On business cycle forecasting

Frontiers of Business Research in China

Table 2 In-sample estimation results: AUC

Forecast horizon (months)^a	1	3	6	9	12
Models	AUC
Model with channels (a), (b), and (c) simultaneously^b
AR-Logit-Factor-MIDAS	0.980 (0.000)	0.980 (0.000)	0.980 (0.000)	0.981 (0.000)	0.981 (0.000)
Models with either channel (a) or (b)
ST-Probit-MCF	0.970 (0.000)	0.971 (0.000)	0.970 (0.000)	0.969 (0.000)	0.964 (0.000)
AR-Probit-YS	0.912 (0.000)	0.916 (0.000)	0.927 (0.000)	0.917 (0.000)	0.923 (0.000)
Models without any channel (a), (b) and (c)
ST-Probit-YS-EI	0.890 (0.000)	0.888 (0.000)	0.900 (0.000)	0.897 (0.000)	0.912 (0.000)
ST-Probit-YS	0.863 (0.000)	0.864 (0.000)	0.868 (0.000)	0.871 (0.000)	0.893 (0.000)

^a For each forecast horizon N (where N = 1, 3, 6, 9, 12), we evaluate the ability of each model to forecast the probability of a recession within the next N months
^bChannel (a): using a flexible function form by including a lagged recession probability into a dynamic Logit or Probit model of recession forecasting; Channel (b): employing a dynamic factor model to extract common factors from many monthly or weekly economic and financial variables; Channel (c): applying MIDAS to incorporate the mixed-frequency common factors in the dynamic Logit framework