状态空间估计:从卡尔曼滤波器回归最小二乘

IF 0.3 Q4 ECONOMICS
Miroslav Plašil
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引用次数: 0

摘要

本文回顾了状态空间模型的直接最小二乘估计,并强调了其在某些应用中优于标准卡尔曼滤波器的优点。虽然这两个概念之间有密切的关系,但主流计量经济学文献和应用研究人员似乎很少重视对估计问题的双重理解。由于计算和理论的进步,状态空间模型的最小二乘估计在许多领域已经成为一种可行的替代方法,在解决其他困难问题方面显示出巨大的潜力。本文收集并讨论了一些可能的应用,以说明这一点,并有助于它们在实践中得到更广泛的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
State Space Estimation: from Kalman Filter Back to Least Squares
This note reviews a direct least squares estimation of a state space model and highlights its advantages over the standard Kalman filter in some applications. Although there is a close relationship between these two concepts, dual understanding of the estimation problem seems to be little appreciated by the mainstream econometric literature as well as applied researchers. Due to computational and theoretical advancements, the least squares estimation of a state space model has become a viable alternative in many fields, showing great potential in solving otherwise difficult problems. This note gathers and discusses some possible applications to illustrate the point and contribute to their wider use in practice.
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来源期刊
CiteScore
0.60
自引率
0.00%
发文量
23
审稿时长
24 weeks
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