This paper discusses estimation methods for limited dependent variable (LDV) models that employ Monte Carlo simulation techniques to overcome computational problems in such models. These difficulties take the form of high dimensional integrals that need to be calculated repeatedly but cannot be...

In this paper, I explore ways of recapturing the efficiency property for estimators that rely on simulation. In particular, I show that this can be achieved by exploiting two-step maximum stimulated likelihood (SL) estimation methods that are familiar from classical applications. I also...

This study makes an empirical comparison of estimators for censored equations using Monte Carlo simulation. The underlying data generation process is rarely known in practice. From the viewpoint of regression, both ordinary censoring rule and sample selection rule are logical rules of censoring....

Performance-dependent options are financial derivatives whose payoff depends on the performance of one asset in comparison to a set of benchmark assets. This paper presents a novel approach to the valuation of general performance-dependent options. To this end, a multidimensional Black-Scholes...

The paper further develops, both from the theoretical and numerical points of view the analytical valuation of the American options, initiated by Geske and Johnson (1984) for the American put with no dividend. We present and prove closed form formulas for the value of the Bermudan put and call,...

In this paper, we consider a detailed decomposition method for limited dependent variable models. That means, we propose a method to decompose the differential in the (limited dependent) outcome variable between two groups into the contributions of the explanatory variables. We provide a...