On the Estimation of Behavioral Macroeconomic Models via Simulated Maximum Likelihood

Abstract

In this paper, we introduce the simulated maximum likelihood method for identifying behavioral heuristics of heterogeneous agents in the baseline three-equation New Keynesian model. The method is extended to multivariate macroeconomic optimization problems, and the estimation procedure is applied to empirical data sets. This approach considerably relaxes restrictive theoretical assumptions and enables a novel estimation of the intensity of choice parameter in discrete choice. In Monte Carlo simulations, we analyze the properties and behavior of the estimation method, which provides important information on the behavioral parameters of the New Keynesian model. 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 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.

Forecast heuristics, consumer expectations, and New-Keynesian macroeconomics: A Horse race

“…In general, given the highly non-linear structure of the BR model specification, pinning down the switching parameter becomes difficult (cf. Kukacka et al (2018)).…”

“…This can enhance the reliability and sensitivity of the parameter estimates of the models according to these structural changes in the economy. This approach is motivated by the study of Kukacka et al (2018) who also consider this kind of data segmentation when evaluating a BR model via the SML estimation procedure.…”

Macroeconomic stability of price level targeting in a model of heterogeneous expectations.

“…HSM has been successfully applied to a plethora of experimental data (see Anufriev and Hommes, 2012a, for a representative example), as well as a number of empirical data sets, ranging from financial markets (see Boswijk et al (2007); Franke and Westerhoff (2012); Kukacka and Barunik (2017) for typical examples and Lux and Zwinkels (2018) for a thorough literature review), through housing market (eg. Bolt et al, 2019;Kouwenberg and Zwinkels, 2014), to macroeconomic applications (among many examples, see Cornea-Madeira et al, 2019;Grazzini et al, 2017;Jang and Sacht, 2021;Kukacka et al, 2018). Despite the relative novelty of this literature, it shows that policy makers should at least consider a possibility that the economic agents have myopic and heterogeneous expectations (Deak et al, 2020;Jump and Levine, 2019).…”

Macroeconomic Stability of Price Level Targeting in a Model of Heterogeneous Expectations

“…HSM has been successfully applied to a plethora of experimental data (see Anufriev and Hommes, 2012a, for a representative example), as well as a number of empirical data sets, ranging from financial markets (see Boswijk et al (2007); Franke and Westerhoff (2012); Kukacka and Barunik (2017) for typical examples and Lux and Zwinkels (2018) for a thorough literature review), through housing market (eg. Bolt et al, 2019;Kouwenberg and Zwinkels, 2014), to macroeconomic applications (among many examples, see Cornea-Madeira et al, 2019;Grazzini et al, 2017;Jang and Sacht, 2021;Kukacka et al, 2018). Despite the relative novelty of this literature, it shows that policy makers should at least consider a possibility that the economic agents have myopic and heterogeneous expectations (Deak et al, 2020;Jump and Levine, 2019).…”