pyblp.ProblemResults.compute_profits

ProblemResults.compute_profits(prices=None, shares=None, costs=None, market_id=None)

Estimate population-normalized gross expected profits, \(\pi\).

With constant costs, the profit from product \(j\) in market \(t\) is

(1)\[\pi_{jt} = (p_{jt} - c_{jt})s_{jt}.\]
Parameters

  • prices (array-like, optional) – Prices, \(p\), such as equilibrium prices, \(p^*\), computed by ProblemResults.compute_prices(). By default, unchanged prices are used.

  • shares (array-like, optional) – Shares, \(s\), such as those computed by ProblemResults.compute_shares(). By default, unchanged shares are used.

  • costs (array-like) – Marginal costs, \(c\). By default, marginal costs are computed with ProblemResults.compute_costs(). Costs under a changed ownership structure can be computed by specifying the firm_ids or ownership arguments of ProblemResults.compute_costs().

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

Returns

Estimated population-normalized gross expected profits, \(\pi\).

Return type

ndarray

Examples