Forecast Pooling or Information Pooling During Crises? MIDAS Forecasting of GDP in a Small Open Economy
Hwee Kwan Chow () and
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Hwee Kwan Chow: School of Economics, Singapore Management University
Daniel Han: School of Economics, Singapore Management University
No 6-2021, Economics and Statistics Working Papers from Singapore Management University, School of Economics
This study compares two distinct approaches, pooling forecasts from single indicator MIDAS models versus pooling information from indicators into factor MIDAS models, for short-term Singapore GDP growth forecasting with a large ragged-edge mixed frequency dataset. We investigate their relative predictive performance in a pseudo-out-of-sample forecasting exercise from 2007Q4 to 2020Q3. In the stable growth non-crisis period, no substantial difference in predictive performance is found across forecast models. We find factor MIDAS models dominate both the quarterly benchmark model and the forecast pooling strategy by wide margins in the Global Financial Crisis and the Covid-19 crisis. Reflecting the small open nature of the economy, pooling single indicator forecasts from a small subgroup of foreign-related indicators beats the benchmark, offering a quick method to incorporate timely information for practitioners who have difficulty updating a large dataset. Nonetheless, the information pooling approach retains its superior ability at tracking rapid output changes during crises.
Keywords: Forecast evaluation; Factor MIDAS; pooling GDP forecasts; global financial crisis; Covid-19 pandemic crisis (search for similar items in EconPapers)
JEL-codes: C22 C53 C55 (search for similar items in EconPapers)
Pages: 37 pages
New Economics Papers: this item is included in nep-ets, nep-for, nep-isf, nep-mac and nep-sea
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Persistent link: https://EconPapers.repec.org/RePEc:ris:smuesw:2021_006
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