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