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This paper shows how to use the Kalman filter (Kalman 1960) to back out the shocks of a dynamic stochastic general equilibrium model. In particular, we use the smoothing algorithm as described in Hamilton (1994) to estimate the shocks of a sticky-prices and sticky-wages model using all the...
Persistent link: https://www.econbiz.de/10013032852
This paper introduces a nonlinear certainty-equivalent approximation method for dynamic stochastic problems. We first introduce a novel, stable, and efficient method for computing the decision rules in deterministic dynamic economic problems. We use the results as nonlinear and global...
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This paper compares different solution methods for computing the equilibrium of dynamic stochastic general equilibrium (DSGE) models with rare disasters along the lines of those proposed by Rietz (1988), Barro (2006), Gabaix (2012), and Gourio (2012). DSGE models with rare disasters require...
Persistent link: https://www.econbiz.de/10011994514
We highlight a state variable misspecification with one accepted method to implement stochastic volatility (SV) in DSGE models when transforming the nonlinear state-innovation dynamics to its linear representation. Although the technique is more efficient numerically, we show that it is not...
Persistent link: https://www.econbiz.de/10012932443
In this paper, we propose a new method to forecast macroeconomic variables that combines two existing approaches to mixed-frequency data in DSGE models. The first existing approach estimates the DSGE model in a quarterly frequency and uses higher frequency auxiliary data only for forecasting...
Persistent link: https://www.econbiz.de/10013465707
This paper presents a framework to undertake likelihood-based inference in nonlinear dynamic equilibrium economies. The authors develop a sequential Monte Carlo algorithm that delivers an estimate of the likelihood function of the model using simulation methods. This likelihood can be used for...
Persistent link: https://www.econbiz.de/10014048588
A medium-scale nonlinear DSGE model is estimated (54 variables, 29 state variables, 7 observed variables). The model includes stock market. RMSE of in sample and out of sample forecasts are calculated. The nonlinear DSGE model with measurement errors outperform AR(1), VAR(1), linearized DSGE in...
Persistent link: https://www.econbiz.de/10013055171