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 original Problem with the importance sampling agent data.

problem_results¶

ProblemResults that was used to compute these importance sampling results.

sampled_agents¶

Importance sampling agent data structured as Agents. The data_to_dict() function can be used to convert this into a more usable data type.

computation_time¶

Number of seconds it took to do importance sampling.

draws¶

Number of importance sampling draws in each market.

diagnostic_market_ids¶

Market IDs the correspond to the ordering of the following arrays of weight diagnostics.

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.

effective_draws¶

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

effective_draws_for_variance¶

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

effective_draws_for_skewness¶

Effective sample sizes for gauging skewness in each market: \(\frac{(\sum_i w_{it}^2)^3}{(\sum_i w_{it}^3)^2}\).

Examples

Methods

`to_dict`([attributes])	Convert these results into a dictionary that maps attribute names to values.
`to_pickle`(path)	Save these results as a pickle file.
`to_problem`()	Re-create the problem with the agent data constructed from importance sampling.