Short-term forecasts of economic activity: Are fortnightly factors useful?

被引:1
|
作者
Monteforte, Libero [1 ,2 ]
Raponi, Valentina [3 ,4 ]
机构
[1] Banca Italia, Econ Outlook & Monetary Policy Directorate, Rome, Italy
[2] Ufficio Parlamentare Bilancio, Macroecon Anal Dept, Rome, Italy
[3] Imperial Colege London, Imperial Coll, Business Sch, London, England
[4] Sapienza Univ Roma, MEMOTEF, Rome, Italy
关键词
factor models; forecasting; mixed frequency data; Kalman filter; state-space models; temporal disaggregation; MAXIMUM-LIKELIHOOD-ESTIMATION; DYNAMIC-FACTOR MODEL; TEMPORAL AGGREGATION; MIDAS REGRESSIONS; COINCIDENT INDEX;
D O I
10.1002/for.2565
中图分类号
F [经济];
学科分类号
02 ;
摘要
A short-term mixed-frequency model is proposed to estimate and forecast Italian economic activity fortnightly. We introduce a dynamic one-factor model with three frequencies (quarterly, monthly, and fortnightly) by selecting indicators that show significant coincident and leading properties and are representative of both demand and supply. We conduct an out-of-sample forecasting exercise and compare the prediction errors of our model with those of alternative models that do not include fortnightly indicators. We find that high-frequency indicators significantly improve the real-time forecasts of Italian gross domestic product (GDP); this result suggests that models exploiting the information available at different lags and frequencies provide forecasting gains beyond those based on monthly variables alone. Moreover, the model provides a new fortnightly indicator of GDP, consistent with the official quarterly series.
引用
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页码:207 / 221
页数:15
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