Sequential Estimation of Structural Models with a Fixed Point Constraint

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Sequential Estimation of Structural Models with a Fixed Point Constraint

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Hiroyuki Kasahara ()
Katsumi Shimotsu ()

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Abstract

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.

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Publisher Info
Paper provided by CESifo Group Munich in its series CESifo Working Paper Series with number CESifo Working Paper No. 2507.

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Date of creation: 2008
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Handle: RePEc:ces:ceswps:_2507

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Paper
- Hiroyuki Kasahara & Katsumi Shimotsu, 2008. "Sequential Estimation of Structural Models with a Fixed Point Constraint," Working Papers 1192, Queen's University, Department of Economics. [Downloadable!]

Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques

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Other versions:
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Other versions:
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This page was last updated on 2009-11-3.

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