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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/hypotheses-testing-in-econometric
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课程名称：经济计量学中的假设检验 课程概述： 在本课程中，您将学习在不确定的情况下使用经典线性回归模型所恢复的参数进行假设检验的合理性。您将： - 加深对普通最小二乘（OLS）估计量统计性质的理解，检验关键假设是否成立。 - 学习OLS估计量的一些良好统计性质，这些性质是进行假设检验、辅助理性决策的基础。 - 探讨零假设和备择假设的概念，同时研究在零假设下的统计量和分布，以及判断哪种假设更可能成立的规则。 - 了解如果经典线性回归模型的一些假设被违背，决策框架会发生什么变化，进一步探索诊断检验。 - 学习检测违规的步骤、对OLS估计量的影响，以及必须采用的解决这些问题的技术。 在开始本课程之前，您应该对一些基本统计学有一定了解，包括均值、方差、偏度和峰度。同时建议您已完成并理解本专业化的前一门课程：经典线性回归模型。 课程结束时，您将能够： - 解释什么是假设检验 - 解释为何OLS是一种合理的假设检验方法 - 对单一和多个假设进行假设检验 - 解释诊断检验的概念 - 使用R进行单一和多个假设的假设检验 - 识别和解决参数识别所引发的问题。 课程大纲： 第1部分：OLS方法的性质 本周我们将重点关注OLS方法作为假设检验基础的性质，包括线性、无偏性、高效性和一致性。 第2部分：假设检验 本周我们将探讨假设检验，重点关注t检验和F检验，以及假设检验所引发的问题。 第3部分：诊断检验 I 本周我们将讨论诊断检验，研究非线性、完全秩违背和与回归变量相关的错误。 第4部分：诊断检验 II 本周我们将继续进行诊断检验，考虑球形误差、异方差、自相关、随机回归变量以及错误的非正态性。
Part: 1
Title:Properties of the OLS Approach
Description:This week we are going to look at the properties of the OLS approach as a basis for the hypothesis testing, focussing on linearity, unbiasedness, efficiency and consistency.
Part: 2
Title:Hypothesis Testing
Description:This week we shall be exploring hypothesis testing, looking at the t-test and the F-test, and considering the problems raised by hypothesis testing.
Part: 3
Title:Diagnostic Testing I
Description:This week we shall be discussing diagnostic testing as we look at non-linearity, violation of full rank and errors correlated with regressors.
Part: 4
Title:Diagnostic Testing II
Description:This week we will continue to look at diagnostic testing as we consider spherical errors, heteroscedasticity, autocorrelation, Stochastic Regressors, and the non-normality of errors.
In this course, you will learn why it is rational to use the parameters recovered under the Classical Linear Regression Model for hypothesis testing in uncertain contexts. You will: – Develop your knowledge of the statistical properties of the OLS estimator as you see whether key assumptions work. – Learn that the OLS estimator has some desirable statistical properties, which are the basis of an approach for hypothesis testing to aid rational decision making. – Examine the concept of null hypothesis and alternative hypothesis, before exploring a statistic and a distribution under the null hypothesis, as well as a rule for deciding which hypothesis is more likely to hold true. – Discover what happens to the decision-making framework if some assumptions of the CLRM are violated, as you explore diagnostic testing. – Learn the steps involved to detect violations, the consequences upon the OLS estimator, and the techniques that must be adopted to address these problems. Before starting this course, it is expected that you have an understanding of some basic statistics, including mean, variance, skewness and kurtosis. It is also recommended that you have completed and understood the previous course in this Specialisation: The Classical Linear Regression model. By the end of this course, you will be able to: – Explain what hypothesis testing is – Explain why the OLS is a rational approach to hypothesis testing – Perform hypothesis testing for single and multiple hypothesis – Explain the idea of diagnostic testing – Perform hypothesis testing for single and multiple hypothesis with R – Identify and resolve problems raised by identification of parameters.
