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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/topics-in-applied-econometrics
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课程名称：应用计量经济学专题 概述：本课程旨在帮助学员掌握应对经验计量经济模型及特定类型数据所带来的挑战的模型和方法。课程内容包括： - 使用图表、初步统计数据和经济理论介绍每种方法的动机 - 讨论参数识别问题，并通过建模联立方程和经济学中的因果关系来解决此问题 - 检查面板数据的关键特征，强调使用面板数据与其他数据结构相比的优缺点 - 学习如何通过引入可组合性检验和豪斯曼检验选择合适的计量经济学规范 - 讨论用于处理定性变量的概率模型，采用不同的方法来解决问题 - 学习如何应用这些方法建立早期预警系统，以使用世界银行数据预测系统性银行危机 建议先完成本专业的前两门课程：《经典线性回归模型》和《计量经济学中的假设检验》。 课程结束时，学员将能够： - 针对数据特征提出适当的应对措施 - 解决识别和因果关系所带来的问题 - 处理联立方程和工具变量模型所提出的问题 - 解决纵向数据所产生的问题 - 处理概率模型所带来的问题 - 操作和绘制不同类型的数据 课程大纲： 第一部分：随机回归 该模块介绍应对随机变量回归带来的挑战的模型和方法，讨论自变量与误差项相关时会产生的问题，通过应用到教育回报的案例中讨论参数识别问题，并最终估算鱼类的供需模型。 第二部分：面板数据模型基础 描述样本中重复观察可能导致的问题，处理未观察到的异质性，分析面板数据的关键特征，以及面板数据相较于其他数据结构的优势，讨论固定效应模型及相关估计器，并使用PWT表数据对新古典增长模型进行估计和讨论，同时介绍可组合性检验的选择。 第三部分：面板数据模型的进一步分析 研究面板数据模型中的随机效应，通过豪斯曼检验比较采用固定效应模型或随机效应模型的适宜性，使用索洛增长模型进行比较，分析时间在面板数据模型中的作用，介绍时间效应的两种估计器，最后研究动态面板数据模型。 第四部分：概率模型 讨论用于处理定性变量的概率模型，分析线性模型在因变量为二项时面临的困难，学习logit和probit估计器，并将概率模型应用于建立早期预警系统，预测系统性银行危机。
Part: 1
Title:Random Regressors
Description:This module presents models and approaches that are designed to deal with challenges raised by the regressors being random variables. We address the problems raising when regressors are correlated with the error term, ad when this problem is likely to raise. We look at modelling simultaneous equations and discuss causality in economics, with an application to returns to schooling. We will discuss the problem of identification of the parameters, and how to address this problem. We finally estimate a model for the demand and supply of fish.
Part: 2
Title:Panel Data Models: The Basics
Description:We describe the problems as raising when repeated observations are present in the sample, and it is possible to deal with unobserved heterogeneity in the sample. We analyse key features of panel data and highlight the advantages of working with panels of data instead of other structures of data. We analyse fixed effects models and estimators associated with this approach. A full example where the neoclassical growth model is estimated is presented and discussed using the PWT tables data. We see how to choose between fixed effects models and pooled models by introducing the test for poolability.
Part: 3
Title:Further Analysis of Panel Data Models
Description:This week we study random effects on models of panel data. We analyse the Hausman test, that helps study whether we should be adopting the fixed effects models of the random effects models. The two approaches are compared using the Solow growth model. We also analyse the role of time in panel data models by presenting the between estimator, the two ways estimators, where we have time effects, and we finally look at dynamic panel data models, where the lagged dependent variable enters the set of regressors.
Part: 4
Title:Probability Models
Description:We discuss models for probability, that are used where the variable under investigation is qualitative, and needs to be treated with a different approach. We analyse the difficulties raised by linear models when the dependent variable is binomial. We study logit and probit estimators. We apply probability models to the problem of building an Early Warning system to forecast systemic banking crises using data from the World Bank.
In this course, you will discover models and approaches that are designed to deal with challenges raised by the empirical econometric modelling and particular types of data. You will: – Explore the motivations of each approach by means of graphs, preliminary statistics and presentation of economic theories – Discuss the problem of identification of the parameters, and how to address this problem by modelling simultaneous equations and causality in economics. – Examine the key features of panel data, and highlight the advantages and disadvantages of working with panel data rather than other structures of data. – Learn how to choose what econometric specification to adopt by introducing the test for poolability and the Hausman tests. – Discuss models for probability that are used where the variable under investigation is qualitative, and needs to be treated with a different approach. – Learn how to apply this approach to building an Early Warning system to forecast systemic banking crises using data from the World Bank. It is recommended that you have completed and understood the previous two courses in this Specialisation: The Classical Linear Regression Model and Hypothesis Testing in Econometrics. By the end of this course, you will be able to: – Respond appropriately to issues raised by some feature of the data – Resolve address problems raised by identification and causality – Resolve problems raised by simultaneous equation and instrumental variables models – Resolve problems raised by longitudinal data – Resolve problems raised by probability models – Manipulate and plot the different types of data.
