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Consistency of modified LS estimation method for identifying 2-Dnoncausal SAR model parameters

机译:修正的LS估计方法识别2-非因果SAR模型参数的一致性

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摘要

Least squares (LS) and maximum likelihood (ML) are the two main methods for parameter estimation of two-dimensional (2D) noncausal simultaneous autoregressive (SAR) models. ML is asymptotically consistent and unbiased but computationally unattractive. On the other hand, conventional LS is computationally efficient but does not produce accurate parameter estimates for noncausal models. Recently, Zhao-Yu (1993) proposed a modified LS estimation method and was shown to be unbiased. In this paper we prove that, under certain assumptions, the method introduced by Zhao-Yu is also consistent
机译:最小二乘(LS)和最大似然(ML)是二维(2D)非因果同时自回归(SAR)模型参数估计的两种主要方法。 ML渐近一致且无偏见,但在计算上没有吸引力。另一方面,常规的LS在计算上是有效的,但不会为非因果模型产生准确的参数估计。最近,Zhao-Yu(1993)提出了一种改进的LS估计方法,并被证明是无偏的。本文证明,在一定的假设下,赵宇介绍的方法也是一致的。

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