Forecasting National Recessions Using State‐Level Data
研究使用州级就业增长数据，通过贝叶斯模型平均的probit模型，发现其能显著提升美国经济周期阶段的即时预测和短期预测准确性，尤其在衰退月份。
We investigate whether there is information useful for identifying U.S. business cycle phases contained in subnational measures of economic activity. Using a probit model to forecast the National Bureau of Economic Research expansion and recession classification, we assess the incremental information content of state‐level employment growth over a commonly used set of national‐level predictors. As state‐level data adds a large number of predictors to the model, we employ a Bayesian model averaging procedure to construct forecasts. Based on a variety of forecast evaluation metrics, we find that including state‐level employment growth substantially improves nowcasts and very short‐horizon forecasts of the business cycle phase. The gains in forecast accuracy are concentrated during months of national recession.