pyblp.ProblemResults.extract_diagonal_means

ProblemResults.extract_diagonal_means(matrices)

Extract means of diagonals from stacked \(J_t \times J_t\) matrices for each market \(t\).

Parameters

matrices (array-like) – Stacked matrices, such as estimates of \(\varepsilon\), computed by ProblemResults.compute_elasticities(); \(\mathscr{D}\), computed by ProblemResults.compute_diversion_ratios(); \(\bar{\mathscr{D}}\), computed by ProblemResults.compute_long_run_diversion_ratios(); or \(s_{jti}\) computed by ProblemResults.compute_probabilities().

Returns

Stacked diagonal means. If the matrices are estimates of \(\varepsilon\), the mean of a diagonal is a market’s mean own elasticity of demand; if they are estimates of \(\mathscr{D}\) or \(\bar{\mathscr{D}}\), the mean of a diagonal is a market’s mean diversion ratio to the outside good. Rows are in the same order as Problem.unique_market_ids.

Return type

ndarray

Examples