# pyblp.ProblemResults.compute_elasticities¶

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

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

In market $$t$$, the value in row $$j$$ and column $$k$$ of $$\varepsilon$$ is

(1)$\varepsilon_{jk} = \frac{x_{kt}}{s_{jt}}\frac{\partial s_{jt}}{\partial x_{kt}}.$
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 elasticities. By default, elasticities are computed in all markets and stacked.

Returns

Estimated $$J_t \times J_t$$ matrices of elasticities of demand, $$\varepsilon$$. 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