KAY GIESECKE, Stanford University - Management Science & Engineering
Email: giesecke@stanford.edu
HOSSEIN KAKAVAND, Stanford University - Management Science & Engineering
Email: hossein@stanford.edu
SEYED MOUSAVI, Stanford University - Management Science & Engineering
Email: mousavi@stanford.edu

Point processes with stochastic intensities are ubiquitous in many application areas, including finance, insurance, reliability and queuing. They can be simulated from standard Poisson arrivals by time-scaling with the cumulative intensity, whose path is typically generated with a discretization method. However, discretization introduces bias into the simulation results. This paper proposes a method for the exact simulation of point processes with stochastic intensities. The method leads to unbiased estimators. It is illustrated for a point process whose intensity follows an affine jump-diffusion process.

S. K. MISHRA, North-Eastern Hill University (NEHU)
Email: mishrasknehu@hotmail.com

The Two-Stage Least Squares (2-SLS) is a well known econometric technique used to estimate the parameters of a multi-equation (or simultaneous equations) econometric model when errors across the equations are not correlated and the equation(s) concerned is (are) over-identified or exactly identified. However, in presence of outliers in the data matrix, the classical 2-SLS has a very poor performance. In this study a method has been proposed to conveniently generalize the 2-SLS to the weighted 2-SLS (W2-SLS), which is robust to the effects of outliers and perturbations in the data matrix. Monte Carlo experiments have been conducted to demonstrate the performance of the proposed method. It has been found that robustness of the proposed method is not much destabilized by the magnitude of outliers, but it is sensitive to the number of outliers/perturbations in the data matrix. The breakdown point of the method is quite high, somewhere between 45 to 50 percent of the number of points in the data matrix.

COLOMBO GIULIA, Centre for European Economic Research (ZEW)
Email: colombo@zew.de

In the paper we describe in detail how to build linked CGE-microsimulation models (using fictitious data) following three main approaches: one in accordance with the fully integrated approach and the other two according to the layered approach - the so-called Top-Down and Top-Down/Bottom-Up approaches. After this, we implement the same policy reform in each of the three models. Results show that all three approaches yield different results especially in terms of income distribution and poverty, although analysed within the same economy and under the same policy simulation. We then analyse in more detail the TD/BU approach as developed by Savard (2003) and, in order to avoid possible deviations due to data inconsistencies, we propose an alternative way of taking into account feedback effects from the micro level of analysis into the CGE model.