# pyblp.ProblemResults.compute_aggregate_elasticities¶

ProblemResults.compute_aggregate_elasticities(factor=0.1, name='prices', market_id=None)

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

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

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

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. If this is None, the variable will be $$x = \delta$$, the mean utility.

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

Returns

Estimates of aggregate elasticities of demand, $$\mathscr{E}$$. If market_id was not specified, rows are in the same order as Problem.unique_market_ids.

Return type

ndarray

Examples