Our approach, in contrast, explores the fact that, in Lucas setting, we can can have a twofold interpretation of analysts' forecasts. The first one is to suppose analysts' forecasts are in agents' information set while the second interpretation rests on the fact all agents are equal and then the analysts are themselves the agents who is solving Lucas problem. The first interpretation implies strong correlation and even causality in the sense of \citet{Granger_1969} between forecasts and actual prices while the second interpretation makes actual prices converge in distribution to analysts' forecasts.
Also Lucas model implies marginal rate of inter-temporal substitution, risk aversion and discount factor may be estimated from macroeconomic data since the model has a representative agent. Besides, this approach allows to rationalize stock analysts forecasts, in particular, when it comes to fundamentalist analysis.