computeConditionalCS_DeltaRM: Computes conditional and hybridized confidence set for Delta...

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

Computes conditional and hybridized confidence set for \Delta = \Delta^{RM}(Mbar).

Description

Computes the conditional confidence set and hybridized confidence set for \Delta = \Delta^{RM}(Mbar).

Usage

computeConditionalCS_DeltaRM(betahat, sigma, numPrePeriods, numPostPeriods,
                         l_vec = .basisVector(index = 1, size = numPostPeriods), Mbar = 0,
                         alpha = 0.05, hybrid_flag = "LF", hybrid_kappa = alpha/10,
                         returnLength = FALSE, postPeriodMomentsOnly = TRUE,
                         gridPoints=10^3, grid.ub = NA, grid.lb = NA, 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.
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)
Mbar	Tuning parameter Mbar for \Delta^{RM}(Mbar) that governs how different the maximal pre-period violation of parallel trends may be from the post-period differential trend. Default sets Mbar = 0. See Section 2.3.2 of Rambachan & Roth (2021) for more details.
alpha	Desired level of the confidence set. Default equals 0.05 (corresponding to 95% confidence interval)
hybrid_flag	Flag for whether user wishes to compute a hybridized confidence set. "ARP" specifies the conditional confidence set "LF" specifies the conditional least-favorable confidence set. The conditional FLCI hybrid confidence set is not available for \Delta^{RM}(Mbar) since the FLCI is infinite length for this choice of \Delta. See Section 3.3 and Section 5.3 of Rambachan & Roth (2021) for details. Default equals "LF".
hybrid_kappa	Desired first-stage size of hybridized confidence set. Only specify this value if the user wishes to compute a hybridized confidence set. Default equals alpha/10. If user specifies hybrid_flag = "ARP", set this value to NULL.
returnLength	Logical value. If TRUE, function only returns the length of the robust confidence. If FALSE, function returns dataframe that contains a grid of possible parameter values and a vector of zeros and ones associated with each value in the grid (one denotes that the grid value lies in the confidence set and zero denotes that the grid value does not fall within the confidence set.) Default equals FALSE.
postPeriodMomentsOnly	Logical value. If TRUE, function excludes moments for \Delta^{RM}(Mbar) that only include pre-period coefficients. Default equals TRUE.
gridPoints	Number of grid points used in test inversion step. Default equals 1000.
grid.ub	Upper bound of grid for test inversion. The user should only specify this if she wishes to manually specify the upper bound of the grid. Default equals NA and sets grid upper bound to equal the upper bound of the identified set under parallel trends plus 20*standard deviation of the point estimate, l_vec'betahat.
grid.lb	Lower bound of grid for test inversion. The user should only specify this if she wishes to manually specify the upper bound of the grid. Default equals NA sets grid lower bound to equal the lower bound of the identified set under parallel trends minus 20*standard deviation of the point estimate, l_vec'betahat.
seed	Random seed for internal computations; included for reproducibility.

Value

If returnLength equals TRUE, function returns a scalar that equals the length of the confidence interval. If returnLength equals FALSE, function returns a dataframe with columns

grid	Vector of grid values used to construct the confidence interval by test inversion.
accept	Vector of zeros-ones associated with grid values, where one denotes a grid value that falls within the confidence interval and zero denotes a grid value that falls outside the confidence interval.

Author(s)

Ashesh Rambachan

References

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

asheshrambachan/HonestDiD documentation built on July 15, 2024, 12:56 p.m.