Dynamic Inferences from Time Series (with Interactions)

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Documentation for package ‘tseffects’ version 0.3.1

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adl.plot Evaluate (and possibly plot) the General Dynamic Response Function (GDRF) for an autoregressive distributed lag (ADL) model, assuming the underlying model is in levels ('d.x' = 'd.y' = 0) and the user wants a marginal effect (the untransformed GDRF). (This is just a wrapper for 'GDRF.adl.plot' with simplifying assumptions)
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 Response Function (GDRF) for a Generalized Error Correction Model (GECM)
gecm.plot Evaluate (and possibly plot) the General Dynamic Response Function (GDRF) for a GECM(1,1) model, assuming the underlying model is in first differences ('x.vrbl.d.x' = 'y.vrbl.d.y' = 0 and 'x.d.vrbl.d.x' = 'y.d.vrbl.d.y' = 1) and the user wants a marginal effect (the untransformed GDRF) and inferences about y in levels to a treatment applied to x in levels. (This is just a wrapper for 'GDRF.gecm.plot' with simplifying assumptions)
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 history
interact.adl.plot Plot the interaction in a single-equation time series model estimated via 'lm'.
pulse.calculator Generate pulse effect formulae for a given autoregressive distributed lag (ADL) model
toy.ts.interaction.data Simulated interactive time series data
yhat.calculator Transform the GDRF formulae to fitted value formulae