pyblp.BootstrappedResults¶

class
pyblp.
BootstrappedResults
¶ Bootstrapped results of a solved problem.
This class has same methods as
ProblemResults
that compute postestimation outputs in one or more markets, but not other methods likeProblemResults.compute_optimal_instruments()
that do not make sense in a bootstrapped dataset. The only other difference is that methods return arrays with an extra first dimension along which bootstrapped results are stacked (these stacked results can be used to construct, for example, confidence intervals for postestimation outputs). Similarly, arrays of data (except for firm IDs and ownership matrices) passed as arguments to methods should have an extra first dimension of sizeBootstrappedResults.draws
.
problem_results
¶ ProblemResults
that was used to compute these bootstrapped results. Type
ProblemResults

bootstrapped_sigma
¶ Bootstrapped Cholesky decomposition of the covariance matrix for unobserved taste heterogeneity, \(\Sigma\).
 Type
ndarray

bootstrapped_pi
¶ Bootstrapped parameters that measures how agent tastes vary with demographics, \(\Pi\).
 Type
ndarray

bootstrapped_rho
¶ Bootstrapped parameters that measure within nesting group correlations, \(\rho\).
 Type
ndarray

bootstrapped_beta
¶ Bootstrapped demandside linear parameters, \(\beta\).
 Type
ndarray

bootstrapped_gamma
¶ Bootstrapped supplyside linear parameters, \(\gamma\).
 Type
ndarray

bootstrapped_prices
¶ Bootstrapped prices, \(p\). If a supply side was not estimated, these are unchanged prices. Otherwise, they are equilibrium prices implied by each draw.
 Type
ndarray
Bootstrapped marketshares, \(s\), implied by each draw.
 Type
ndarray

bootstrapped_delta
¶ Bootstrapped mean utility, \(\delta\), implied by each draw.
 Type
ndarray

computation_time
¶ Number of seconds it took to compute the bootstrapped results.
 Type
float

draws
¶ Number of bootstrap draws.
 Type
int

fp_converged
¶ Flags for convergence of the iteration routine used to compute equilibrium prices in each market. Rows are in the same order as
Problem.unique_market_ids
and column indices correspond to draws. Type
ndarray

fp_iterations
¶ Number of major iterations completed by the iteration routine used to compute equilibrium prices in each market for each draw. Rows are in the same order as
Problem.unique_market_ids
and column indices correspond to draws. Type
ndarray

contraction_evaluations
¶ Number of times the contraction used to compute equilibrium prices was evaluated in each market for each draw. Rows are in the same order as
Problem.unique_market_ids
and column indices correspond to draws. Type
ndarray
Examples
