This paper provides a reverse mode derivative for DSGE models. Reverse mode differentiation enables the efficient computation of gradients from the model likelihood to the model parameters. These gradients can then be used by derivative based sampling algorithms including the No U-Turn Sampler....

We investigate U.S. monetary and fiscal policy interactions in a regime-switching model of monetary and fiscal policy rules where policy mixes are determined by a latent bivariate autoregressive process consisting of monetary and fiscal policy regime factors, each determining a respective policy...

We develop a novel filtering and estimation procedure for parametric option pricing models driven by general affine jump-diffusions. Our procedure is based on the comparison between an option-implied, model-free representation of the conditional log-characteristic function and the model-implied...

This paper develops a dynamic portfolio selection model incorporating economic uncertainty for business cycles. It is assumed that the financial market at each point in time is defined by a hidden Markov model, which is characterized by the overall equity market returns and volatility. The risk...

We propose a semiparametric multivariate estimator and a multivariate score-type testing procedure under a perturbed multivariate fractional process. The estimator is based on the periodogram and uses a local Whittle criterion function which is generalised by an additional constant to capture...

In this paper we consider the Fractional Vector Error Correction model proposed in Avarucci (2007), which is characterized by a richer lag structure than models proposed in Granger (1986) and Johansen (2008, 2009). We discuss the identification issues of the model of Avarucci (2007), following...

We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation...

Recently, several institutions have increased their forecast horizons, and many institutions rely on their past forecast errors to estimate measures of forecast uncertainty. This work addresses the question how the latter estimation can be accomplished if there are only very few errors available...

We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...