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Вернуться к Econometrics: Methods and Applications

Отзывы учащихся о курсе Econometrics: Methods and Applications от партнера Роттердамский университет им. Эразма

4.6
звезд
Оценки: 1,001
Рецензии: 214

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...

Лучшие рецензии

DT
13 февр. 2020 г.

Course was very well structured, pacing was very pleasant (albeit a little fast for the chapter about time series). Teachers were top notch! I had lots of fun while learning . Thank you!

JJ
15 нояб. 2015 г.

The design of the course is very Helpful and efficient. The course is well explained. The instructors are very clear and master the subject. They very detailed and well organized.

Фильтр по:

151–175 из 207 отзывов о курсе Econometrics: Methods and Applications

автор: Alizada T

12 апр. 2020 г.

Very good

автор: Daniela C

9 апр. 2018 г.

EXCELENTE

автор: Alejandro C

15 февр. 2017 г.

very good

автор: MD W H

9 авг. 2016 г.

Important

автор: Bilous V

3 нояб. 2015 г.

very good

автор: Aymen A

11 июля 2020 г.

perfect

автор: Jhon A A C

6 июня 2017 г.

Good.

автор: Saket B

25 июня 2017 г.

nice

автор: Ivan S

28 июля 2016 г.

cool

автор: Tran D B

1 авг. 2017 г.

d

автор: Sergio A G P

21 апр. 2020 г.

Overall, it was a good project. The use of real datasets made the learning experience so much rich.

Pros: it is very rigurous. If one pays enough attention, it will provide the necessary knowledge to perform robust analysis. Linear regressions ara a basic algortithm. Still, most of the time people do not know how to use them properly.

Cons: the explanations are poor. The slides are sometimes quite unclear. This course would benefit a lot if it had guided implementations. Also, something that I really disliked was that the solutions for the tests (which you use to review others in peer activities) were extremely simple and vague. It seems as if they were made carelessly. From test 1, the course should instill the importance of the analysis, rather than getting the calculations correct.

Having said so, the course is great. I do recommend it for enhancing the intuition for model specification and robust analysis.

автор: Taylor B

8 мая 2016 г.

This course is not for someone who hasn't taken much advanced math. There's a strong requirement of linear algebra, calculus, and probability. Someone who is relying only on the math prep they give you in the course will likely be very under-prepared for some of the more theoretical homework assignments.

With that disclaimer out of the way, this course gives a fairly good overview of important econometric techniques, though I wish they would have done more with time series analysis.

A major shortcoming of this course is some of the more complicated material (RESET test, Chow test, endogeneity, etc) were not presented in a complete way (in my opinion). I found myself referring to quite a few outside sources in order to figure out some of the more complicated material. Keep this in mind when taking the class and give yourself extra time to read farther into the concepts discussed in class.

автор: Arcangel M

20 янв. 2020 г.

The course is very dense in content and sometimes with many concepts that do not go deeper and are then required in the tests. In my opinion, the course should be divided into two courses, especially extending the part of Time Series and with a much more practical approach with real cases and with prediction models. So I encourage the people of this university to do a second part more focused on applying the concepts in real cases explaining everything in more detail. For the rest, the course has a high level and it is necessary to have previous statistical and mathematical knowledge.

автор: Josiah N

28 сент. 2016 г.

Good information, and detailed mathematical representation of the concepts, but often you will have to do outside research to truly understand the material, and the building blocks are not very helpful beyond a basic refresher course on matrices and statistics. This class requires a lot of studying and initiative to seek outside help to understand the material.

If more time was dedicated to truly explaining the concepts and principles and the REASONING behind them instead of just supplying equations and test names, this class would get a 5 star rating.

автор: Thomas B

25 авг. 2018 г.

Good content and quality. Coming from machine learning this gave me a new perspectives, e.g. a topic like endogeneity or the different kind of statistical tests.

I did the course using R, RStudio and R Markdown for the course assignments and that worked great. However, the course is taught without any reference to specific software packages and I think that's a big plus.

Some of the assignments were too academic for my taste (proving statements). I would have rather liked more examples showing different aspects and situations of the taught topics.

автор: Filippo A B

20 нояб. 2016 г.

Very interesting intermediate course in Econometrics. I strongly recommend for both new learners and those are interested in refreshing their knowledge in econometrics.

Maybe some blocks are too theoretical, there are some "bugs" in the content, and I miss a bit more of applied econometrics using statistical softwares.

Definetly a very good example of teaching econometrics through MOOC, my heartfelt congratulations to the Econometrics team of the Erasmus University for the great job, it has been a really valuable course to attend.

автор: Sovit S

18 мая 2020 г.

It is a fairly expansive course in terms of the topics covered. However, it is not as discursive as I'd have liked. The tests are good but would have been better had they not provided too many hints. It is definitely an eight-week course if you diligently work on the exercises. Maybe there is room to recommend additional readings for those who'd like to learn more.

автор: Maximiliano G

29 сент. 2016 г.

Un curso muy interesante, con mucho contenido que requiere un esfuerzo por parte de los alumnos y una base matemática/estadística sólida. Las prácticas están muy bien organizadas. Hay explicaciones que podrían mejorar. Sin embargo, cumple sobremanera mis expectativas. Lo recomiendo.

автор: ANTHONY E J

19 окт. 2020 г.

Having more explanation with the answer videos will lead to a better understanding from MOOC participants. Also, additional review and participation from class instructors on grading tests would ensure a more effective and accurate calculation of participant performance.

автор: Arthur M

10 апр. 2016 г.

Good content and exercise, very pedagogic.

The only problem are the use of the program: If you don't know how to use a statistical program such as R, you will spend more time struggling with the program than understanding the topic.

автор: Pradeep M

11 нояб. 2020 г.

This course provides inputs for advance level students. Theoretical aspects are well-covered. I gained a lot from this course. If some idea of any one econometric software is provided, it can further add value to the students.

автор: Tatsunari W

13 сент. 2017 г.

This course introduces basic and important statistical methods both in conceptual and practical ways. I wish this course were more longer and covered the topics deeper because I really enjoyed learning the topics.

автор: Cristian A T T

4 мая 2020 г.

This is course is very smooth yet challenging. It provides good grounds for econometric analysis at an intermediate level. I totally recommend it. Thanks for the good job to all the professors and assistants.

автор: Софья

22 февр. 2017 г.

Some topics that were covered in this course were not explained in details and, in addition, there was no explanation of the theoretical aspects of the course (for instance, formulas transcript ).

автор: Danish U

5 нояб. 2015 г.

Very good course. But too much emphasis on statistical derivations. Also estimating models by using any statistical software (SPSS, STATA, R, Eviews) will for sure be an interesting ad on.