Forecasting the Consumer Confidence Index with tree-based MIDAS regressions |
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Institution: | 1. Department of Management, Università Politecnica delle Marche, Ancona, Italy;2. Department of Economics and Social Science, Università Politecnica delle Marche, Ancona, Italy;1. Department of Financial Engineering, Ajou University, Suwon, 16499, Republic of Korea;2. Department of Applied Mathematics & Institute of Natural Science, Kyung Hee University, Yongin, 17104, Republic of Korea;3. Department of Mathematical Sciences, Seoul National University, Seoul, 08826, Republic of Korea;1. Graduate School of Economics, Kobe University, 2-1 Rokko-dai, Nada, Kobe, 657-8501, Japan;2. Institute of Social and Economic Research, Osaka University, 6-1, Mihogaoka, Ibaraki, Osaka, 567-0047, Japan;1. Texas A&M University, Department of Finance, Mays Business School, College Station, TX, 77843, USA;2. University of Valladolid (Spain), NRU Higher School of Economics (Russia), School of Business and Economics, Avda. Valle Del Esgueva 6, 47011, Valladolid, Spain;3. University of Valladolid, School of Business and Economics, Avda. Valle Del Esgueva 6, 47011, Valladolid, Spain;1. CREM UMR 6211, Université de Caen Normandie, France;2. ICN Business School-CEREFIGE, Nancy, France |
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Abstract: | The macroeconomic literature has recently uncovered the importance of the consumer confidence variations at driving business cycles. However, it remains a challenge to predict changes in agents'confidence by exploiting the information from ultra high-frequency sentiment data extracted from social media. Based on the mixed data sampling (MIDAS) literature, we propose a new MIDAS method that introduces regression tree-based algorithms into the MIDAS framework. Our method is more flexible at sampling high-frequency lagged regressors compared to existing MIDAS models with tightly parametrized functions of lags. In an out-of-sample forecasting exercise for the Consumer Confidence Index, our results reveal that (i) the proposed procedure exploits more fully the information from historical sentiment data and (ii) our method substantially improves the forecast accuracy and confirms the role of social media at affecting the consumer confidence. |
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Keywords: | Consumer confidence forecast Twitter sentiment MIDAS regression Machine learning C53 E27 D83 |
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