nested_std_dev_from_params: Helper function - wraps single execution of the...

In badp: Bayesian Averaging for Dynamic Panels

Helper function - wraps single execution of the log-likelihood & deviation parameters calculations. Used for nested version of SEM likelihood.

Description

Helper function - wraps single execution of the log-likelihood & deviation parameters calculations. Used for nested version of SEM likelihood.

Usage

nested_std_dev_from_params(
  params,
  data,
  df,
  timestamp_col,
  entity_col,
  dep_var_col,
  n_entities,
  n_periods
)

Arguments

params	A matrix (with named rows) with each column corresponding to a model. Each row specifies model parameters. Compare with optim_model_space_params
data	List of the SEM setup matrices, shared along different models
df	Data frame with data for the SEM analysis.
timestamp_col	The name of the column with timestamps
entity_col	Column with entities (e.g. countries)
dep_var_col	Column with the dependent variable
n_entities	Number of entities - passed to save calc. time
n_periods	Number of periods - passed to save calc. time

Value

Matrix with columns describing likelihood and standard deviations for each model. The first row is the likelihood for the model (computed using the parameters in the provided model space). The second row is the marginal likelihood approximation used to weight the models, namely exp((loglik - (k/2) * log(N * T)) / N); note that it is not a BIC. Then there are rows with standard deviations for each parameter, followed by rows with robust standard deviations. The last three rows hold tr(H^-1 J), where H is the observed information and J the outer product of the entity-level scores, the dimension of the parameter vector, and the numerical rank of J. These are the ingredients of the magnitude adjustment for misspecified likelihoods, whose rate is rank(J) / tr(H^-1 J); see score_rank for why J is typically rank deficient, and bma for why no weighting option is built on it.

badp documentation built on Aug. 20, 2026, 9:08 a.m.