Estimating Models with Dispersed Information
对DSGE模型进行似然估计，发现企业不完全共同知识比Calvo价格粘性更能解释名义冲击对产出和通胀的持续影响，且企业很少关注总体名义条件。
We conduct likelihood evaluation of a DSGE model in which firms have imperfect common knowledge. Imperfect common knowledge is found to be more successful than price stickiness à la Calvo to account for the highly persistent effects of nominal shocks on output and inflation. Our likelihood analysis suggests that firms pay little attention to aggregate nominal conditions. This paper shows that such allocation of attention is plausible because it is optimal for firms with a reasonably small size of information frictions and a size of idiosyncratic uncertainty that is in line with the micro evidence on price changes.