Learning from news

This paper contributes to two strands of business cycle literature –news shocks and bounded rationality– by assessing the empirical importance of total factor productivity (TFP) news shocks while relaxing the rational expectations assumption. We estimate a medium-scale dynamic stochastic general equilibrium (DSGE) model, incorporating financial frictions and TFP news shocks, under two different expectation formation mechanisms: rational expectations (RE) and adaptive learning (AL). The results suggest that AL amplifies the effects of financial market frictions, leading to three key findings. First, AL improves the model’s fit, as shown in the related literature, and better replicates the volatility of several aggregate variables. Second, the AL amplification results in a deflationary response and a more persistent reaction of lending spreads to TFP news shocks. Third, AL increases the importance of pure news shocks (i.e. purely anticipated shocks), amplifying their effects through both expectation and credit channels. Finally, we show that the dynamics generated by the DSGE model under AL align more closely with empirical vector autoregression evidence than those produced by the RE version of the DSGE model.