Computing The Mean Square Error Of Unobserved Components Extracted By Misspecified Time Series Models

Andrew C. Harvey; Davide Delle Monache

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Computing The Mean Square Error Of Unobserved Components Extracted By Misspecified Time Series Models

机译：计算未指定时间序列模型提取的未观测分量的均方误差

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Algorithms are presented for computing mean square errors in a misspecified unobserved components model when the true model is known. It is assumed that both the true and misspecified models can be put in linear state space form. The algorithm for filtering is based on the Kalman filter while that for smoothing modifies the fixed-point smoother. Illustrations include the efficiency of the Hodrick-Prescott filter for annual flow data and the mean square error of predictions for misspecified models from the autoregressive integrated moving average class.

机译：当已知真实模型时，提出了用于在错误指定的未观察组件模型中计算均方误差的算法。假设正确和错误指定的模型都可以放入线性状态空间形式。滤波算法基于卡尔曼滤波器，而平滑算法则修改定点平滑器。插图包括Hodrick-Prescott过滤器对年度流量数据的效率，以及自回归综合移动平均类对误指定模型的预测的均方误差。

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来源
《Journal of Economic Dynamics and Control》 |2009年第2期|p.283-295|共13页
作者
Andrew C. Harvey; Davide Delle Monache;
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作者单位

Faculty of Economics, University of Cambridge, Sidgwick Avenue, Cambridge CB3 9DD, UK;

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原文格式 PDF
正文语种 eng
中图分类 f;
关键词
detrending; exponentially weighted moving average; hodrick-prescott filter; kalman filter; smoother;

机译：去趋势;指数加权移动平均;霍德里克-普雷斯科特滤波器;卡尔曼滤波器;平滑;

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7. Forecasting daily time series using periodic unobserved components time series models [O] . Ooms, M., Koopman, S.J. 2006

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Computing The Mean Square Error Of Unobserved Components Extracted By Misspecified Time Series Models

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