Dynamic Stochastic General Equilibrium Models in a Liquidity Trap and Self-Organizing State Space Modeling

Abstract

This paper proposes a novel approach to estimate dynamic stochastic general equilibrium models in a liquidity trap. Our approach is based on the Monte Carlo particle filter and a self-organizing state space model. The main feature of this paper is that we estimate most parameters of DSGE models using the time-varying-parameter approach, which is often used to infer invariant parameters in practice. Adopting our method creates the great advantage that the structural changes of parameters are detected naturally. Therefore, our method is a framework to investigate how stable structural parameters are. Moreover, it is a great contribution that natural rates of macroeconomic data, parameters, and unknown states are estimated simultaneously. The estimates of natural rates, thus, are consistent with DSGE models. In empirical analysis, we estimate new Keynesian DSGE models in a liquidity trap using Japanese macroeconomic data, which includes the zero-interest-rate period (1999-2006). The analysis shows that the target rate of in inflation is too low in the 1990s and the 2000s, and it causes deflation in the Japanese economy.