They then outline a number of approaches to the selection of factor proxies (observed variables that proxy unobserved estimated factors) using the statistics developed in Bai and Ng (2006a,b). The authors' approach to factor proxy selection is examined via a small Monte Carlo experiment, where evidence supporting their proposed methodology is presented, and via a large set of prediction experiments using the panel dataset of Stock and Watson (2005). One of their main empirical findings is that their "smoothed" approaches to factor proxy selection appear to yield predictions that are often superior not only to a benchmark factor model, but also to simple linear time series models, which are generally difficult to beat in forecasting competitions. In some sense, by using the authors' approach to predictive factor proxy selection, one is able to open up the "black box" often associated with factor analysis, and to identify actual variables that can serve as primitive building blocks for (prediction) models of a host of macroeconomic variables, and that can also serve as policy instruments, for example. The authors' findings suggest that important observable variables include various S&P500 variables, including stock price indices and dividend series; a 1-year Treasury bond rate; various housing activity variables; industrial production; and exchange rates.

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