A Stochastic Nested Fixed Point Algorithm for Large-Scale BLP Estimation

2026-09-20

AI Summary

本文提出了一种用于大规模BLP估计的随机嵌套固定点算法，通过减少内存需求和计算成本，在非常大的数据集上实现可行的估计。

Abstract

We develop a stochastic nested fixed point (SNFP) estimator for random coefficients logit demand models that updates model parameters using stochastic gradients and performs demand inversion one market at a time. Relative to the conventional nested fixed point (NFP) estimator, SNFP substantially reduces memory requirements and computational cost, making estimation feasible in very large datasets. We establish the large-$T$ (number of markets) asymptotic properties of the estimator under regularity conditions. We also characterize the effect of sharing one block of simulation draws across markets and show how to correct for it. Monte Carlo simulations show that the SNFP estimator achieves statistical accuracy comparable to the NFP estimator, and in our benchmark a single online pass estimates a model with 100 million markets in about 5.5 hours. An empirical application using scanner data further demonstrates the practical advantages of SNFP for large-scale demand estimation.

Problem

• stochastic nested fixed point
• large-scale BLP estimation
• random coefficients logit demand models

Innovation

• Stochastic Nested Fixed Point (SNFP)
• Random Coefficients Logit Demand Models
• Stochastic Gradients
• Demand Inversion
• Large-Scale Datasets