Abstract
Previous findings indicate that the inclusion of dynamic factors obtained from a large set of predictors can improve macroeconomic forecasts. In this paper, we explore three possible further developments: (i) using automatic criteria for choosing those factors which have the greatest predictive power; (ii) using only a small subset of preselected predictors for the calculation of the factors; and (iii) utilizing frequency-domain information for the estimation of the factor models. Reanalyzing a standard macroeconomic dataset of 143 U.S. time series and using the major measures of economic activity as dependent variables, we find that (i) is not helpful, whereas focusing on the low-frequency components of the factors and disregarding the high-frequency components can actually improve the forecasting performance for some variables. In the case of the gross domestic product, a combination of (ii) and (iii) yields the best results.
Highlights
Factor models have become increasingly popular for the efficient extraction of information from a large number of macroeconomic variables
Using the macroeconomic dataset of Stock and Watson (2012), we explored various methods to improve the performance of the standard factor-augmented forecast, which is based on lagged values of the variable of interest and a small number of factors obtained from a large set of predictors
We found that the use of automatic criteria for the selection of the optimal subset of factors is not helpful, whether the order of the factors is fixed or not
Summary
Factor models have become increasingly popular for the efficient extraction of information from a large number of macroeconomic variables. To investigate the forecasting performance of these models, Eickmeier and Ziegler (2008) conducted a meta-analysis of 52 studies and obtained mixed results that depended on the region, the category of the variable to be predicted, the size of the dataset, and the estimation technique. This is aggravated by the facts that it is a priori not clear how many factors should be included (Bai and Ng 2002, 2006, 2008b) and that the findings change noticeably when different sub-periods (states of the business cycle) are considered (Kim and Swanson 2014). As pointed out by the authors, the obvious procedure to include the factors in their natural order fails to take their predictive power into account, it could possibly be improved by using fewer but more informative predictors (targeted-predictors)
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