# pyblp.ProblemResults.compute_elasticities¶

ProblemResults.compute_elasticities(name='prices')

Estimate matrices of elasticities of demand, $$\varepsilon$$, with respect to a variable, $$x$$.

For each market, the value in row $$j$$ and column $$k$$ of $$\varepsilon$$ is

(1)$\varepsilon_{jk} = \frac{x_k}{s_j}\frac{\partial s_j}{\partial x_k}.$
Parameters

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

Returns

Stacked $$J_t \times J_t$$ estimated matrices of elasticities of demand, $$\varepsilon$$, for each market $$t$$. 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