A likelihood-Based Approximate Solution to the Incidental Parameter Problem in Dynamic Nonlinear Models with Multiple Effects
A likelihood-Based Approximate Solution to the Incidental Parameter Problem in Dynamic Nonlinear Models with Multiple Effects
复制标题
多重效应动态非线性模型中附带参数问题的基于似然的近似解
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
J. Hahn
中科院分区：
文献类型：
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作者：
Manuel Arellano;J. Hahn
摘要讨论了一种改进的目标函数策略，在多重效应的非线性动态面板模型中获得了不偏于1/T阶的估计。估计是从相对于某个目标不可行准则的偏差校正的目标函数开始的。我们考虑一种基于行列式的似然设置方法，以及一种不限于似然设置的基于迹的方法。这两种方法都完全依赖于黑森和固定效应分数的外积。即使在具有多种效果的模型中，它们也能产生简单而透明的校正。我们分析了当n和T以相同的速度增长时，这两种估计量的渐近性质，并证明了它们是渐近正态的，并且集中在真值上。我们的策略是发展一个一般的偏差校正估计方程的理论，这样我们就可以利用一阶条件获得特定偏差校正方法的渐近结果。
Abstract We discuss a modified objective function strategy to obtain estimators without bias to order 1/T in nonlinear dynamic panel models with multiple effects. Estimation proceeds from a bias-corrected objective function relative to some target infeasible criterion. We consider a determinant-based approach for likelihood settings, and a trace-based approach, which is not restricted to the likelihood setup. Both approaches depend exclusively on the Hessian and the outer product of the scores of the fixed effects. They produce simple and transparent corrections even in models with multiple effects. We analyze the asymptotic properties of both types of estimators when n and T grow at the same rate, and show that they are asymptotically normal and centered at the truth. Our strategy is to develop a theory for general bias-corrected estimating equations, so that we can obtain asymptotic results for a specific bias correction method using the first-order conditions.