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Title: On the estimation of behavioral macroeconomic models via simulated maximum likelihood Authors:  Jiri Kukacka - Charles University, Faculty of Social Sciences (Czech Republic) [presenting]
Tae-Seok Jang - Kyungpook National University (Korea, South)
Stephen Sacht - Department of Economics - Kiel University (Germany)
Abstract: The simulated maximum likelihood estimation method is extended to multivariate macroeconomic optimization problems and employ it to identify the behavioral heuristics of heterogeneous agents in the baseline three-equation New Keynesian model. This approach considerably relaxes restrictive theoretical assumptions and enables a novel estimation of the intensity of choice parameter in the discrete choice switching process. Using Monte Carlo simulation, we first analyze the properties of the estimation framework and study its ability to consistently recover the pseudo-true parameters in a controlled environment. The proposed method favors estimation of the switching parameter; however, the curse of dimensionality arises via a consistent downward bias for idiosyncratic shocks. Our empirical results show that the forward-looking version of both the behavioral and the rational model specifications exhibits good performance. We further identify potential sources of misspecification for the hybrid version. A novel feature of our analysis is that we pin down the switching parameter for the intensity of choice for the Euro Area and US economy.