Outliers can cause significant errors in forecasting, and it is essential to reduce their impact without losing the information they store. Information loss naturally arises if observations are dropped from the dataset. Thus, two alternative procedures are considered here: the Fast Minimum Covariance Determinant and the Iteratively Reweighted Least Squares. The procedures are used to estimate factor models robust to outliers, and a comparison of the forecast abilities of the robust approaches is carried out on a large dataset widely used in economics. The dataset includes observations relative to the 2009 crisis and the COVID-19 pandemic, some of which can be considered outliers. The comparison is carried out at different sampling frequencies and horizons, in-sample and out-of-sample, on relevant variables such as GDP, Unemployment Rate, and Prices for both the US and the EU.
Citation: Fausto Corradin, Monica Billio, Roberto Casarin. Forecasting Economic Indicators with Robust Factor Models[J]. National Accounting Review, 2022, 4(2): 167-190. doi: 10.3934/NAR.2022010
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Outliers can cause significant errors in forecasting, and it is essential to reduce their impact without losing the information they store. Information loss naturally arises if observations are dropped from the dataset. Thus, two alternative procedures are considered here: the Fast Minimum Covariance Determinant and the Iteratively Reweighted Least Squares. The procedures are used to estimate factor models robust to outliers, and a comparison of the forecast abilities of the robust approaches is carried out on a large dataset widely used in economics. The dataset includes observations relative to the 2009 crisis and the COVID-19 pandemic, some of which can be considered outliers. The comparison is carried out at different sampling frequencies and horizons, in-sample and out-of-sample, on relevant variables such as GDP, Unemployment Rate, and Prices for both the US and the EU.
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