# pyblp.ProblemResults.compute_long_run_diversion_ratios¶

ProblemResults.compute_long_run_diversion_ratios()

Estimate matrices of long-run diversion ratios, $$\bar{\mathscr{D}}$$.

Long-run diversion ratios to the outside good are reported on diagonals. For each market, the value in row $$j$$ and column $$k$$ is

(1)$\bar{\mathscr{D}}_{jk} = \frac{s_{k(-j)} - s_k}{s_j},$

in which $$s_{k(-j)}$$ is the share of product $$k$$ computed with the outside option removed from the choice set if $$j = k$$, and with product $$j$$ removed otherwise.

Returns

Stacked $$J_t \times J_t$$ estimated matrices of long-run diversion ratios, $$\bar{\mathscr{D}}$$, for all markets. 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