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Table 5 Forecasting the conventional target variable: in-sample estimation results

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 0.302 (0.000) 0.443 (0.000) 0.577 (0.000) 0.765 (0.010) 0.862 (0.085)
 Models with either channel (a) or (b)
  ST-Probit-MCF 0.394 (0.000) 0.539 (0.000) 0.762 (0.000) 0.941 (0.360) 1.034 (0.530)
  AR-Probit-YS 0.680 (0.000) 0.708 (0.000) 0.808 (0.002) 0.945 (0.339) 1.000 (0.993)
 Models without any channel (a), (b) and (c)
  ST-Probit-YS-EI 0.905 (0.002) 0.908 (0.012) 0.910 (0.015) 0.986 (0.391) 0.999 (0.550)
  ST-Probit-YS 1 1 1 1 1
Models Relative LPS
 Model with channels (a), (b), and (c) simultaneously
  AR-Logit-Factor-MIDAS 0.319 (0.000) 0.444 (0.000) 0.606 (0.000) 0.807 (0.034) 0.893 (0.141)
 Models with either channel (a) or (b)
  ST-Probit-MCF 0.387 (0.000) 0.558 (0.000) 0.761 (0.001) 0.936 (0.333) 1.056 (0.277)
  AR-Probit-YS 0.647 (0.000) 0.668 (0.000) 0.739 (0.000) 0.866 (0.016) 0.967 (0.302)
 Models without any channel (a), (b) and (c)
  ST-Probit-YS-EI 0.938 (0.059) 0.909 (0.008) 0.914 (0.025) 0.984 (0.287) 1.000 (0.912)
  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 in the Nth month
  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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