The Application Of Mont Carlo Simulation In Researching The Properties Of LS Parametric Estimators

The least squares method which plays an important role in many subjects is a classic and widely used method of estimation. Problems about the least squares method are hot topics, and the theoretical results are endless. The Monte Carlo Simulation method can realize taking samples and statistical analysis with the help of computers and using methods of randomness solve the deterministic problems. Monte Carlo Simulation is a useful tool to solve issues with complex models or in the case that the model is difficult to establish. This simulation relies on extensive computation, so it is used in various fields with the development of computer technology. In this paper, applying Monte Carlo Simulation in an intuitive way to study the highly theoretical nature of the least squares estimator, it has a certain theoretical significance and application value.This paper takes the least squares estimators as research objects. By creating linear models that do not meet the classic assumptions, such as random errors with heteroscedasticity and serial correlation. Getting the least square estimators under different interferential conditions through Monte Carlo Simulation and simulating the distribution of the estimators. Then study the influence on estimators caused by interferential conditions and how the relative efficiency of least squares changes under different intensity interference conditions.The major innovation of this article is first in the choice of methods. Monte Carlo Simulation can be more effective in testing academic theories. It can also get visual interpretations of the least squares problems without complex mathematical theory. Second, under the conditions of getting the least squares estimators that from linear regression models with interferential conditions. It has discussed the influence on relative efficiency of least squares under interferential conditions in different intensity and types in an intuitive way.