随机扰动项

具有偏正态随机扰动项AR模型的统计推断

Statistical Inference for the AR Models with Skew Normal Distributions

经典线性回归模型的一个重要假设就是回归方程的随机扰动项ui，具有相同的方差，也称同方差性。

One of the important hypotheses of classical linear regression model is that the random disturbances have equal variance .

IO模型是静态的确定性模型，它提供了一个非常细致的部门分类结构；EC模型通常是动态的，包含随机扰动项。

IO model is a static deterministic model , which provides a very detailed sectoral classification structure . Econometric ( EC ) model is usually dynamic , and it contains a stochastic component .

困扰计量经济建模的一个重要问题是模型中随机扰动项性质的设定。

The specification of random error term is a complex problem in econometric modeling .

传统计量模型中均假定随机扰动项为高斯白噪声，而实际经济背景和数据经常不支持这一假定。

It is assumed that the error term is Gaussian white noise in classical econometric model , which does not been supported in reality and economical data .

该方法以传统横截面回归模型作为理论基础，假定模型的随机扰动项彼此独立且和解释变量不相关。

The regression method is based on the linear regression theory which assumes that the random disturbances are independent and are not correlated with the explanatory variable .

与经典的随机稳定性结论相比，本文所建立的判据充分利用了随机扰动项的作用，无须LV（扩散算子）的负定。

Compared with the classical stochastic stability results , the proposed stability criteria make the best use of the effects of stochastic disturbed term in stochastic systems and cancel the requirement of the negative definite of LV ( diffusion operator ) .