pyblp.ProblemResults.compute_prices¶

ProblemResults.
compute_prices
(firm_ids=None, ownership=None, costs=None, prices=None, iteration=None)¶ Estimate equilibrium prices after firm or cost changes, \(p^*\).
Prices are computed in each market by iterating over the \(\zeta\)markup contraction in (42):
(1)¶\[p^* \leftarrow c^* + \zeta^*(p^*),\]in which the markup term from (39) is
(2)¶\[\zeta^*(p^*) = \Lambda^{1}(p^*)[O^* \odot \Gamma(p^*)]'(p^*  c^*)  \Lambda^{1}(p^*)\]where \(O^*\) is the ownership matrix associated with firm changes.
 Parameters
firm_ids (arraylike, optional) – Potentially changed firm IDs. By default, the unchanged
firm_ids
field ofproduct_data
inProblem
will be used.ownership (arraylike, optional) – Potentially changed ownership matrices. By default, standard ownership matrices based on
firm_ids
will be used unless theownership
field ofproduct_data
inProblem
was specified.costs (arraylike) – Potentially changed marginal costs, \(c^*\). By default, unchanged marginal costs are computed with
ProblemResults.compute_costs()
.prices (arraylike, optional) – Prices at which the fixed point iteration routine will start. By default, unchanged prices, \(p\), are used as starting values. Other reasonable starting prices include the approximate equilibrium prices computed by
ProblemResults.compute_approximate_prices()
.iteration (Iteration, optional) –
Iteration
configuration for how to solve the fixed point problem in each market. By default,Iteration('simple', {'atol': 1e12})
is used. Analytic Jacobians are not supported for solving this system.
 Returns
Estimates of equilibrium prices after any firm or cost changes, \(p^*\).
 Return type
ndarray
Examples