FORECASTING TURNING POINTS IN METROPOLITAN EMPLOYMENT GROWTH RATES USING BAYESIAN TECHNIQUES
将Zellner等人提出的贝叶斯方法应用于美国大都市区，预测就业增长率的上升和下降转折点，发现该方法在区域层面同样有效，能正确预测约70%的下降和80%的上升。
ABSTRACT In this paper, I adapt to the regional level a Bayesian approach developed by Zellner, Hong, and Min (1990) to analyze forecasts of turning points in a multicountry setting. The techniques applied to a regional setting treat the individual metropolitan areas in the same way that Zellner, Hong, and Min treated countries. The findings in this study indicate that the techniques and models employed by Zellner, Hong, and Min work just as well at the metropolitan‐area level as they did in the multicountry setting. The best models, from those studied here, forecast around 70 percent of the downturns and 80 percent of the upturns correctly, which compares favorably to the performance of the same techniques in Zellner, Hong, and Min.