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Table 1 In-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 0.397 (0.000) 0.409 (0.000) 0.369 (0.000) 0.397 (0.000) 0.387 (0.000)
 Models with either channel (a) or (b)
  ST-Probit-MCF 0.519 (0.000) 0.513 (0.000) 0.492 (0.000) 0.521 (0.000) 0.558 (0.000)
  AR-Probit-YS 0.867 (0.019) 0.837 (0.004) 0.775 (0.000) 0.781 (0.019) 0.771 (0.084)
 Models without any channel (a), (b) and (c)
  ST-Probit-YS-EI 0.879 (0.033) 0.879 (0.018) 0.868 (0.043) 0.881 (0.005) 0.903 (0.024)
  ST-Probit-YS 1 1 1 1 1
Models Relative LPS
 Model with channels (a), (b), and (c) simultaneously
  AR-Logit-Factor-MIDAS 0.426 (0.000) 0.431 (0.000) 0.418 (0.000) 0.409 (0.000) 0.445 (0.000)
 Models with either channel (a) or (b)
  ST-Probit-MCF 0.517 (0.000) 0.517 (0.000) 0.520 (0.000) 0.512 (0.000) 0.602 (0.000)
  AR-Probit-YS 0.822 (0.000) 0.812 (0.000) 0.757 (0.000) 0.840 (0.002) 0.870 (0.012)
 Models without any channel (a), (b) and (c)
  ST-Probit-YS-EI 0.893 (0.007) 0.901 (0.006) 0.877 (0.038) 0.909 (0.046) 0.920 (0.097)
  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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