ADAPTIVE ESTIMATION OF ERROR CORRECTION MODELS
研究了在未知创新序列密度函数形状（仅需对称）的情况下，如何通过非参数核密度估计实现降秩向量误差修正模型的渐近有效估计，并推导了估计量的渐近分布。
This paper considers adaptive maximum likelihood estimation of reduced rank vector error correction models. It is shown that such models can be asymptotically efficiently estimated even in the absence of knowledge of the shape of the density function of the innovation sequence, provided that this density is symmetric. The construction of the estimator, involving the nonparametric kernel estimation of the unknown density using the residuals of a consistent preliminary estimator, is described, and its asymptotic distribution is derived. Asymptotic efficiency gains over the Gaussian pseudo–maximum likelihood estimator are evaluated for elliptically symmetric innovations.