Banks’ leverage behaviour in a two-agent new Keynesian model

Abstract

Bayesian methods for graphical log-linear marginal models has not been developed in the same extend as traditional frequentist approaches. In this work, we introduce a novel Bayesian approach for quantitative learning for such models. They belong to curved exponential families that are difficult to handle from a Bayesian perspective. Furthermore, the likelihood cannot be analytically expressed as a function of the marginal log-linear interactions, but only in terms of cell counts or probabilities. Posterior distributions cannot be directly obtained, and MCMC methods are needed. Finally, a welldefined model requires parameter values that lead to compatible marginal probabilities. Hence, any MCMC should account for this important restriction. We construct a fully automatic and efficient MCMC strategy for quantitative learning for graphical log-linear marginal models that handles these problems. While the prior is expressed in terms of the marginal log-linear interactions, we build an MCMC algorithm which employs a proposal on the probability parameter space. The corresponding proposal on the marginal log-linear interactions is obtained via parameter transformations. By this strategy, we achieve to move within the desired target space. At each step we directly work with welldefined probability distributions. Moreover, we can exploit a conditional conjugate setup to build an

Endogenous Growth, Skill Obsolescence, and Output Hysteresis in a New Keynesian Model with Unemployment

“…All these papers use a similar learning‐by‐doing mechanism as we do, but assume a perfectly competitive labor market. One exception is Boitani and Punzo (2019) who also consider labor market frictions and learning‐by‐doing. However, they focus on the distributive effects of financial shocks and they do not consider training costs as we do.…”