Procedures

CheckData <: StatsStep

Call DiffinDiffsBase.checkdata! for some preliminary checks of the input data.

CheckVars <: StatsStep

Call DiffinDiffsBase.checkvars! to exclude invalid rows for relevant variables.

GroupContrasts <: StatsStep

Call DiffinDiffsBase.groupcontrasts to obtain one of the instances of contrasts that have been grouped by equality (hash) for allowing later comparisons based on object-id.

This step is only useful when working with @specset and proceed.

GroupSample <: StatsStep

Call DiffinDiffsBase.groupsample to obtain one of the instances of esample that have been grouped by equality (hash) for allowing later comparisons based on object-id.

This step is only useful when working with @specset and proceed.

GroupTreatintterms <: StatsStep

Call DiffinDiffsBase.grouptreatintterms to obtain one of the instances of treatintterms that have been grouped by equality (hash) for allowing later comparisons based on object-id.

This step is only useful when working with @specset and proceed.

GroupXterms <: StatsStep

Call DiffinDiffsBase.groupxterms to obtain one of the instances of xterms that have been grouped by equality (hash) for allowing later comparisons based on object-id.

This step is only useful when working with @specset and proceed.

MakeWeights <: StatsStep

Call DiffinDiffsBase.makeweights to create a generic Weights vector.

checkdata!(args...)

Check data is Tables.AbstractColumns-compatible and find valid rows for options subset and weightname. See also CheckData.

checkvars!(args...)

Exclude rows with missing data or violate the overlap condition and find rows with data from treated units. See also CheckVars.

groupcontrasts(contrasts)

Return the argument without change for allowing later comparisons based on object-id. See also GroupContrasts.

groupsample(esample)

Return the argument without change for allowing later comparisons based on object-id. See also GroupSample.

grouptreatintterms(treatintterms)

Return the argument without change for allowing later comparisons based on object-id. See also GroupTreatintterms.

groupxterms(xterms)

Return the argument without change for allowing later comparisons based on object-id. See also GroupXterms.

makeweights(args...)

Construct a generic Weights vector. See also MakeWeights.

CheckFEs <: StatsStep

Call InteractionWeightedDIDs.checkfes! to drop any singleton observation from fixed effects over the relevant subsample.

CheckVcov <: StatsStep

Call InteractionWeightedDIDs.checkvcov! to exclude rows that are invalid for variance-covariance estimator.

EstVcov <: StatsStep

Call InteractionWeightedDIDs.estvcov to estimate variance-covariance matrix and F-statistic.

GroupFEterms <: StatsStep

Call InteractionWeightedDIDs.groupfeterms to obtain one of the instances of feterms that have been grouped by equality (hash) for allowing later comparisons based on object-id.

This step is only useful when working with @specset and proceed.

MakeFESolver <: StatsStep

Call InteractionWeightedDIDs.makefesolver to construct the fixed effect solver.

MakeFEs <: StatsStep

Call InteractionWeightedDIDs.makefes to construct FixedEffects from data (the full sample).

MakeTreatCols <: StatsStep

Call InteractionWeightedDIDs.maketreatcols to obtain residualized binary columns that capture treatment effects and obtain cell-level weight sums and observation counts.

MakeYXCols <: StatsStep

Call InteractionWeightedDIDs.makeyxcols to obtain residualized outcome variables and covariates.

ParseFEterms <: StatsStep

Call InteractionWeightedDIDs.parsefeterms! to extract any FixedEffectTerm or interaction of FixedEffectTerm from xterms. Whether any intercept term should be omitted is also determined.

SolveLeastSquares <: StatsStep

Call InteractionWeightedDIDs.solveleastsquares! to solve the least squares problem for regression coefficients and residuals.

SolveLeastSquaresWeights <: StatsStep

Call InteractionWeightedDIDs.solveleastsquaresweights to solve the cell-level weights assigned by least squares. If lswtnames is not specified, cells are defined by the partition based on treatment time and calendar time.

checkfes!(args...)

Drop any singleton observation from fixed effects over the relevant subsample. See also CheckFEs.

checkvcov!(args...)

Exclude rows that are invalid for variance-covariance estimator. See also CheckVcov.

estvcov(args...)

Estimate variance-covariance matrix and F-statistic. See also EstVcov.

groupfeterms(feterms)

Return the argument without change for allowing later comparisons based on object-id. See also GroupFEterms.

makefes(args...)

Construct FixedEffects from data (the full sample). See also MakeFEs.

makefesolver(args...)

Construct FixedEffects.AbstractFixedEffectSolver. See also MakeFESolver.

maketreatcols(args...)

Construct residualized binary columns that capture treatment effects and obtain cell-level weight sums and observation counts. See also MakeTreatCols.

makeyxcols(args...)

Construct columns for outcome variables and covariates and residualize them with fixed effects. See also MakeYXCols.

parsefeterms!(xterms)

Extract any FixedEffectTerm or interaction of FixedEffectTerm from xterms and determine whether any intercept term should be omitted. See also ParseFEterms.

solveleastsquares!(args...)

Solve the least squares problem for regression coefficients and residuals. See also SolveLeastSquares.

solveleastsquaresweights(args...)

Solve the cell-level weights assigned by least squares. If lswtnames is not specified, cells are defined by the partition based on treatment time and calendar time. See also SolveLeastSquaresWeights.