Abstract Details
Activity Number:
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460
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #313731
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Title:
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WITHDRAWN: Forecasting Macroeconomic Time Series: Lasso-Based Approaches and Their Forecast Combinations with Dynamic Factor Models
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Author(s):
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Jiahan Li and Weiye Chen
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Companies:
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University of Notre Dame and University of Notre Dame
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Keywords:
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High-dimensional time series ;
Model selection ;
Dynamic factor model ;
Forecasts combination
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Abstract:
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In a data-rich environment, dynamic factor model has been the most popular models for forecasting with a large number of predictors. In this paper, we investigate a category of LASSO-based approaches and evaluate their predictive abilities in forecasting twenty important macroeconomic variables. We also show analytically and empirically that combing forecasts from LASSO-based models and those from the dynamic factor models could further reduce the mean square forecast error (MSFE). Our three main findings can be summarized as follows. First, for most of the variables under investigation, all LASSO-based models outperform dynamic factor models in out-of-sample forecast evaluations. Second, by extracting information and formulating predictors at the economically meaningful block levels, the new method greatly enhances the model interpretability. Third, once forecasts from a LASSO-based approach and those from dynamic factor model are combined by forecasts combination techniques, the combined forecasts are significantly better than dynamic factor model forecasts for all twenty variables we investigated.
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