Seeing Inside the Black Box: Using Diffusion Index Methodology to Construct Factor Proxies in Large Scale Macroeconomic Time Series Environments
综述了扩散指数文献，提出用Bai和Ng的统计量选择因子代理变量，通过蒙特卡洛实验和预测实验验证，发现平滑方法能提升预测效果，并识别出S&P500指数、国债利率等关键可观测变量。
In economics, common factors are often assumed to underlie the co-movements of a set of macroeconomic variables. For this reason, many authors have used estimated factors in the construction of prediction models. In this article, we begin by surveying the extant literature on diffusion indexes. We 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 Bai , J. , Ng , S. (2006a). Confidence intervals for diffusion index forecasts and inference for factor-augmented regressions. Econometrica 74:1133–1150.[Crossref], [Web of Science ®] , [Google Scholar],b). Our approach to factor proxy selection is examined via a small Monte Carlo experiment, where evidence supporting our proposed methodology is presented, and via a large set of prediction experiments using the panel dataset of Stock and Watson (2005 Stock , J. , Watson , M. ( 2005 ). Implications of Dynamic Factor Models for VAR Analysis. Working Paper 11467, National Bureau of Economic Research . [Google Scholar]). One of our main empirical findings is that our “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 our 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. Our 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.