Spatial-Temporal Rainfall Models Based On Poisson Cluster Processes

We fit stochastic spatial-temporal models to high-resolution rainfall radar data using Approximate Bayesian Computation (ABC). As a baseline we fit a model of Cox, Isham and Northrop, which we then generalise in a variety of ways. Of central importance is the use of ABC, as it is not possible to fit models of this complexity using previous approaches. We also introduce the use of Simulated Method of Moments (SMM) to initialise the ABC fit.

Do Job Destruction Shocks Matter in the Theory of Unemployment

Because the data show that market tightness is not orthogonal to unemployment, this paper identifies the many empirical difficulties caused by adopting the free entry of vacancies assumption in the Diamond-Mortensen-Pissarides (DMP) framework. Relaxing the free entry assumption and using Simulated Method of Moments (SMM) finds the vacancy creation process is less than infinitely elastic. Because a recession-leading job separation shock then causes vacancies to fall as unemployment increases, the ad hoc restriction to zero job separation shocks (to generate Beveridge curve dynamics) becomes redundant. In contrast to standard arguments, the calibrated model finds the job separation process drives unemployment volatility over the cycle. (JEL E24, E32, J24, J63, J64)

ESTIMATING PREFERENCE PARAMETERS FROM STOCK RETURNS USING SIMULATED METHOD OF MOMENTS

This study proposes a new way of solving standard dynamic problem based on Simulated Method of Moments (SMM) approach. It uses a newly introduced model of stock returns involving latent state variables and the regime-switching fundamentals and estimates three key preference parameters namely the Coefficient of Relative Risk Aversion, the Elasticity of Intertemporal Substitution and the subjective discount factor by suitably applying SMM and without directly using noisy consumption data. The estimates we found here seem to be relatively better than prevalent studies and very close to the true values of the parameters.