This paper extends two optimization routines to deal with objective functions for DSGE models. The optimization routines are i) a version of Simulated Annealing developed by Corana, Marchesi & Ridella (1987), and ii) the evolutionary algorithm CMA-ES developed by Hansen, Müller & Koumoutsakos...

The presence of i) stochastic trends, ii) deterministic trends, and/or iii) stochastic volatility in DSGE models may imply that the agents' objective functions attain infinite values. We say that such models do not have a valid micro foundation. The paper derives sufficient conditions which...

This paper studies the pruned state-space system for higher-order approximations to the solutions of DSGE models. For second- and third-order approximations, we derive the statistical properties of this system and provide closed-form expressions for ?first and second unconditional moments and...

This paper argues that a specification of stochastic volatility commonly used to analyze the Great Moderation in DSGE models may not be appropriate, because the level of a process with this specification does not have conditional or unconditional moments. This is unfortunate because agents may...

This paper studies how non-Gaussian shocks affect risk premia in DSGE models approximated to second and third order. Based on an extension of the results in Schmitt-Grohé & Uribe (2004) to third order, we derive propositions for how rare disasters, stochastic volatility, and GARCH affect any...

While stochastic volatility models improve on the option pricing error when compared to the Black-Scholes-Merton model, mispricings remain. This paper uses mixed normal heteroskedasticity models to price options. Our model allows for significant negative skewness and time varying higher order...

This paper proposes a model that simultaneously captures long memory and structural breaks. We model structural breaks through irreversible Markov switching or so-called change-point dynamics. The parameters subject to structural breaks and the unobserved states which determine the position of...

This paper presents the Matlab package DeCo (Density Combination) which is based on the paper by Billio et al. (2013) where a constructive Bayesian approach is presented for combining predictive densities originating from different models or other sources of information. The combination weights...

The restrictions implied by the theory of time-consistent monetary policy are imposed on empirical data. Model estimation is conducted using Bayesian Markov chain Monte Carlo techniques. We are able to identify two major regimes regarding the policy of the Federal Reserve from 1970 to 2008....

A modification of the self-perturbed Kalman filter of Park and Jun (1992) is proposed for the on-line estimation of models subject to parameter instability. The perturbationterm in the updating equation of the state covariance matrix is weighted by the measurement error variance, thus avoiding...