evaluate (and possibly plot) the General Dynamic Treatment Effect (GDTE) for an autoregressive distributed lag (ADL) model
GDTE.gecm.plot | Evaluate (and possibly plot) the General Dynamic Treatment Effect (GDTE) for a Generalized Error Correction Model (GECM)
gecm.to.adl | Translate the coefficients from the General Error Correction Model (GECM) to the autoregressive distributed lag (ADL) model
general.calculator | Generate the generalized effect formulae for an autoregressive distributed lag (ADL) model, given pulse effects and shock/treatment history
interact.adl.plot | Plot the interaction in a single-equation time series model estimated via 'lm'. It is imperative that you double-check you have referenced all x, y, z, and interaction terms through 'x.vrbl', 'y.vrbl', 'z.vrbl', and 'x.z.vrbl'. You must also have their orders correctly entered. 'interact.adl.plot' has no way of determining, from the variable list, which correspond with which
pulse.calculator | Generate pulse effect formulae for a given autoregressive distributed lag (ADL) model
toy.ts.interaction.data | Simulated interactive time series data