a b s t r a c t
This paper studies two refinements to the method of factor forecasting. First, we consider the method
of quadratic principal components that allows the link function between the predictors and the factors
to be non-linear. Second, the factors used in the forecasting equation are estimated in a way to
take into account that the goal is to forecast a specific series. This is accomplished by applying the
method of principal components to `targeted predictors' selected using hard and soft thresholding rules.
Our three main findings can be summarized as follows. First, we find improvements at all forecast
horizons over the current diffusion index forecasts by estimating the factors using fewer but informative
predictors. Allowing for non-linearity often leads to additional gains. Second, forecasting the volatile
one month ahead inflation warrants a high degree of targeting to screen out the noisy predictors. A
handful of variables, notably relating to housing starts and interest rates, are found to have systematic
predictive power for inflation at all horizons. Third, the targeted predictors selected by both soft and hard
thresholding changes with the forecast horizon and the sample period. Holding the set of predictors fixed
as is the current practice of factor forecasting is unnecessarily restrictive.
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