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所在平台: Udemy |
课程主页: https://www.udemy.com/course/econometrics-basics-to-advance/
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课程名称：经济计量学从A到Z：理论、模型、函数和数据 课程概述： 本课程旨在为学生、专业人士以及对经济计量学感兴趣的人员提供一个简明易懂的介绍。课程内容涵盖回归分析、概率理论和时间序列建模等概念，通过27小时的易于理解的讲座和100多个可下载的PDF资源，让学生能够轻松掌握经济计量学的基本知识。 课程内容包括： 第一章：具有一个解释变量的最小二乘法代数 这一章节介绍了最小二乘法如何拟合散点图中的直线，重点讨论了最小二乘法的代数，不涉及概率理论或统计学。 第二章：概率理论入门 本章介绍了观察到的偏差背后的概率理论，讨论了随机变量、分布函数、期望值、方差和协方差等基础概念。 第三章：具有一个解释变量的线性回归模型 本章正式介绍线性回归模型，并讨论关键假设，如外生性，以及OLS公式在此模型中的应用。 第四章：具有多个解释变量的回归模型 这一章节扩展了前一章的内容，允许多个解释变量的存在，并讨论了非线性回归模型和虚拟变量。 第五章：时间序列数据 本章介绍了时间序列经济计量学，探讨了平稳和非平稳时间序列数据的相关问题。 第六章：内生性与工具变量 本章讨论了解释变量内生性的情况及其对经济计量分析的影响，并引入了工具变量的使用。 第七章：二元选择模型 本章介绍二元选择模型，探讨如何使用线性概率模型及其问题，并介绍更为复杂的Probit和Logit模型。 第八章：非平稳时间序列模型 本章分析了非平稳数据的使用及其后果。 第九章：面板数据 这一章节是对面板数据模型的入门，重点介绍了固定效应估计和随机效应模型。 课程由瑞典隆德大学的著名经济计量学讲师彼得·约霍姆岑（Peter Jochumzen）授课，适合对经济计量学感兴趣的人士。
Introduction to EconometricsWhether you're a student, a professional, or just intellectually curious, this course breaks down intimidating concepts like regression analysis, probability theory, and time series modeling into bite-sized, easy-to-understand lessons. This course will cover material comparable to a typical first course in econometrics. It includes 27 hours of 100 easy-to-understand lectures with over 100 downloadable PDF resources accompanying the lectures. This course will cover:Chapter 1: The algebra of least squares with one explanatory variableThis chapter introduces the least squares method which is used to fit a straight line through a scatter plot. This chapter focuses on the algebra of least squares. There is no probability theory or statistics in this chapter. The chapter begins with sample moments, goes through and derives the OLS formula. Important concepts introduced in this chapter: Trendline, residuals, fitted values and R-squared. In addition to Excel, we will also introduce EViews in this chapter and look at how to find trendlines using Excel and EViews.Chapter 2: Introduction to probability theoryIn order to make more sense of the concepts introduced in chapter 1, we need some probability theory and statistics. We want to be able to explain observed deviations from the trendline and we will do that with random variables called error terms. This chapter covers the absolute minimum from probability theory: random variables, distribution functions, expected value, variance and covariance. This chapter also introduces conditional moments which will turn out to be of great importance in econometrics as the fundamental assumption on the error terms will be stated as a conditional expectation.Chapter 3: The linear regression model with one explanatory variableThis chapter formalizes the most important model in econometrics, the linear regression model. The entire chapter is restricted to a special case, nameley when you have only one explanatory variable. The key assumtion of the linear regression model, exogeneity is introduced. Then, the OLS formula from chapter 1 is reinterpreted as an estimator of unknown parameters in the linear regression model. This chapter also introduces the variance of the OLS estimator under an important set of assumptions, the Gauss-Markov assumptions. The chapter concludes with inference in the linear regression model, specifically discussing hypothesis testing and confidence intervals.Chapter 4: The regression model with several explanatory variableThis chapter is an extension of chapter 3 allowing for several explanatory variables. First, the linear regression with several explanatory variables, the focus of this chapter, is thoroughly introduced and an extension of the OLS formula is discussed. Since we are not using matrix algebra in this course, we will not be able to present the general formulas such as the OLS formula. Instead, we rely on the fact that they have been correctly programmed into software such as Excel, EVies, Stata and more. We need to make small changes to the inference of this model and we will also introduce some new tests. A new problem that will appear in this model is that of multicolinearity. Next, we look at some nonlinear regression models followed by dummy variables. This chapter is concluded with an anlysis of the data problem heteroscedasticity.Chapter 5: Time series dataThis chapter is an introduction to econometrics with time series data. Chapters 1 to 4 have been restricted to cross sectional data, data for individuals, firms, countries and so on. Working with time series data will introduce new problems, the first and most important being that time series data may be nonstationary which may lead to spurios (misleading) results. However, this chapter will only look at stationary time series data. Time series models may be static or dynamic, where the latter maeans that the dependent variable may depend on values from previous periods. We will look at some dynamic models, most importantly ADL (autoregressive distributed lag) models and AR (autoregressive) models. Another problem with time series data is that the error terms may be correlated over time (autocorrelation). The chapter concludes with a discussion of autocorrelation, how to test for autocorrelation and how to estimate models in the presence of autocorrelation.Chapter 6: Endogeneity and instrumental variablesThroughout the course so far, we have assumed that the explanatory variables are exogenous. This is the most critical assumption in econometrics. In this chapter we will look at cases when explanatory variables cannot be expected to be exogenous (we then say that they are endogenous). We will also look at the consequence of econometric analysis with endogenous variables. Specifically, we will look at misspecification of our model, errors in variables and the simultaneity problem. When we have endogenous variables, we can sometimes find instruments for them, variables which are correlated with our endogenous variable but not with the error term. This opens for the possibility of consistently estimate the parameters in our model using the instrumental variable estimator and the generalized instrumental variable estimator.Chapter 7: Binary choice modelsThis chapter is an introduction to microeconometric models. We will look at the simplest of these types of models, the binary choice model, a model where your dependent variable is a dummy variable. It turns out that we can use the same methods described in chapter 4, the model is then called the linear probability model. However, the linear probability model has some problems. For example, predict probabilities may be less than zero and/or larger than 100%. In order to rectify this problem, new models are presented (the probit- and the logit model) and a new technique for estimating these models is introduced (maximum likelihood).Chapter 8: Non-stationary time series modelsIn chapter 5 working with time series data, stationarity was a critical assumption. In this chapter we investigate data that is not stationary, the consequences of using non-stationary data and how to determine if your data is stationary.Chapter 9: Panel dataPanel data is data over cross-section as well as time. This chapter is only an introduction to models using panel data. The focus of this chapter is on the error component model where we look at the fixed effect estimator as well as the random effects model. The chapter concludes with a discussion of how to choice between s and how to choice between the fixed effect estimator and the random effects estimator (including the Hausman test).The lectures are provided by renowned econometrics lecturer, Peter Jochumzen from Lund University.
