createSensitivityResults: Constructs robust confidence intervals for Delta =...

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

Constructs robust confidence intervals for \Delta = \Delta^{SD}(M), \Delta^{SDB}(M) and \Delta^{SDM}(M) for vector of possible M values.

Description

Constructs robust confidence intervals for a choice \Delta = \Delta^{SD}(M), \Delta^{SDB}(M) and \Delta^{SDM}(M) for vector of possible M values. By default, the function constructs robust confidence intervals for \Delta^{SD}(M).

Usage

createSensitivityResults(betahat, sigma,
                         numPrePeriods, numPostPeriods,
                         method = NULL,
                         Mvec = NULL,
                         l_vec = .basisVector(index = 1, size = numPostPeriods),
                         monotonicityDirection = NULL,
                         biasDirection = NULL,
                         alpha = 0.05,
                         parallel = FALSE,
                         seed = 0)

Arguments

betahat	Vector of estimated event study coefficients.
sigma	Covariance matrix of event study coefficients.
numPrePeriods	Number of pre-periods.
numPostPeriods	Number of post-periods.
method	String that specifies the choice of method for constructing robust confidence intervals. This must be one of "FLCI", "Conditional", "C-F" (conditional FLCI hybrid), or "C-LF" (conditional least-favorable hybrid). Default equals NULL and the function automatically sets method based on the recommendations in Rambachan & Roth (2021) depending on the choice of Delta. If Delta = DeltaSD, default selects the FLCI. If Delta = DeltaSDB or DeltaSDM, default delects the conditional FLCI hybrid.
Mvec	Vector of M values for which the user wishes to construct robust confidence intervals. If NULL, the function constructs a grid of length 10 that starts at M = 0 and ends at M equal to the upper bound constructed from the pre-periods using the function DeltaSD_upperBound_Mpre if number of pre-periods > 1 or the standard deviation of the first pre-period coefficient if number of pre-periods = 1. Default equals null.
l_vec	Vector of length numPostPeriods that describes the scalar parameter of interest, theta = l_vec'tau. Default equals to first basis vector, (1, 0, ..., 0)
biasDirection	This must be specified if the user wishes to add an additional bias restriction to \Delta^{SD}(M). If "positive", bias is restricted to be positive, \delta \ge 0. If "negative", bias is restricted to be negative, \delta \le 0. Default equals NULL.
monotonicityDirection	This must be specified if the user wishes to add an additional monotonicity restriction to \Delta^{SD}(M). If "increasing", underlying trend specified to be increasing, \delta_t \ge \delta_{t-1}. If "decreasing", underlying trend specified to be decreasing \delta_t \le \delta_{t-1}. Default equals NULL
alpha	Desired size of the robust confidence sets. Default equals 0.05 (corresponding to 95% confidence interval)
parallel	Logical to indicate whether the user would like to construct the robust confidence intervals in parallel. This uses the Foreach package and doParallel package. Default equals FALSE.
seed	Random seed for internal computations; included for reproducibility.

Value

Returns a dataframe with columns

lb	Lower bound of robust confidence sets.
ub	Upper bound of robust confidence sets.
method	Method for constructing robust confidence sets
Delta	The set Delta that was specified.
M	Values of M associated with each robust confidence set.

Author(s)

Ashesh Rambachan

References

Rambachan, Ashesh and Jonathan Roth. "An Honest Approach to Parallel Trends." 2021.

Examples

  # Simple use case; for more detailed examples,
  # see <https://github.com/asheshrambachan/HonestDiD#honestdid>
  createSensitivityResults(betahat        = BCdata_EventStudy$betahat,
                           sigma          = BCdata_EventStudy$sigma,
                           numPrePeriods  = length(BCdata_EventStudy$prePeriodIndices),
                           numPostPeriods = length(BCdata_EventStudy$postPeriodIndices),
                           alpha          = 0.05)

asheshrambachan/HonestDiD documentation built on March 6, 2025, 3:19 a.m.