fetwfe (version 1.5.0)

twfeCovsWithSimulatedData: Run twfeCovs on Simulated Data

Description

This function runs the bridge-penalized extended two-way fixed effects estimator (twfeCovs()) on simulated data. It is simply a wrapper for twfeCovs(): it accepts an object of class "FETWFE_simulated" (produced by simulateData()) and unpacks the necessary components to pass to twfeCovs(). So the outputs match twfeCovs(), and the needed inputs match their counterparts in twfeCovs().

Usage

twfeCovsWithSimulatedData(
  simulated_obj,
  verbose = FALSE,
  alpha = 0.05,
  add_ridge = FALSE
)

Value

A named list with the following elements:

att_hat

The estimated overall average treatment effect for a randomly selected treated unit.

att_se

If q < 1, a standard error for the ATT. If indep_counts was provided, this standard error is asymptotically exact; if not, it is asymptotically conservative. If q >= 1, this will be NA.

catt_hats

A named vector containing the estimated average treatment effects for each cohort.

catt_ses

If q < 1, a named vector containing the (asymptotically exact, non-conservative) standard errors for the estimated average treatment effects within each cohort.

cohort_probs

A vector of the estimated probabilities of being in each cohort conditional on being treated, which was used in calculating att_hat. If indep_counts was provided, cohort_probs was calculated from that; otherwise, it was calculated from the counts of units in each treated cohort in pdata.

catt_df

A dataframe displaying the cohort names, average treatment effects, standard errors, and 1 - alpha confidence interval bounds.

beta_hat

The full vector of estimated coefficients.

treat_inds

The indices of beta_hat corresponding to the treatment effects for each cohort at each time.

treat_int_inds

The indices of beta_hat corresponding to the interactions between the treatment effects for each cohort at each time and the covariates.

sig_eps_sq

Either the provided sig_eps_sq or the estimated one, if a value wasn't provided.

sig_eps_c_sq

Either the provided sig_eps_c_sq or the estimated one, if a value wasn't provided.

X_ints

The design matrix created containing all interactions, time and cohort dummies, etc.

y

The vector of responses, containing nrow(X_ints) entries.

X_final

The design matrix after applying the change in coordinates to fit the model and also multiplying on the left by the square root inverse of the estimated covariance matrix for each unit.

y_final

The final response after multiplying on the left by the square root inverse of the estimated covariance matrix for each unit.

N

The final number of units that were in the data set used for estimation (after any units may have been removed because they were treated in the first time period).

T

The number of time periods in the final data set.

R

The final number of treated cohorts that appear in the final data set.

d

The final number of covariates that appear in the final data set (after any covariates may have been removed because they contained missing values or all contained the same value for every unit).

p

The final number of columns in the full set of covariates used to estimate the model.

Arguments

simulated_obj

An object of class "FETWFE_simulated" containing the simulated panel data and design matrix.

verbose

Logical; if TRUE, more details on the progress of the function will be printed as the function executes. Default is FALSE.

alpha

Numeric; function will calculate (1 - alpha) confidence intervals for the cohort average treatment effects that will be returned in catt_df.

add_ridge

(Optional.) Logical; if TRUE, adds a small amount of ridge regularization to the (untransformed) coefficients to stabilize estimation. Default is FALSE.

Examples

if (FALSE) {
  # Generate coefficients
  coefs <- genCoefs(R = 5, T = 30, d = 12, density = 0.1, eff_size = 2, seed = 123)

  # Simulate data using the coefficients
  sim_data <- simulateData(coefs, N = 120, sig_eps_sq = 5, sig_eps_c_sq = 5)

  result <- twfeCovsWithSimulatedData(sim_data)
}