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Stopping Rules for Targeted Di Forecasting usion Index

By Jack Fosten


This paper proposes methods to combine the bene ts of two extant renements to the di usion index forecasting model. We argue that LASSOtype pre-selection of variables for factor estimation, proposed by Bai and Ng (2008), should be more carefully scrutinised as the surviving variables may have undesirable factor model properties such as highly correlated idiosyncratic errors or weak factor in uence. We therefore propose empirical methods drawing upon both Bai and Ng (2008) and Boivin and Ng (2006) to target the factor dataset taking both predictive ability and factor properties into account. We nd our proposed methods work well in forecasting U.S. industrial production over a range of forecast horizons, as well as for a broader set of macroeconomic and nancial series. This con rms the assertion that we should be careful when using LASSO-type pre-selection as the sole targeting method. Another proposed method which allows some discarded variables to enter the model as leading indicators performs well for industrial production but less so over the wider range of series, suggesting that a hybrid DGP with factors and variables is not correct for the majority of series considered. Accounting for a structural break in the factor space in 1984 by running post-break forecasts does not a ect the success of the proposed methods

Topics: Forecasting, Factor Models, LASSO, Least angle regression, Shrinkage Estimation, Model Selection
Year: 2013
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