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A Quasi-differencing Approach to Dynamic Modelling from a Time Series of Independent Cross-Sections.

机译:从独立截面的时间序列进行动态建模的拟微分方法。

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摘要

We motivate and describe a GMM method of estimating linear dynamic models from a time series of independent cross-sections. This involves subjecting the model to a quasi-differencing transformation across pairs of individuals that belong to the same group. Desirable features of the model include the fact that: (i) no aggregation is involved, (ii) dynamic response parameters can vary across groups, (iii) the presence of unobserved individual specific heterogeneity is explicitly allowed for, and (iv) the GMM estimators are derived by realistically assuming group size asymptotics. The Monte Carlo experiments conducted to assess the finite sample performances of the proposed estimators provide us with encouragement.
机译:我们激励并描述了一种GMM方法,用于从独立横截面的时间序列估计线性动力学模型。这涉及使模型在属于同一组的成对个体之间进行准微分转换。该模型的理想功能包括以下事实:(i)不涉及任何聚集;(ii)动态响应参数可以在各个组之间变化;(iii)明确允许存在未观察到的个体特定异质性;以及(iv)GMM估计量是通过实际假设组大小渐近而得出的。进行蒙特卡洛实验以评估拟议估计量的有限样本性能为我们提供了鼓励。

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