pyblp.ImportanceSamplingResults¶

class
pyblp.
ImportanceSamplingResults
¶ Results of importance sampling.
Along with the sampled agents, these results also contain a number of useful importance sampling diagnostics from Owen (2013).
The
ImportanceSamplingResults.to_problem()
method can be used to update the originalProblem
with the importance sampling agent data.
problem_results
¶ ProblemResults
that was used to compute these importance sampling results. Type
ProblemResults

sampled_agents
¶ Importance sampling agent data structured as
Agents
. Thedata_to_dict()
function can be used to convert this into a more usable data type. Type
Agents

computation_time
¶ Number of seconds it took to do importance sampling.
 Type
float

draws
¶ Number of importance sampling draws in each market.
 Type
int

diagnostic_market_ids
¶ Market IDs the correspond to the ordering of the following arrays of weight diagnostics.
 Type
ndarray

weight_sums
¶ Sum of weights in each market: \(\sum_i w_{it}\). If importance sampling was successful, weights should not sum to numbers too far from one.
 Type
ndarray

effective_draws
¶ Effective sample sizes in each market: \(\frac{(\sum_i w_{it})^2}{\sum_i w_{it}^2}\).
 Type
ndarray

effective_draws_for_variance
¶ Effective sample sizes for variance estimates in each market: \(\frac{(\sum_i w_{it}^2)^2}{\sum_i w_{it}^4}\).
 Type
ndarray

effective_draws_for_skewness
¶ Effective sample sizes for gauging skewness in each market: \(\frac{(\sum_i w_{it}^2)^3}{(\sum_i w_{it}^3)^2}\).
 Type
ndarray
Examples
Methods
to_dict
([attributes])Convert these results into a dictionary that maps attribute names to values.
Recreate the problem with the agent data constructed from importance sampling.
