Initial Conditions and Moment Restrictions in Dynamic Panel Data Models
Initial Conditions and Moment Restrictions in Dynamic Panel Data Models
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DOI：
10.1920/wp.ifs.1995.9517
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发表时间：
1998-11
期刊：
影响因子：
--
通讯作者：
R. Blundell;Stephen R. Bond
中科院分区：
文献类型：
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作者：
R. Blundell;Stephen R. Bond
本文考虑自回归误差分量模型的估计问题。当自回归参数是适度的大，时间序列的观测值的数量是适度的小，通常的广义矩量法（GMM）估计后得到的第一差分已被发现表现不佳。在这里，我们考虑替代线性估计，旨在改善标准的第一差分GMM估计的属性。我们考虑两种方法来估计。第一种方法通过添加观测的初始值作为额外的回归量来扩展模型。这使得一致的估计误差分量GLS获得。这个估计被证明是等价的最佳GMM估计正常的同方差误差分量模型。第二种方法考虑了一个温和的限制，在初始条件下，因变量的滞后差异可以用来构建线性矩条件的水平方程。然后，可以利用一个线性GMM估计系统中的一阶差分方程和水平方程的矩条件的完整集合，从而使非线性矩条件对于估计是冗余的。当附加的限制有效时，该估计比非线性GMM更有效。Monte Carlo模拟报告表明，所提出的估计性能的显着改善相比，通常的第一差分GMM估计，特别是对于高值的自回归参数。(This摘要是从本条目的另一个版本中借用的。）(This摘要是从本条目的另一个版本中借用的。）(This摘要是从本条目的另一个版本中借用的。）(This摘要是从本条目的另一个版本中借用的。）(This摘要是从本条目的另一个版本中借用的。）(This abst（本摘要是从本条目的另一个版本中借用的。）
In this paper we consider estimation of the autoregressive error components model. When the autoregressive parameter is moderately large and the number of time series observations is moderately small, the usual Generalised Methods of Moments (GMM) estimator obtained after first differencing has been found to be poorly behaved. Here we consider alternative linear estimators that are designed to improve the properties of the standard first-differenced GMM estimator. We consider two approaches to estimation. The first approach extends the model by adding the observed initial values as an extra regressor. This allows consistent estimates to be obtained by error-components GLS. This estimator is shown to be equivalent to the optimal GMM estimator for the normal homoskedastic error components model. The second approach considers a mild restriction on the initial condition process under which lagged differences in the dependent variable can be used to construct linear moment conditions in the levels equations. The complete set of moment conditions can then be exploited by a linear GMM estimator in a system of first-differenced and levels equations, rendering the non-linear moment conditions redundant for estimation. This estimator is strictly more efficient than non-linear GMM when the additional restriction is valid. Monte Carlo simulations are reported which demonstrate the dramatic improvement in performance of the proposed estimators compared to the usual first-differenced GMM estimator, especially for high values of the autoregressive parameter. (This abstract was borrowed from another version of this item.) (This abstract was borrowed from another version of this item.) (This abstract was borrowed from another version of this item.) (This abstract was borrowed from another version of this item.) (This abstract was borrowed from another version of this item.) (This abst (This abstract was borrowed from another version of this item.)