bayesian_blp#
Bayesian BLP (Berry-Levinsohn-Pakes) model on aggregate market-share panels.
Fits the random-coefficients aggregate-share demand model of Berry, Levinsohn
& Pakes (1995) in a fully Bayesian formulation that follows Jiang, Manchanda
& Rossi (2009, QME) and Yang, Chen & Allenby (2003): the BLP contraction
mapping and GMM are dropped in favour of a joint posterior over preference
parameters and the latent product-market shocks ξ_jt. This makes
hierarchical pooling across markets / regions cheap, returns full posterior
elasticities, and stays honest under weak instruments.
Use this when the data is aggregate market shares across products and
markets (the common Nielsen / IRI / retailer-scanner data shape) and you
need cross-price substitution patterns that come from a structural
preference model rather than a reduced-form share allocation. For
individual-level discrete choice, use
pymc_marketing.customer_choice.MixedLogit instead. For
“what happened when X launched” style impact analyses on aggregate shares,
use pymc_marketing.customer_choice.MVITS.
Classes
|
Bayesian random-coefficients logit on aggregate market-share panels. |