A SIMPLE PROCEDURE FOR COMPUTING IMPROVED PREDICTION INTERVALS FOR AUTOREGRESSIVE MODELS

Paolo Vidoni

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A SIMPLE PROCEDURE FOR COMPUTING IMPROVED PREDICTION INTERVALS FOR AUTOREGRESSIVE MODELS

机译：计算自回归模型的改进预测间隔的简单过程

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This article concerns the construction of prediction intervals for time series models. The estimative or plug-in solution is usually not entirely adequate, since the (conditional) coverage probability may differ substantially from the nominal value. Prediction intervals with improved (conditional) coverage probability can be defined by adjusting the estimative ones, using rather complicated asymptotic procedures or suitable simulation techniques. This article extends to Markov process models a recent result by Vidoni. which defines a relatively simple predictive distribution function, giving improved prediction limits as quantiles. This new solution is fruitfully considered in the challenging context of prediction for time-series models, with particular regard to AR and ARCH processes.

机译：本文涉及时间序列模型的预测间隔的构造。由于（有条件的）覆盖概率可能与标称值有很大差异，因此估计或插件解决方案通常并不完全合适。可以使用相当复杂的渐近过程或合适的模拟技术，通过调整估计值来定义具有改善的（条件）覆盖率的预测间隔。本文将Vidoni的最新结果扩展到Markov过程模型。它定义了一个相对简单的预测分布函数，从而提高了分位数的预测极限。在具有挑战性的时序模型预测环境中，尤其是在AR和ARCH流程方面，已充分考虑了这一新解决方案。

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来源
《Journal of Time Series Analysis》 |2009年第6期|577-590|共14页
作者
Paolo Vidoni;
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作者单位

University of Inline;

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原文格式 PDF
正文语种 eng
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关键词
ARCH model; AR model; conditional coverage probability; prediction limit; time-series;

机译：ARCH模型;AR模型;有条件的覆盖概率;预测极限时间序列;

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A SIMPLE PROCEDURE FOR COMPUTING IMPROVED PREDICTION INTERVALS FOR AUTOREGRESSIVE MODELS

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