Package: HonestDiD 0.2.8

HonestDiD: Robust Inference in Difference-in-Differences and Event Study Designs

Provides functions to conduct robust inference in difference-in-differences and event study designs by implementing the methods developed in Rambachan & Roth (2023) <doi:10.1093/restud/rdad018>, "A More Credible Approach to Parallel Trends" [Previously titled "An Honest Approach..."]. Inference is conducted under a weaker version of the parallel trends assumption. Uniformly valid confidence sets are constructed based upon conditional confidence sets, fixed-length confidence sets and hybridized confidence sets.

Authors:Ashesh Rambachan [aut, cph, cre], Jonathan Roth [aut, cph]

HonestDiD_0.2.8.tar.gz
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HonestDiD_0.2.8.tgz(r-4.6-any)HonestDiD_0.2.8.tgz(r-4.5-any)
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
HonestDiD/json (API)

# Install 'HonestDiD' in R:
install.packages('HonestDiD', repos = c('https://asheshrambachan.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/asheshrambachan/honestdid/issues

Datasets:
  • BCdata_EventStudy - Event study estimates from baseline event study specification on profits in Benzarti & Carloni (2019). See discussion in Section 6.1 of Rambachan & Roth (2021).
  • LWdata_EventStudy - Event study estimates from baseline female specification on employment in Lovenheim & Willen (2019). See discussion in Section 6.2 of Rambachan & Roth (2021).

On CRAN:

Conda:

difference-in-differencesevent-studiesrobust-inference

8.74 score 242 stars 1 packages 431 scripts 2.9k downloads 19 exports 56 dependencies

Last updated from:6813f02ed3. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK155
source / vignettesOK227
linux-release-x86_64OK179
macos-release-arm64OK216
macos-oldrel-arm64OK206
windows-develOK109
windows-releaseOK161
windows-oldrelOK98
wasm-releaseOK150

Exports:basisVectorcomputeConditionalCS_DeltaRMcomputeConditionalCS_DeltaRMBcomputeConditionalCS_DeltaRMMcomputeConditionalCS_DeltaSDcomputeConditionalCS_DeltaSDBcomputeConditionalCS_DeltaSDMcomputeConditionalCS_DeltaSDRMcomputeConditionalCS_DeltaSDRMBcomputeConditionalCS_DeltaSDRMMconstructOriginalCScreateEventStudyPlotcreateSensitivityPlotcreateSensitivityPlot_relativeMagnitudescreateSensitivityResultscreateSensitivityResults_relativeMagnitudesDeltaSD_lowerBound_MpreDeltaSD_upperBound_MprefindOptimalFLCI

Dependencies:alabamabackportscheckmateclarabelclicodetoolscpp11CVXRdplyrECOSolveRfarverforeachgenericsggplot2gluegmpgtablehighsisobanditeratorslabelinglatex2explatticelifecyclelpSolveAPImagrittrMatrixmatrixStatsmvtnormnleqslvnumDerivosqppillarpkgconfigpracmapurrrqrngR6RColorBrewerRcppRcppArmadilloRcppEigenRglpkrlangS7scalesscsslamspacefillrtibbletidyselectTruncatedNormalutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Creates a standard basis vector.basisVector
Event study estimates from baseline event study specification on profits in Benzarti & Carloni (2019). See discussion in Section 6.1 of Rambachan & Roth (2021).BCdata_EventStudy
Computes conditional and hybridized confidence set for Delta = Delta^{RM}(Mbar).computeConditionalCS_DeltaRM
Computes conditional and hybridized confidence set for Delta = Delta^{RMB}(Mbar).computeConditionalCS_DeltaRMB
Computes conditional and hybridized confidence set for Delta = Delta^{RMM}(Mbar).computeConditionalCS_DeltaRMM
Computes conditional and hybridized confidence set for Delta = Delta^{SD}(M).computeConditionalCS_DeltaSD
Computes conditional and hybridized confidence set for Delta = Delta^{SDB}(M).computeConditionalCS_DeltaSDB
Computes conditional and hybridized confidence set for Delta = Delta^{SDM}(M).computeConditionalCS_DeltaSDM
Computes conditional and hybridized confidence set for Delta = Delta^{SDRM}(Mbar).computeConditionalCS_DeltaSDRM
Computes conditional and hybridized confidence set for Delta = Delta^{SDRMB}(Mbar).computeConditionalCS_DeltaSDRMB
Computes conditional and hybridized confidence set for Delta = Delta^{SDRMM}(Mbar).computeConditionalCS_DeltaSDRMM
Constructs original confidence interval for parameter of interest, theta = l_vec'tau.constructOriginalCS
Constructs event study plotcreateEventStudyPlot
Constructs sensitivity plot for Delta = Delta^{SD}(M), Delta^{SDB}(M) and Delta^{SDM}(M)createSensitivityPlot
Constructs sensitivity plot for Delta = Delta^{RM}(Mbar), Delta^{SDRM}{Mbar} and their variants that incorporate additional shape or sign restrictions.createSensitivityPlot_relativeMagnitudes
Constructs robust confidence intervals for Delta = Delta^{SD}(M), Delta^{SDB}(M) and Delta^{SDM}(M) for vector of possible M values.createSensitivityResults
Constructs robust confidence intervals for Delta = Delta^{RM}(Mbar), Delta^{SDRM}(Mbar) and their variants that incorporate shape or sign restrictions for a vector of possible Mbar values.createSensitivityResults_relativeMagnitudes
Construct lower bound for M for Delta = Delta^{SD}(M) based on observed pre-period coefficients.DeltaSD_lowerBound_Mpre
Construct upper bound for M for Delta = Delta^{SD}(M) based on observed pre-period coefficients.DeltaSD_upperBound_Mpre
Constructs optimal fixed length confidence interval for Delta = Delta^{SD}(M).findOptimalFLCI
Event study estimates from baseline female specification on employment in Lovenheim & Willen (2019). See discussion in Section 6.2 of Rambachan & Roth (2021).LWdata_EventStudy