Martin M. Andreasen

An Estimated DSGE Model: Explaining Variation in Nominal Term Premia, Real Term Premia, and Inflation Risk Premia

This paper develops a DSGE model which is shown to explain variation in the nominal and real term structure as well as inflation surveys and four macrovariables for the UK economy. The model is estimated based on a third-order approximation to allow for time-varying term premia. We find a fall in nominal term premia during the 1990s which mainly is caused by lower inflation risk premia. A structural decomposition further shows that this fall is driven by negative preference shocks, lower fixed production costs, positive investment shocks, and a more aggressive response to inflation by the Bank of England.

On the Effects of Rare Disasters and Uncertainty Shocks for Risk Premia in Non-Linear DSGE Models

This paper studies how rare disasters and uncertainty shocks affect risk premia in DSGE models approximated to second and third order. Based on an extension of the results in Schmitt-GrohA© & Uribe (2004) to third order, we derive propositions for how rare disasters, stochastic volatility, and GARCH affect any type of risk premia in a wide class of DSGE models. To quantify the effects, we set up a standard New Keynesian DSGE model where total factor productivity includes rare disasters, stochastic volatility, and GARCH. We find that rare disasters increase the level of the 10-year nominal term premium, whereas a key effect of uncertainty shocks, i.e. stochastic volatility and GARCH, is an increase in the variability of this premium. (Copyright: Elsevier)

NON‐LINEAR DSGE MODELS AND THE CENTRAL DIFFERENCE KALMAN FILTER

This paper introduces a quasi maximum likelihood (QML) approach based on the central difference Kalman filter to estimate non-linear DSGE models with potentially non-Gaussian shocks. We argue that this estimator can be expected to be consistent and asymptotically normal for DSGE models solved up to third order. These properties are verified in a Monte Carlo study for a DSGE model solved to second and third order with structural shocks that are Gaussian, Laplace distributed, or display stochastic volatility.

Non-Linear DSGE Models, The Central Difference Kalman Filter, and The Mean Shifted Particle Filter

This paper shows how non-linear DSGE models with potential non-normal shocks can be estimated by Quasi-Maximum Likelihood based on the Central Difference Kalman Filter (CDKF). The advantage of this estimator is that evaluating the quasi log-likelihood function only takes a fraction of a second. The second contribution of this paper is to derive a new particle filter which we term the Mean Shifted Particle Filter (MSPFb). We show that the MSPFb outperforms the standard Particle Filter by delivering more precise state estimates, and in general the MSPFb has lower Monte Carlo variation in the reported log-likelihood function.

Explaining Macroeconomic and Term Structure Dynamics Jointly in a Non-Linear DSGE Model

This paper shows how a standard DSGE model can be extended to reproduce the dynamics in the 10 year yield curve for the post-war US economy with a similar degree of precision as in reduced form term structure models. At the same time, we are able to reproduce the dynamics of four key macro variables almost perfectly. Our extension of a standard DSGE model is to introduce three non-stationary shocks which allow us to explain interest rates with medium and long maturities without distorting the dynamics of the macroeconomy.

The Business Cycle Implications of Banks’ Maturity Transformation

This paper develops a DSGE model in which banks use short-term deposits to provide firms with long-term credit. The demand for long-term credit arises because firms borrow in order to finance their capital stock which they only adjust at infrequent intervals. We show within a real business cycle framework that maturity transformation in the banking sector in general attenuates the output response to a technological shock. Implications of long-term nominal contracts are also examined in a New Keynesian version of the model, where we find that maturity transformation reduces the real effects of a monetary policy shock.

The SR Approach: A New Estimation Procedure for Non-Linear and Non-Gaussian Dynamic Term Structure Models

This paper suggests a new approach for estimating linear and non-linear dynamic term structure models with latent factors. We impose no distributional assumptions on the factors which therefore may be non-Gaussian. The novelty of our approach is to use many observables (yields or bond prices) in the cross-section dimension. This implies that the latent factors can be determined quite accurately by a sequence of cross-section regressions. We also show how output from these regressions can be used to obtain model parameters by a two- or three-step moment-based estimation procedure.

Stochastic Volatility and DSGE Models

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 as a result expect productivity and hence consumption to be inifinite in all future periods. This observation is followed by three ways to overcome the problem.

Non-linear DSGE Models and The Optimized Central Difference Particle Filter

We improve the accuracy and speed of particle filtering for non-linear DSGE models with potentially non-normal shocks. This is done by introducing a new proposal distribution which (i) incorporates information from new observables and (ii) has a small optimization step that minimizes the distance to the optimal proposal distribution. A particle filter with this proposal distribution is shown to deliver a high level of accuracy even with relatively few particles, and it is therefore much more efficient than the standard particle filter.

How Non-Gaussian Shocks Affect Risk Premia in Non-Linear DSGE Models

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 work by Schmitt-Grohe and Uribe to third order, we derive propositions for how rare disasters, stochastic volatility, and GARCH affect any risk premia in a wide class of DSGE models. To quantify these effects, we then set up a standard New Keynesian DSGE model where total factor productivity includes rare disasters, stochastic volatility, and GARCH. We find that rare disasters increase the mean level of the ten-year nominal term premium, whereas a key effect of stochastic volatility and GARCH is an increase in the variability of this premium.