plausibounds (version 1.0.1)

plausible_bounds: Calculate Plausible Bounds

Description

This function calculates the plausible bounds for a vector of estimates along with average treatment effect, Wald tests, and optional pointwise/sup-t bounds. Supports pre-treatment periods for event study designs.

Usage

plausible_bounds(
  estimates,
  var,
  alpha = 0.05,
  preperiods = 0,
  include_pointwise = TRUE,
  include_supt = TRUE,
  parallel = FALSE,
  n_cores = NULL
)

Value

A list containing:

alpha

Significance level

preperiods

Number of pre-treatment periods

wald_test

List with post (and pre if preperiods > 0) Wald test results

restricted_bounds

Data frame with horizon, unrestr_est, restr_est, lower, upper

restricted_bounds_metadata

List with supt_critval, supt_b, degrees_of_freedom, K, lambda1, lambda2, restr_class, best_fit_model

avg_treatment_effect

List with estimate, se, lower, upper

pointwise_bounds

List with lower and upper vectors (if include_pointwise = TRUE)

supt_bounds

List with lower and upper vectors (if include_supt = TRUE)

Arguments

estimates

A numeric vector or single-row/single-column matrix of point estimates. If preperiods > 0, the first preperiods elements are pre-treatment estimates, followed by post-treatment estimates.

var

The variance-covariance matrix of the estimates

alpha

Significance level (default: 0.05)

preperiods

Number of pre-treatment periods (default: 0). Period 0 is assumed to be normalized and not included in estimates.

include_pointwise

Whether to include pointwise bounds (default: TRUE)

include_supt

Whether to include sup-t bounds (default: TRUE)

parallel

Whether to use parallel processing for restricted bounds calculation (default: FALSE)

n_cores

Number of cores to use for parallel processing (default: NULL, which uses detectCores() - 1). Only used when parallel = TRUE.

Examples

# Example with constant estimates and no correlation (simple case)
data(estimates_constant)
data(var_constant)
pb <- plausible_bounds(estimates_constant[1:4], var_constant[1:4, 1:4])
print(pb)
summary(pb)