compute_ATE: Computes the bounds on the AME on the dataset, at all periods...

In cgaillac/MarginalFElogit: Estimation of Average Marginal Effects in Fixed Effect Logit Models

Computes the bounds on the AME on the dataset, at all periods given in compute_T, using the method specified as entry parameter.

Description

Computes the bounds on the AME on the dataset, at all periods given in compute_T, using the method specified as entry parameter.

Usage

compute_ATE(
  data,
  estimators = env(),
  selectX = NULL,
  compute_T = "all",
  Option = "quick",
  CIOption = "CI2",
  alpha = 0.05,
  nbCores = 4
)

Arguments

data	is an environment variable containing the data. If not already formatted, it will be formatted by the format_data function (see its documentation for information about the stored variables). It must contain at least: - data$Y is a matrix of size n x Tmax containing the values of the dependent variable Y, - data$X is an array of size n x Tmax x dimX containing the values of the covariates X, - data$clusterIndexes is a vector of size n x 1 that specifies the cluster each observation pertains to. If it does not exist, the function enforces the default setting of i.i.d. observations - the parameter takes value 1:n so that each observation is its own cluster.
estimators	(default empty environment) is an environment in which the results of the CMLE estimation and of the non-parametric estimation of the conditional distribution of S will be stored. That way, the results will be saved during the first call of the compute_ATE_t function and will not need to be computed in the subsequent calls. Results are stored as follows: - estimators$beta_hat, a list which contains the results from CMLE estimation: - estimators$beta_hat$beta_hat a vector of length dimX, the estimated value for the slope parameter. - estimators_beta_hat$phi_b a matrix of size n x dimX containing the value of the influence function at each observation (rows) w.r.t. each dimension of the covariates (columns). - estimators$beta_hat$var_b the estimated asymptotic covariance matrix, of size dimX x dimX, for the estimator beta_hat. - estimators$h_local_lin_proba a vector of length (Tmax + 1) containing, in j-th position, the bandwidth used to estimate the P(S = j - 1 | X)'s. - estimators$condlProbas: a matrix of size n x (Tmax + 1) containing, in position (i, j), the estimate for P(S = j - 1 | X) at the i-th observation. - estimators$densityEstimate a matrix of size n x (Tmax + 1) containing, at each row (individual), the estimated density for having covariates (X_1, ..., X_T). Each column represents the value found using the corresponding bandwidths from h.
selectX	(default NULL) a vector containing the indices of the covariates w.r.t. which the AME must be computed. If null, the AME w.r.t. all covariates will be computed. All variables of interest should be given in the same call, as then there will be no additional cost relative to estimating w.r.t. only one covariate.
compute_T	(default "all") is a vector containing all periods at which the ATE must be computed. Alternatively, it can be "all", in which case the ATE will be computed successively at every period and, on top of that, the average ATE across all periods will also be computed using the function compute_average_AMTE. Also note that non-positive values will be counted backwards from the last period at which each individual is observed, as in an event-study.
Option	(default "quick") Estimation method to be used. If "quick" or "outer" (case-insensitive) the outer bounds are computed. Otherwise, the sharp bounds are computed. We recommend using the outer bounds method if the number of covariates is at least three, if the number of periods observed is four or more, or if the sample size is small (less than 500) or large (more than 10^4).
CIOption	(default "CI2") When the outer bounds method is being used, specifies which confidence interval should be used. If "CI2", the CI2 confidence interval is being used (see DDL, section 4.2), otherwise the CI3 confidence interval will be used (see DDL, appendix C). We recommend using CI3 only if the user suspects the FE logit model may be a severely misspecified model for the data.
alpha	(default 0.05) desired asymptotic level of the estimated confidence intervals
nbCores	(default 4) number of cores to be used for parallel computing, to speed up the estimation of the sharp bounds.

Value

a list containing for each entry in compute_T the output of compute_ATE_T specifying the relevant TEstim. If compute_T = "all", we use compute_T = 1:Tmax and also add an extra entry for the average AME over all periods. We refer to the documentation of the compute_ATE_t and compute_average_ATE functions for a detailed description of the output of each function.

cgaillac/MarginalFElogit documentation built on Dec. 24, 2024, 3:23 p.m.