Bootstrap a model
Samples individuals with replacement from the database, and estimates the model in each sample.
apollo_bootstrap(
apollo_beta,
apollo_fixed,
apollo_probabilities,
apollo_inputs,
estimate_settings = list(estimationRoutine = "bfgs", maxIterations = 200, writeIter =
FALSE, hessianRoutine = "none", printLevel = 2L, silent = FALSE, maxLik_settings =
list()),
bootstrap_settings = list(nRep = 30, samples = NA, seed = 24, calledByEstimate =
FALSE, recycle = TRUE)
)apollo_beta |
Named numeric vector. Names and values for parameters. |
apollo_fixed |
Character vector. Names (as defined in |
apollo_probabilities |
Function. Returns probabilities of the model to be estimated. Must receive three arguments:
|
apollo_inputs |
List grouping most common inputs. Created by function apollo_validateInputs. |
estimate_settings |
List. Options controlling the estimation process. See apollo_estimate.
|
bootstrap_settings |
List. Options defining the sampling procedure. The following are valid options.
|
This function implements a basic block bootstrap. It estimates the model parameters on nRep number of different samples.
Each new sample is constructed by sampling with replacement from the original full sample. Each new sample has as many
individuals as the original sample, though some of them may be repeated. Sampling is done at the individual level,
therefore if different individuals have different number of observations, each re-sample could have different number of observations.
If the sampling wants to be done at the individual level (not recommended on panel data), then the optional
bootstrap_settings$samples argument should be provided.
For each sample, only the parameters and loglikelihood are estimated. Standard errors are not calculated (they may be in future versions). The composition of each re-sample is stored on a file, though it should be consistent across runs.
This function writes three different files to the working directory:
modelName_bootstrap_params.csv: Records the estimated parameters, final loglikelihood, and number of observations on each re-sample
modelName_bootstrap_samples.csv: Records the composition of each re-sample.
modelName_bootstrap_vcov.csv: Variance-covariance matrix of the estimated parameters across re-samples.
The first two files are updated throughout the run of this function, while the last one is only written once the function finishes.
When run, this function will look for the first two files above in the working directory. If they are found, the function will attempt to pick up re-sampling from where those files left off. This is useful in cases where the original bootstrapping was interrupted, or when additional re-sampling wants to be performed.
List with three elements.
estimates: Matrix containing the parameter estimates for each repetition. As many rows as repetitions and as many columns as parameters.
varcov: Covariance matrix of the estimated parameters across the repetitions.
LL: Vector of final loglikelihoods of each repetition.
This function also writes three output files to the working directory, with the following names ('x' represents the model name):
x_bootstrap_params.csv: Table containing the parameter estimates, loglikelihood, and number of observations for each repetition.
x_bootstrap_samples.csv: Table containing the description of the sample used in each repetition. Same format than bootstrap_settings$samples.
x_bootstrap_vcov: Table containing the covariance matrix of estimated parameters across the repetitions.
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