Package: HonestDiD 0.2.6

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]

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HonestDiD/json (API)

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

Peer review:

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:

difference-in-differencesevent-studiesrobust-inference

7.08 score 175 stars 57 scripts 336 downloads 19 exports 61 dependencies

Last updated 5 months agofrom:e304b8e093. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 12 2024
R-4.5-winOKNov 12 2024
R-4.5-linuxOKNov 12 2024
R-4.4-winOKNov 12 2024
R-4.4-macOKNov 12 2024
R-4.3-winOKNov 12 2024
R-4.3-macOKNov 12 2024

Exports:basisVectorcomputeConditionalCS_DeltaRMcomputeConditionalCS_DeltaRMBcomputeConditionalCS_DeltaRMMcomputeConditionalCS_DeltaSDcomputeConditionalCS_DeltaSDBcomputeConditionalCS_DeltaSDMcomputeConditionalCS_DeltaSDRMcomputeConditionalCS_DeltaSDRMBcomputeConditionalCS_DeltaSDRMMconstructOriginalCScreateEventStudyPlotcreateSensitivityPlotcreateSensitivityPlot_relativeMagnitudescreateSensitivityResultscreateSensitivityResults_relativeMagnitudesDeltaSD_lowerBound_MpreDeltaSD_upperBound_MprefindOptimalFLCI

Dependencies:alabamabitbit64clicodetoolscolorspaceCVXRdplyrECOSolveRfansifarverforeachgenericsggplot2gluegmpgtableisobanditeratorslabelinglatex2explatticelifecyclelpSolveAPImagrittrMASSMatrixmatrixStatsmgcvmunsellmvtnormnleqslvnlmenumDerivosqppillarpkgconfigpracmapurrrqrngR6RColorBrewerRcppRcppArmadilloRcppEigenRglpkrlangRmpfrscalesscsslamspacefillrstringistringrtibbletidyselectTruncatedNormalutf8vctrsviridisLitewithr

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