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Econometric models involving data sampled at different frequencies are of general interest. Mixed-data sampling (MIDAS) is an econometric regression developed by Eric Ghysels with several co-authors. There is now a substantial literature on MIDAS regressions and their applications, including Ghysels, Santa-Clara and Valkanov (2006), Ghysels, Sinko and Valkanov, Andreou, Ghysels and Kourtellos (2010) and Andreou, Ghysels and Kourtellos (2013).

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  • Econometric models involving data sampled at different frequencies are of general interest. Mixed-data sampling (MIDAS) is an econometric regression developed by Eric Ghysels with several co-authors. There is now a substantial literature on MIDAS regressions and their applications, including Ghysels, Santa-Clara and Valkanov (2006), Ghysels, Sinko and Valkanov, Andreou, Ghysels and Kourtellos (2010) and Andreou, Ghysels and Kourtellos (2013). (en)
  • 混合數據抽樣(Mixed-data sampling, MIDAS)是由 Ghysels 等人所發展的一種計量經濟迴歸或篩選法。簡單迴歸的迴歸變數(regressors)出現頻率高於迴歸值(regressand): 其中y是迴歸值,x是迴歸變數,m表示頻率。舉例來說,如果y是年度,就是季,是誤差項(disturbance)而是落後分配(lag distribution),如B函數或Almon Lag。 這個迴歸模型應用在混合頻率數據上有時可以代替卡爾曼濾波。Bai, Ghysels and Wright(2010)探討MIDAS迴歸與卡爾曼濾波狀態空間模型應用於混合頻率數據的關係。一般來說,後者是方程組,而MIDAS迴歸只有一條方程式。因此,MIDAS迴歸或許效率較差,但比較不容易出錯。以MIDAS迴歸作為近似值的誤差不大。 (zh)
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  • Econometric models involving data sampled at different frequencies are of general interest. Mixed-data sampling (MIDAS) is an econometric regression developed by Eric Ghysels with several co-authors. There is now a substantial literature on MIDAS regressions and their applications, including Ghysels, Santa-Clara and Valkanov (2006), Ghysels, Sinko and Valkanov, Andreou, Ghysels and Kourtellos (2010) and Andreou, Ghysels and Kourtellos (2013). (en)
  • 混合數據抽樣(Mixed-data sampling, MIDAS)是由 Ghysels 等人所發展的一種計量經濟迴歸或篩選法。簡單迴歸的迴歸變數(regressors)出現頻率高於迴歸值(regressand): 其中y是迴歸值,x是迴歸變數,m表示頻率。舉例來說,如果y是年度,就是季,是誤差項(disturbance)而是落後分配(lag distribution),如B函數或Almon Lag。 這個迴歸模型應用在混合頻率數據上有時可以代替卡爾曼濾波。Bai, Ghysels and Wright(2010)探討MIDAS迴歸與卡爾曼濾波狀態空間模型應用於混合頻率數據的關係。一般來說,後者是方程組,而MIDAS迴歸只有一條方程式。因此,MIDAS迴歸或許效率較差,但比較不容易出錯。以MIDAS迴歸作為近似值的誤差不大。 (zh)
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  • Mixed-data sampling (en)
  • 混合数据抽样 (zh)
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