REGRESSION MODELS WITH STOCHASTIC REGRESSORS : AN EXPOSITORY NOTE

被引：0

作者：

Bharali, Sulaxana ^{[1
]}

Hazarika, Jiten ^{[1
]}

机构：

[1] Dibrugarh Univ, Dept Stat, Dibrugarh 786004, Assam, India

来源：

INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES | 2019年 / 15卷 / 02期

关键词：

Classical linear regression model; Modified maximum likelihood estimation; Non-normality; OLS estimator; AUTOREGRESSIVE MODELS; ESTIMATING PARAMETERS; ASYMPTOTIC PROPERTIES; NONNORMAL REGRESSION; STRONG CONSISTENCY; ALTERNATIVE TESTS; INDEPENDENCE; HYPOTHESIS; ERROR; ESTIMATORS;

D O I：

暂无

中图分类号：

S [农业科学];

学科分类号：

09 ;

摘要：

Regression models form the core of the discipline of econometrics. One of the basic assumptions of classical linear regression model is that the values of the explanatory variables are fixed in repeated sampling. However, in most of the real life cases, particularly in economics the assumption of fixed regressors is not always tenable. Under a non-experimental or uncontrolled environment, the dependent variable is often under the influence of explanatory variables that are stochastic in nature. There is a huge literature related to stochastic regressors in various aspects. In this paper, a historical perspective on some of the works related to stochastic regressor is being tried to pen down based on literature search.

引用

页码：873 / 880

页数：8

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