We propose an ordered probit framework to simultaneously predict the probabilities of recession, weaker recovery, and stronger recovery. Our approach helps identify (a) whether the next phase is a recession, (b) when the recovery period starts, and (c) whether the recovery would be a weak or strong one compared to historical standards. We believe our approach would help policy makers decide when would be appropriate to (1) start expansionary policies (higher probabilities of recession), (2) continue expansionary policies (higher probabilities of weaker recovery), or (3) turn to neutral/contractionary policies (higher probabilities of stronger recovery). The ordered probit model shows the probabilities of recession staying above 50 percent during all five recessions in our simulated out-of-sample analysis of 1980:Q1–2016:Q1. The probabilities of weaker recovery are consistent with actual periods of below trend growth. Based on 2016:Q1 data, the model suggests a meaningfully higher chance of continuing below trend growth. One key result is that the probability of weaker growth has been persistently higher than the other two scenarios for the past several years. These higher probabilities of weak growth are consistent with the accommodative monetary policy stance of the past eight years.
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With the exception of the recovery from the 1980 recession. However, since that recovery only lasted for 3 quarters, we did not consider that period as a full recovery/expansion.
It is worth noting that if we change the benchmark to the average of the last two recoveries instead of the last recovery then the conclusion does not change.
As noted, we use the NBER dates to determine periods of recession. For more detail see http://www.nber.org/cycles/cyclesmain.html.
See the next section for more detail about the threshold parameters, r 1, r 2, and r 3.
Typically, we face non-stationary issue when we deal with a time series dataset. Augmented Dickey–Fuller test results suggest that all predictors are stationary.
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