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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/the-classical-linear-regression-model
课程评论：没有评论
课程总结：经典线性回归模型 在这门Coursera课程中，您将探索计量经济学能够回答的各种问题，以及可能使用的不同类型的数据，包括时间序列数据、横截面数据和纵向数据。 课程将包含以下内容： - 学习使用经典线性回归模型（CLRM）以及普通最小二乘法（OLS）估计器，并讨论OLS产生真实回归参数所需的假设。 - 从单一自变量与因变量的回归分析入手，逐步过渡到多元回归分析，将二元模型推广至多元模型。 - 探索不同的建模理念，特别是从一般到具体的思路，并学习如何使用拟合优度统计量来衡量模型对因变量变异的解释能力。 课程中将通过实例帮助您理解感兴趣的关系，并解释如何解读这些关系。同时，您将有机会应用所学知识，使用R语言对资本资产定价模型（CAPM）进行实证估算。 该课程适合初学者，要求的前置知识较少，但具备在xy坐标系中绘制两个变量图形的能力、基础代数及求导知识将对您有帮助。尽管不要求掌握矩阵代数，但了解它将带来额外优势。 课程结束时，您将能够： - 描述计量经济学所能解决的问题及应使用的数据类型。 - 解释为何某些假设对于该方法产生估计是必要的。 - 计算经典线性回归模型中感兴趣的系数。 - 解读估计参数及拟合优度统计量。 - 使用R进行单变量和多变量线性回归模型的估算。 课程大纲包括： 1. 计量经济学的目的和用途：探讨计量经济学在经济学和金融专业中的应用以及可以解决的问题，关注单回归模型。 2. 经典线性回归模型：研究OLS方法的假设，讨论多重线性回归模型和线性代数的重要性。 3. OLS参数的解释：讨论OLS参数的解读，拟合优度统计量（R平方及调整R平方）及CAPM的初步结果。 4. 资本资产定价模型：使用R进行CAPM的计算与解读，同时讨论模型扩展，采用Fama和French（1993）三因素模型。 通过这门课程，您将具备建立和解释线性回归模型的基本能力，为日后的研究和实践奠定坚实基础。
Name:Aims and Uses of Econometrics
Description:Welcome to Coursera and Queen Mary University of London, we are excited to have you studying with us. We are going to help you prepare for your studies by ensuring you know exactly what is expected of you throughout your course and how to most effectively engage with the platform. We will look at how the platform works as well as how you will interact with your peers. You will be introduced to the university you are studying with and we will share some top tips on how to succeed with Coursera. This week we shall start by getting to know Coursera as you will be introduced to the platform and explore how to use the various functions which will support your learning journey. You will see how you can make the most of your learning experience which will enable you to succeed on this course. This week we are going to explore the aims and uses of econometrics for economists and finance professionals and consider some of the questions that econometrics can address. We will also look at the types of data we can work with, and discuss the transformation and manipulation of this data. This week will be focussing on the single regression model.
Name:The Classical Linear Regression Model
Description:This week we shall be focussing on the Classical Linear Regression Model as well as the classical linear regression model. We will explore the assumptions of the OLS approach and see why we need those assumptions. We shall also discuss the Multiple Linear Regression Model and consider why we use linear algebra.
Name:Interpretation of the Ordinary Least Squares Parameters
Description:This week we are going to discuss the interpretation of the Ordinary Least Squares parameters as well as the goodness of fit statistics: R-squared and the adjusted R-squared. We will also consider some CAPM introductory results, model building and determinants of bus driving in the USA.
Name:Capital Asset Pricing Model
Description:This week we are going to focus on a real example of estimating and interpreting the Capital Asset Pricing Model with R. We are also going to look at data description, manipulation, estimations of the CAPM and interpretations of the estimated parameters. We shall discuss expanding the model using the three factors Fama and French (1993) model.
In this course, you will discover the type of questions that econometrics can answer, and the different types of data you might use: time series, cross-sectional, and longitudinal data. During the course you will: – Learn to use the Classical Linear Regression Model (CLRM) as well as the Ordinary Least Squares (OLS) estimator, as you discuss the assumptions needed for the OLS to deliver true regression parameters. – Look at cases with only one independent variable for one dependent variable, before progressing to regression analysis by generalising the bivariate model to multiple regression. – Explore different model-building philosophies, with particular focus on the general-to-specific approach, and learn how to use goodness-of-fit statistics as the measures of “how well your model explains variations in the dependent variable”. Throughout this course, you will see examples to help clarify which kind of relationship is of interest, and how we can interpret it. You will also have the opportunity to apply your learning to estimating the Capital Asset Pricing Model using real data with R. The course is for beginners, so little prior knowledge is required, but you will benefit from an ability to graph two variables in the xy framework, an understanding of basic algebra and taking derivatives. Knowledge of matrix algebra is not a requirement but will also provide you with an advantage. By the end of this course, you will be able to: – Describe the problems that econometrics can help addressing and the type of data that should be used – Explain why some hypotheses are needed for the approach to produce an estimate – Calculate the coefficients of interest in the classical linear regression model – Interpret the estimated parameters and goodness of fit statistics – Estimate single and multiple linear regression models with R.
