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Table 3 Out-of-sample estimation results: relative QPS and LPS

From: On business cycle forecasting

Forecast horizon (months)a 1 3 6 9 12
Models Relative QPS
 Model with channels (a), (b), and (c) simultaneouslyb
  AR-Logit-Factor-MIDAS 0.485 (0.000) 0.528 (0.000) 0.489 (0.000) 0.696 (0.004) 0.664 (0.001)
 Models with either channel (a) or (b)
  ST-Probit-MCF 0.612 (0.005) 0.631 (0.005) 0.657 (0.002) 0.725 (0.019) 0.686 (0.006)
  AR-Probit-YS 0.981 (0.672) 0.949 (0.224) 0.944 (0.356) 0.877 (0.001) 0.867 (0.000)
 Models without any channel (a), (b) and (c)
  ST-Probit-YS-EI 0.889 (0.042) 0.889 (0.070) 0.962 (0.612) 0.912 (0.188) 0.933 (0.314)
  ST-Probit-YS 1 1 1 1 1
Models Relative LPS
 Model with channels (a), (b), and (c) simultaneously
  AR-Logit-Factor-MIDAS 0.703 (0.006) 0.635 (0.028) 0.530 (0.000) 0.734 (0.009) 0.783 (0.002)
 Models with either channel (a) or (b)
  ST-Probit-MCF 0.784 (0.341) 0.691 (0.044) 0.701 (0.012) 0.790 (0.130) 0.874 (0.509)
  AR-Probit-YS 0.923 (0.051) 0.887 (0.000) 0.904 (0.036) 0.856 (0.000) 0.846 (0.000)
 Models without any channel (a), (b) and (c)
  ST-Probit-YS-EI 0.869 (0.020) 0.872 (0.019) 0.931 (0.231) 0.893 (0.080) 0.903 (0.112)
  ST-Probit-YS 1 1 1 1 1
  1. 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
  2. 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
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