一阶自回归的测量误差

Q2 Decision Sciences

Advances in Decision Sciences Pub Date : 2020-06-01 DOI:10.47654/v24y2020i2p1-14

P. Franses

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引用次数: 1

摘要

一阶自回归模型中斜率参数的普通最小二乘（OLS）估计器在测量变量时存在误差。这种错误可能发生在宏观经济数据的修订中。本文阐述并提出了一种基于总最小二乘法（TLS）的简单程序来减轻偏差。TLS通常是一致的，在小样本中也能很好地工作。仿真实验和实例验证了该方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Measurement Error in a First-order Autoregression

The Ordinary Least Squares (OLS) estimator for the slope parameter in a first-order autoregressive model is biased when the variable is measured with error. Such an error may occur with revisions of macroeconomic data. This paper illustrates and proposes a simple procedure to alleviate the bias, and is based on Total Least Squares (TLS). TLS is, in general, consistent, and also works well in small samples. Simulation experiments and an empirical example show the usefulness of this method.

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来源期刊

Advances in Decision Sciences Mathematics-Applied Mathematics

CiteScore

4.70

自引率

0.00%

发文量

审稿时长

29 weeks