Rational expectations has been the dominant way to model expectations, but the literature\ud has quickly moved to a more realistic assumption of boundedly rational learning where agents\ud are assumed to use only a limited set of information to form their expectations. A standard\ud assumption is that agents form expectations by using the correctly speci ed reduced form model\ud of the economy, the minimal state variable solution (MSV), but they do not know the parameters.\ud However, with medium-sized and large models the closed-form MSV solutions are di¢ cult to attain\ud given the large number of variables that could be included. Therefore, agents base expectations\ud on a misspeci ed MSV solution. In contrast, we assume agents know the deep parameters of\ud their own optimising frameworks. However, they are not assumed to know the structure nor the\ud parameterisation of the rest of the economy, nor do they know the stochastic processes generating\ud shocks hitting the economy. In addition, agents are assumed to know that the changes (or the\ud growth rates) of fundament variables can be modelled as stationary ARMA(p,q) processes, the\ud exact form of which is not, however, known by agents. This approach avoids the complexities of\ud dealing with a potential vast multitude of alternative mis-speci ed MSVs.\ud Using a new Multi-country Euro area Model with Boundedly Estimated Rationality we show\ud this approach is compatible with the same limited information assumption that was used in\ud deriving and estimating the behavioral equations of di¤erent optimizing agents. We nd that\ud there are strong di¤erences in the adjustment path to the shocks to the economy when agent form\ud expectations using our learning approach compared to expectations formed under the assumption\ud of strong rationality. Furthermore, we nd that some variation in expansionary scal policy in\ud periods of downturns compared to boom periods
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