Abstract Within the context of a financial accelerator model, we model time-varying uncertainty (i.e. risk shocks) through the use of a mixture normal model with time variation in the weights applied to the underlying distributions characterizing entrepreneur productivity. Specifically, we model...

This paper shows that greater uncertainty about monetary policy can lead to a decline in nominal interest rates. In the context of a limited participation model, monetary policy uncertainty is modeled as a mean preserving spread in the distribution for the money growth process. This increase in...

Typically real-business-cycle models are assessed by their ability to mimic the covariances and variances of actual business cycle data. Recently, however, advocates of RBC models have used them to fit the historical path of real GDP using the Solow residual as a driving process. We demonstrate...

This paper analyzes the role of stochastic uncertainty in a multi-sector housing model with financial frictions. We include time varying uncertainty (i.e. risk shocks) in the technology shocks that affect housing production and provide estimates of the time-series properties of risk shocks by...

Within the context of a stochastic growth economy, the shocks to technology are modeled as a four-state Markov process. The parameters of this process are chosen so that the implied conditional distributions for the marginal product of capital can be ordered in terms of first- and second-order...

This paper introduces a new algorithm, the recursive upwind Gauss-Seidel method, and applies it to solve a standard stochastic growth model in which the technology shocks exhibit heteroskedasticity. This method exploits the fact that the equations defining equilibrium can be viewed as a set of...