Abstract: When estimating in a practical situation, asymmetric loss functions are preferred over squared error loss functions, as the former is more appropriate than the latter in many estimation problems. This talk focus on fixed precision point estimation for two some what similar but interesting problems, one of which is that of forecasting the value of the dependent variable, while the other is that of estimating a linear parametric function in beta. Both the problems are considered for the multiple linear regression model using asymmetric loss functions. Due to the presence of nuissance parameters, the respective sample sizes for the estimation as well as the forecasting problems are not known beforehand and hence one has to take the recourse of adaptive multistage sampling methodologies into consideration. We discuss here some multistage sampling techniques and compare the performances of these methodologies, both for the forecasting as well as the estimation problem, using some interesting simulation runs. The implementation of the codes for our proposed models is accomplished utilizing MATLAB 7.0.1 program run on a Pentium IV machine. Finally, we also highlight the significance of such asymmetric loss functions with few practical examples.