pyblp.ProblemResults.compute_demand_hessians

ProblemResults.compute_demand_hessians(name='prices', market_id=None)

Estimate arrays of second derivatives of demand with respect to a variable, \(x\).

In market \(t\), the value indexed by \((j, k, \ell)\) is

(1)\[\frac{\partial^2 s_{jt}}{\partial x_{kt} \partial x_{\ell t}}.\]
Parameters

  • name (str, optional) – Name of the variable, \(x\). By default, \(x = p\), prices.

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

Returns

Estimated \(J_t \times J_t \times J_t\) arrays of second derivatives of demand. If market_id was not specified, arrays are estimated in each market \(t\) and stacked. Indices for a market are in the same order as products for the market. If a market has fewer products than others, extra indices will contain numpy.nan.

Return type

ndarray

Examples