pyblp.ProblemResults.compute_passthrough

ProblemResults.compute_passthrough(firm_ids=None, ownership=None, market_id=None)

Estimate matrices of passthrough of marginal costs to equilibrium prices, \(\Upsilon\).

In market \(t\), the value in row \(j\) and column \(k\) of \(\Upsilon\) is

(1)\[\Upsilon_{jk} = \frac{\partial p_j}{\partial c_k}.\]
Parameters

  • firm_ids (array-like, optional) – Firm IDs. By default, the firm_ids field of product_data in Problem will be used.

  • ownership (array-like, optional) – Ownership matrices. By default, standard ownership matrices based on firm_ids will be used unless the ownership field of product_data in Problem was specified.

  • market_id (object, optional) – ID of the market in which to compute passthrough. By default, passthrough matrices are computed in all markets and stacked.

Returns

Estimated \(J_t \times J_t\) passthrough matrices, \(\Upsilon\). If market_id was not specified, matrices are estimated in each market \(t\) and stacked. Columns for a market are in the same order as products for the market. If a market has fewer products than others, extra columns will contain numpy.nan.

Return type

ndarray

Examples