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Component versus tradicional models to forecast quarterly national account aggregates: a Monte Carlo experiment

机译:组件与传统模型预测季度国民账户总量:蒙特卡洛实验

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摘要

Econometric models applied to observed data, specified and estimated using traditional Box-Jenkins techniques, have been widely used to forecast Quarterly National Accountud(QNA) aggregates. We assess the extent to which an alternative forecasting procedure, based on component models, improves the forecasting accuracy of traditional methods. Component models distinguish between the stochastic processes underlying the low- and the high-frequency component of time series, while traditional methods do not. Relationships between QNA aggregates and their coincident indicators are often significantly differentudfor diverse frequencies, as suggested by even an informal examination of empirical evidence. Under these circumstances, a Monte Carlo out-of-sample experiment reveals that component models improve the forecasting accuracy of traditional methods to predict QNA aggregatesudwhen their coincident indicators play an important role in such predictions. Otherwise, specially when dealing with pure univariate specifications, traditional procedures likely beat component methods. We illustrate these findings with several applications for the Spanish economy.
机译:使用传统Box-Jenkins技术指定和估计的用于观测数据的计量经济学模型已被广泛用于预测季度国民账户(QNA)总量。我们评估基于组件模型的替代预测程序在多大程度上提高了传统方法的预测准确性。成分模型可以区分时间序列的低频和高频成分的随机过程,而传统方法则不能。对于QNA集合体及其一致指标之间的关系,由于频率的不同,通常也存在很大差异,甚至通过对经验证据的非正式检查也可以看出。在这种情况下,蒙特卡洛(Monte Carlo)的样本外实验表明,组件模型提高了传统方法预测QNA聚合的预测准确性,因为它们的同时指标在此类预测中起着重要作用。否则,特别是在处理纯单变量规范时,传统过程可能会击败组件方法。我们通过对西班牙经济的一些应用来说明这些发现。

著录项

  • 作者

    Marrero Gustavo A.;

  • 作者单位
  • 年度 2004
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  • 原文格式 PDF
  • 正文语种 en
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