Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. By using an autoregressive distributed lag model, this paper analyzes how optimal Litterman prior changes when an economy is hit by a recession. The results show that a sharp economic slowdown changes the optimal prior in two directions. First, it changes the structure of the optimal weight prior by setting smaller weight on the lagged dependent variable compared to variables containing more recent information. Second, greater uncertainty brought by a rapid economic downturn requires more space for coefficient variation which is set by the overall tightness parameter. It is shown that the optimal overall tightness parameter may increase to such an extent that Bayesian ADL becomes equivalent to frequentist ADL.