# pyblp.ProblemResults.compute_aggregate_elasticities¶

ProblemResults.compute_aggregate_elasticities(factor=0.1, name='prices')

Estimate aggregate elasticities of demand, $$\mathscr{E}$$, with respect to a variable, $$x$$.

In market $$t$$, the aggregate elasticity of demand is

(1)$\mathscr{E} = \sum_{j=1}^{J_t} \frac{s_{jt}(x + \Delta x) - s_{jt}}{\Delta},$

in which $$\Delta$$ is a scalar factor and $$s_{jt}(x + \Delta x)$$ is the share of product $$j$$ in market $$t$$, evaluated at the scaled values of the variable.

Parameters
• factor (float, optional) – The scalar factor, $$\Delta$$.

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

Returns

Estimates of aggregate elasticities of demand, $$\mathscr{E}$$, for all markets. Rows are in the same order as Problem.unique_market_ids.

Return type

ndarray

Examples