Sequential Estimation of Structural Models with a Fixed Point Constraint

By Hiroyuki Kasahara, Katsumi Shimotsu. Social Science Research Network, 2008

101 citations recorded by the source corpus.

This paper considers the estimation problem of structural models for which empirical restrictions are characterized by a fixed point constraint, such as structural dynamic discrete choice models or models of dynamic games. We analyze the conditions under which the nested pseudo-likelihood (NPL) algorithm achieves convergence and derive its convergence rate. We find that the NPL algorithm may not necessarily converge when the fixed point mapping does not have a local contraction property. To address the issue of non-convergence, we propose alternative sequential estimation procedures that can achieve convergence even when the NPL algorithm does not. Upon convergence, some of our proposed estimation algorithms produce more efficient estimators than the NPL estimator.

Subfields: Economics and Econometrics, Marketing

View Paper: https://www.semanticscholar.org/paper/e1660f2c628f1a9505f2e15b76eab2273a2f4a7e

Citations in the corpus

• A Dynamic Oligopoly Game of the US Airline Industry: Estimation and Policy Experiments (2012, unknown): “This estimator has lower asymptotic variance and fi nite sample bias than the two step estimator (see Aguirregabiria and Mira, 2007, and Kasahara and Shimotsu, 2008).”