Nearly Efficient Estimation of Time Series Models with Predetermined, but not Exogenous, Instruments
针对理性预期假设下误差序列相关且工具变量非严格外生的情况，提出一种变换方法消除序列相关，使标准工具变量估计适用，并证明其渐近性质与最优估计器接近。
Particularly under the assumption of rational expectations, a model may have serially correlated errors and those errors may be uncorrelated with contemporaneous and lagged values of a predetermined instrument, yet the instruments may not be strictly exogenous. This paper proposes a method for transforming such a model to one without serial correlation, while keeping the instrument predetermined. Standard theory of instrumental variables estimation then applies. Furthermore, it turns out that for transformations of the class proposed, asymptotic distribution theory is the same whether the serial correlation properties of the errors are known a priori or estimated. As the number of lagged values of the predetermined variables used as instruments increases, the asymptotic variance of the standard instrumental variables estimator applied to the transformed model approaches that of the optimal estimator proposed by Hansen and Sargent [8]. IN A NUMBER of recently developed macroeconomic models behavioral equations arise in which error terms can be asserted on the basis of economic arguments to be uncorrelated with some set of instrumental variables at a certain set of dates, but not to be uncorrelated with the instruments at all dates. Examples of such