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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/enjoyable-econometrics
课程评论：没有评论
课程名称：愉快的计量经济学 课程概述：本MOOC的目标是展示计量经济学方法在回答问题时的重要性。首先提出问题，然后收集数据，最后才是模型或方法。根据数据的不同，有时需要调整方法。例如，最初关注两个变量，后来可能需要考虑三个或更多变量。此外，如果数据缺失，我们该如何处理？如果数据是计数形式，比如引用某人的报纸文章数量，情况也会有所不同。然而，这些修改通常是在相关时才考虑的。 创建此MOOC的重要动机是强调计量经济学模型和方法可以应用于更非传统的环境，尤其是在这些环境中，实践者需要首先收集自己的数据。这可以通过仔细结合现有数据库、进行调查或进行实验来实现。收集自己数据的副产品是，这有助于选择可用的潜在方法和技术。 如果您在寻找有关计量经济学的MOOC，且关注计量经济学的（数学和统计）方法及其应用，您也可以考虑来自鹿特丹伊拉斯姆斯大学的课程“计量经济学：方法与应用”。 课程大纲： 1. 名称：导言 描述：本周将介绍MOOC“愉快的计量经济学”。 2. 名称：基本统计 描述：本周涵盖基本统计，如分布、相关性和t检验。您将通过真实案例来了解这些概念。 3. 名称：简单回归 描述：本周的重点是简单回归，通过真实案例帮助您应用所学知识。本周结束时将进行测试，以检查您对前三周内容的掌握情况。 4. 名称：多重回归 描述：本周将重点介绍多重回归，通过实践中的例子为您讲解。 5. 名称：多重回归的变体 描述：本周您将遇到不同的多重回归变体，并通过实例进行说明。 6. 名称：新案例与新技术 描述：本MOOC的最后一周关注需要更高级计量经济学方法的真实案例。
Name:Introduction
Description:In this week you will be introduced to the MOOC Enjoyable Econometrics.
Name:Basic statistics
Description:This week covers basic statistics like distributions, correlations and t-tests. You will be introduced to these concepts through real-life examples.
Name:Simple regression
Description:Simple regression is the focus of this week, real-life examples will help you to apply your knowledge. This week ends with a test to check if you've mastered the content of the first three weeks.
Name:Multiple regression
Description:This week covers the topic of multiple regression, which is introduced to you through examples from practice.
Name:Variants of multiple regression
Description:In this week you will encounter different variants of multiple regression illustrated with examples.
Name:New cases with new techniques
Description:The last week of this MOOC focuses on real life cases that require more advanced econometric methods.
The goal of this MOOC is to show that econometric methods are often needed to answer questions. A question comes first, then data are to be collected, and then finally the model or method comes in. Depending on the data, however, it can happen that methods need to be adapted. For example, where we first look at two variables, later we may need to look at three or more. Or, when data are missing, what then do we do? And, if the data are counts, like the number of newspaper articles citing someone, then matters may change too. But these modifications always come last, and are considered only when relevant. An important motivation for me to make this MOOC is to emphasize that econometric models and methods can also be applied to more unconventional settings, which are typically settings where the practitioner has to collect his or her own data first. Such collection can be done by carefully combining existing databases, but also by holding surveys or running experiments. A byproduct of having to collect your own data is that this helps to choose amongst the potential methods and techniques that are around. If you are searching for a MOOC on econometrics that treats (mathematical and statistical) methods of econometrics and their applications, you may be interested in the Coursera course “Econometrics: Methods and Applications” that is also from Erasmus University Rotterdam.
