К содержимому
learnspaceYOUR NEXT CHAPTER
ПРОСТРАНСТВО ОБУЧЕНИЯ
ГлавнаяКаталог курсовМоё обучениеCoursera

Знания без границ

Учитесь у лучших университетов и компаний мира.

Открыть Coursera
Интеграция
Пространство университета
Моё пространствоСтраница курса
↵
ЯЛичный кабинетСтудент
© 2026 LearnSpaceКаждый день — возможность узнать больше.Помощь
Econometrics: Methods and Applications · LearnSpace
Назад в каталог
courseraАнализ данных

Econometrics: Methods and Applications

Курс от Erasmus University Rotterdam
Уровень не указан≈ 66.6 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Welcome! Do you wish to know how to analyze and solve business and economic questions with data analysis tools? Then Econometrics by Erasmus University Rotterdam is the right course for you, as you learn how to translate data into models to make forecasts and to support decision making. * What do I learn? When you know econometrics, you are able to translate data into models to make forecasts and to support decision making in a wide variety of fields, ranging from macroeconomics to finance and marketing. Our course starts with introductory lectures on simple and multiple regression, followed by topics of special interest to deal with model specification, endogenous variables, binary choice data, and time series data. You learn these key topics in econometrics by watching the videos with in-video quizzes and by making post-video training exercises. * Do I need prior knowledge? The course is suitable for (advanced undergraduate) students in economics, finance, business, engineering, and data analysis, as well as for those who work in these fields. The course requires some basics of matrices, probability, and statistics, which are reviewed in the Building Blocks module. If you are searching for a MOOC on econometrics of a more introductory nature that needs less background in mathematics, you may be interested in the Coursera course “Enjoyable Econometrics” that is also from Erasmus University Rotterdam. * What literature can I consult to support my studies? You can follow the MOOC without studying additional sources. Further reading of the discussed topics (including the Building Blocks) is provided in the textbook that we wrote and on which the MOOC is based: Econometric Methods with Applications in Business and Economics, Oxford University Press. The connection between the MOOC modules and the book chapters is shown in the Course Guide – Further Information – How can I continue my studies. * Will there be teaching assistants active to guide me through the course? Staff and PhD students of our Econometric Institute will provide guidance in January and February of each year. In other periods, we provide only elementary guidance. We always advise you to connect with fellow learners of this course to discuss topics and exercises. * How will I get a certificate? To gain the certificate of this course, you are asked to make six Test Exercises (one per module) and a Case Project. Further, you perform peer-reviewing activities of the work of three of your fellow learners of this MOOC. You gain the certificate if you pass all seven assignments. Have a nice journey into the world of Econometrics! The Econometrics team

Навыки, которые вы освоите

Case StudiesEconometricsForecastingLinear AlgebraLogistic RegressionStatistical AnalysisAnalyticsStatistical ModelingRegression AnalysisEstimationEconomicsStatisticsTime Series Analysis and ForecastingData AnalysisModel EvaluationProbability & StatisticsProbabilityTrend AnalysisStatistical Hypothesis TestingStatistical Methods

Программа курса

9 модулей · 134 учебных материалов

01Welcome Module4 материалов

Welcome to the fascinating world of Econometrics!

Welcome to our MOOC on EconometricsВидеоAbout this courseВидеоCourse Guide - Structure of the MOOCЧтениеCourse Guide - Further informationЧтение
02Simple Regression

Учитесь у экспертов

Francine Gresnigt

PhD candidate

Dennis Fok

Prof. Dr.

Michel van der Wel

Dr.

Erik Kole

Dr.

Markus Mueller

MSc.

Christiaan Heij

Dr.

Wendun Wang

Dr.

Richard Paap

Prof. Dr.

Dick van Dijk

Prof. Dr.

Philip Hans Franses

Prof. Dr.

Myrthe van Dieijen

PhD candidate

Econometrics: Methods and Applications
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 66.6 ч

9 модулей

Язык: Английский

Субтитры: Арабский, Французский, Бенгальский, Украинский, Европейский португальский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Немецкий, Пушту, Урду, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Дари, Хинди, Японский, Казахский, Венгерский, Польский

Часть программы вашего университета
17 материалов

Dataset for Lectures on Simple Regression

Dataset Simple RegressionЧтение

1.1 Simple Regression: Motivation

Lecture 1.1 on Simple Regression: MotivationВидеоTraining Exercise 1.1ЧтениеSolution Training Exercise 1.1Чтение

1.2 Simple Regression: Representation

Lecture 1.2 on Simple Regression: RepresentationВидеоTraining Exercise 1.2ЧтениеSolution Training Exercise 1.2Чтение

1.3 Simple Regression: Estimation

Lecture 1.3 on Simple Regression: EstimationВидеоTraining Exercise 1.3ЧтениеSolution Training Exercise 1.3Чтение

1.4 Simple Regression: Evaluation

Lecture 1.4 on Simple Regression: EvaluationВидеоTraining Exercise 1.4ЧтениеSolution Training Exercise 1.4Чтение

1.5 Simple Regression: Application

Lecture 1.5 on Simple Regression: ApplicationВидеоTraining Exercise 1.5ЧтениеSolution Training Exercise 1.5Чтение

Test Exercise 1

Test Exercise 1Взаимная проверка
03Multiple Regression20 материалов

Dataset for Lectures on Multiple Regression

Dataset Multiple RegressionЧтение

2.1 Multiple Regression: Motivation

Lecture 2.1 on Multiple Regression: MotivationВидеоTraining Exercise 2.1ЧтениеSolution Training Exercise 2.1Чтение

2.2 Multiple Regression: Representation

Lecture 2.2 on Multiple Regression: RepresentationВидеоTraining Exercise 2.2ЧтениеSolution Training Exercise 2.2Чтение

2.3 Multiple Regression: Estimation

Lecture 2.3 on Multiple Regression: EstimationВидеоTraining Exercise 2.3ЧтениеSolution Training Exercise 2.3Чтение

2.4 Multiple Regression: Evaluation

Lecture 2.4.1 on Multiple Regression: Evaluation - Statistical PropertiesВидеоTraining Exercise 2.4.1ЧтениеSolution Training Exercise 2.4.1ЧтениеLecture 2.4.2 on Multiple Regression: Evaluation - Statistical TestsВидеоTraining Exercise 2.4.2ЧтениеSolution Training Exercise 2.4.2Чтение

2.5 Multiple Regression: Application

Lecture 2.5 on Multiple Regression: ApplicationВидеоTraining Exercise 2.5ЧтениеSolution Training Exercise 2.5Чтение

Test Exercise 2

Test Exercise 2Взаимная проверка
04Model Specification17 материалов

Dataset for Lectures on Model Specification

Dataset Model SpecificationЧтение

3.1 Model Specification: Motivation

Lecture 3.1 on Model Specification: MotivationВидеоTraining Exercise 3.1ЧтениеSolution Training Exercise 3.1Чтение

3.2 Model Specification: Specification

Lecture 3.2 on Model Specification: SpecificationВидеоTraining Exercise 3.2ЧтениеSolution Training Exercise 3.2Чтение

3.3 Model Specification: Transformation

Lecture 3.3 on Model Specification: TransformationВидеоTraining Exercise 3.3ЧтениеSolution Training Exercise 3.3Чтение

3.4 Model Specification: Evaluation

Lecture 3.4 on Model Specification: EvaluationВидеоTraining Exercise 3.4ЧтениеSolution Training Exercise 3.4Чтение

3.5 Model Specification: Application

Lecture 3.5 on Model Specification: ApplicationВидеоTraining Exercise 3.5ЧтениеSolution Training Exercise 3.5Чтение

Test Exercise 3

Test Exercise 3Взаимная проверка
05Endogeneity17 материалов

Dataset for Lectures on Endogeneity

Dataset EndogeneityЧтение

4.1 Endogeneity: Motivation

Lecture 4.1 on Endogeneity: MotivationВидеоTraining Exercise 4.1ЧтениеSolution Training Exercise 4.1Чтение

4.2 Endogeneity: Consequences

Lecture 4.2 on Endogeneity: ConsequencesВидеоTraining Exercise 4.2ЧтениеSolution Training Exercise 4.2Чтение

4.3 Endogeneity: Estimation

Lecture 4.3 on Endogeneity: EstimationВидеоTraining Exercise 4.3ЧтениеSolution Training Exercise 4.3Чтение

4.4 Endogeneity: Testing

Lecture 4.4 on Endogeneity: TestingВидеоTraining Exercise 4.4ЧтениеSolution Training Exercise 4.4Чтение

4.5 Endogeneity: Application

Lecture 4.5 on Endogeneity: ApplicationВидеоTraining Exercise 4.5ЧтениеSolution Training Exercise 4.5Чтение

Test Exercise 4

Test Exercise 4Взаимная проверка
06Binary Choice18 материалов

Dataset for Lectures on Binary Choice

Dataset Binary ChoiceЧтение

5.1 Binary Choice: Motivation

Lecture 5.1 on Binary Choice: MotivationВидеоTraining Exercise 5.1ЧтениеSolution Training Exercise 5.1Чтение

5.2 Binary Choice: Representation

Lecture 5.2 on Binary Choice: RepresentationВидеоTraining Exercise 5.2ЧтениеSolution Training Exercise 5.2Чтение

5.3 Binary Choice: Estimation

Lecture 5.3 on Binary Choice: EstimationВидеоTraining Exercise 5.3ЧтениеSolution Training Exercise 5.3Чтение

5.4 Binary Choice: Evaluation

Lecture 5.4 on Binary Choice: EvaluationВидеоTraining Exercise 5.4ЧтениеSolution Training Exercise 5.4Чтение

5.5 Binary Choice: Application

Dataset for Lecture 5.5 on Binary Choice: ApplicationЧтениеLecture 5.5 on Binary Choice: ApplicationВидеоTraining Exercise 5.5ЧтениеSolution Training Exercise 5.5Чтение

Test Exercise 5

Test Exercise 5Взаимная проверка
07Time Series17 материалов

Dataset for Lectures on Time Series

Dataset Time SeriesЧтение

6.1 Time Series: Motivation

Lecture 6.1 on Time Series: MotivationВидеоTraining Exercise 6.1ЧтениеSolution Training Exercise 6.1Чтение

6.2 Time Series: Representation

Lecture 6.2 on Time Series: RepresentationВидеоTraining Exercise 6.2ЧтениеSolution Training Exercise 6.2Чтение

6.3 Time Series: Specification and Estimation

Lecture 6.3 on Time Series: Specification and EstimationВидеоTraining Exercise 6.3ЧтениеSolution Training Exercise 6.3Чтение

6.4 Time Series: Evaluation and Illustration

Lecture 6.4 on Time Series: Evaluation and IllustrationВидеоTraining Exercise 6.4ЧтениеSolution Training Exercise 6.4Чтение

6.5 Time Series: Application

Lecture 6.5 on Time Series: ApplicationВидеоTraining Exercise 6.5ЧтениеSolution Training Exercise 6.5Чтение

Test Exercise 6

Test Exercise 6Взаимная проверка
08Case Project1 материалов

Case

Case ProjectВзаимная проверка
09OPTIONAL: Building Blocks23 материалов

Building Blocks: Overview of Topics

StructureЧтение

Matrices

Lecture M.1: Introduction to Vectors and MatricesВидеоTraining Exercise M.1ЧтениеSolution Training Exercise M.1ЧтениеLecture M.2: Special Matrix OperationsВидеоTraining Exercise M.2ЧтениеSolution Training Exercise M.2ЧтениеLecture M.3: Vectors and DifferentiationВидеоTraining Exercise M.3ЧтениеSolution Training Exercise M.3Чтение

Probability

Lecture P.1: Random VariablesВидеоTraining Exercise P.1ЧтениеSolution Training Exercise P.1ЧтениеLecture P.2: Probability DistributionsВидеоTraining Exercise P.2ЧтениеSolution Training Exercise P.2Чтение

Statistics

Dataset for Lecture S.1 on Parameter EstimationЧтениеLecture S.1: Parameter EstimationВидеоTraining Exercise S.1ЧтениеSolution Training Exercise S.1ЧтениеLecture S.2: Statistical TestingВидеоTraining Exercise S.2ЧтениеSolution Training Exercise S.2
Чтение