the first to develop a united framework for the three problems (interpolation, extrapolation and distribution) of …-sectional and panel data and compares the classical and Bayesian estimation methods. We outline the error covariance structure in a … spatial context and derive the BLUE for the ML and Bayesian MCMC estimation. Finally, we apply the procedure to Spanish …

transitional probability density functions (PDFs). As a result, a large number of competing estimation approaches have been … estimation method are introduced and explored in the context of estimating the parameters of the popular Cox-Ingersoll-Ross and …

) and Barraquand and Pudet (1996), among others. In certain cases, interpolation is required because the number of possible … interpolation scheme is selected in appropriately. We concentrate on Asian options, due to their popularity and because of some …

intra-annual fiscal information for interpolation purposes allows us to capture genuine intra-annual "fiscal" dynamics in …

$ is a sufficiently smooth test function. Our methods are based on Malliavin calculus, on interpolation techniques and on …

the first to develop a unified framework for the three problems (interpolation, extrapolation and distribution) of …-sectional and panel data and compares the classical and Bayesian estimation methods. We outline the error covariance structure in a … spatial context and derive the BLUE for the ML and Bayesian MCMC estimation. Finally, we apply the procedure to Spanish …

related commodities; and linear interpolation, which uses the last and next observations for the item to linearly interpolate …. Certain hybrid techniques, combining either carry-forward or cell-mean with linear interpolation, are also considered. Our … the price index: (3) linear interpolation results in less fluctuation of prices than the true series: (4) combining either …