pyblp.ProblemResults.simulate_micro_data

ProblemResults.simulate_micro_data(dataset, seed=None)

Simulate observations \(n \in N_d\) from a micro dataset \(d\).

Each micro observation \(n\) underlying the dataset \(d\) is simulated according to agent weights \(w_{it}\), choice probabilities \(s_{ijt}\), and survey weights \(w_{dijt}\).

Parameters

  • dataset (MicroDataset) – The MicroDataset for which micro data will be simulated.

  • seed (int, optional) – Passed to numpy.random.RandomState to seed the random number generator before data are simulated. By default, a seed is not passed to the random number generator.

Returns

Micro data with as many rows as observations passed to the dataset. Fields:

  • micro_ids : (object) - IDs corresponding to observations \(n\).

  • market_ids : (object) - Market IDs \(t_n\) for each observation \(n\).

  • agent_indices : (int) - Within-market indices of agents \(i_n\) that take on values from \(0\) to \(I_t - 1\).

  • choice_indices : (int) - Within-market indices of simulated choices \(j_n\). If compute_weights passed to the dataset returns an array with \(J_t\) elements in its second axis, then choice indices take on values from \(0\) to \(J_t - 1\) where \(0\) corresponds to the first inside good. If it returns an array with \(1 + J_t\) elements in its second axis, then choice indices take on values from \(0\) to \(J_t\) where \(0\) corresponds to the outside good.

If the dataset is configured to support second choice data, second choices will also be simulated:

  • second_choice_indices : (int) - Within-market indices of simulated second choices \(k_n\). If compute_weights passed to the dataset returns an array with \(J_t\) elements in its third axis, then second choice indices take on values from \(0\) to \(J_t - 1\) where \(0\) corresponds to the first inside good. If it returns an array with \(1 + J_t\) elements in its third axis, then second choice indices take on values from \(0\) to \(J_t\) where \(0\) corresponds to the outside good.

Integration nodes and demographics can be merged in on the market_ids and agent_indices fields. Product characteristics can be merged in on the market_ids and choice_indices fields. Product characteristics of any second choices can be merged in on the market_ids and second_choice_indices fields.

Return type

recarray

Examples