Improving the Performance of Random Coefficients Demand Models: The Role of Optimal Instruments

By Mathias Reynaert, Frank Verboven. 2014

100 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Marketing

View Paper: https://www.semanticscholar.org/paper/bdb71c74ab841cad7d5c252f022e3dd13c7672eb

Citations in the corpus

• Subsidies and Time Discounting in New Technology Adoption: Evidence from Solar Photovoltaic Systems (2019, unknown): “However, to improve e¢ ciency, in a second stage we use an approximation to optimal instruments (Chamberlain 1987), as applied in static aggregate discrete choice models by Berry, Levinsohn and Pakes (1999) and Reynaert & Verboven (2014).”
• Does Merger Simulation Work? Evidence from the Swedish Analgesics Market (2016, unknown): “…each variable with a random coe¢ cient), and optimal instruments in a second stage following Chamberlain (1987) and Berry, Levinsohn and Pakes (1999); Reynaert and Verboven (2014) provide detailed Monte Carlo evidence to demonstrate that optimal instruments improve the e¢ ciency of the estimator).”
• Best practices for differentiated products demand estimation with PyBLP (2020, unknown): “Reynaert and Verboven (2014) appear to construct their version of optimal instruments by summing across the rows of (28) and excluding either the first or third row.43 This gives them K = K1 +K2 +K3 instruments and K unknowns so that the model is just identified. However, because they stack (ξ, ω) t”