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首页> 外文会议>Predictive econometrics and big data >Does Forecasting Benefit from Mixed-Frequency Data Sampling Model: The Evidence from Forecasting GDP Growth Using Financial Factor in Thailand
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Does Forecasting Benefit from Mixed-Frequency Data Sampling Model: The Evidence from Forecasting GDP Growth Using Financial Factor in Thailand

机译：预测是否会受益于混合频率数据采样模型：泰国使用金融因素预测GDP增长的证据

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摘要

It is common for macroeconomic data to be observed at different frequencies. This gives a challenge to analysts when forecasting with multivariate model is concerned. The mixed-frequency data sampling (MIDAS) model has been developed to deal with such problem. However, there are several MIDAS model specifications and they can affect forecasting outcomes. Thus, we investigate the forecasting performance of MIDAS model under different specifications. Using financial variable to forecast quarterly GDP growth in Thailand, our results suggest that U-MIDAS model significantly outperforms the traditional time-aggregate model and MIDAS models with weighting schemes. Additionally, MIDAS model with Beta weighting scheme exhibits greater forecasting precision than the time-aggregate model. This implies that MIDAS model may not be able to surpass the traditional time-aggregate model if inappropriate weighting scheme is used.
机译：通常以不同的频率观察宏观经济数据。当涉及到多元模型的预测时，这给分析师带来了挑战。为了解决这种问题，已经开发了混合频率数据采样（MIDAS）模型。但是，有几种MIDAS模型规范，它们可能会影响预测结果。因此，我们研究了不同规格下的MIDAS模型的预测性能。使用财务变量预测泰国的季度GDP增长，我们的结果表明，U-MIDAS模型明显优于传统的时间汇总模型和带有加权方案的MIDAS模型。此外，具有Beta权重方案的MIDAS模型比时间汇总模型具有更高的预测精度。这意味着，如果使用了不适当的加权方案，则MIDAS模型可能无法超越传统的时间汇总模型。

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  • 来源
  • 会议地点 Chiang Mai(TH)
  • 作者

    Natthaphat Kingnetr; Tanaporn Tungtrakul; Songsak Sriboonchitta;

  • 作者单位

    Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand;

    Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand;

    Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand,Center of Excellence in Econometrics, Chiang Mai University,Chiang Mai 50200, Thailand;

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