Econometric analysis is a common way to answer the economic questions in research. and various software like "Stata", "Eviews", "R", "Python" and etc are used in this aim. hence, which source do you suggest to economic students to learn econometrics by software?
Your answer could help many students who want to start learning. so, I will appreciate you, if share your experience or share this question with other researchers and teachers.
I think some of the best books to learn econometric software are in the below link:
https://www.stata.com/bookstore/books-on-stata/
I think both "An Introduction to Modern Econometrics Using Stata" and "Using Stata for Principles of Econometrics" are good books for learning econometrics by state. I suggest online courses too.
I agree with Shahin. On the other hand, eviews is the best for beginners. No need of using commands like stata.
If you are interested in R, you can use:
https://www.econometrics-with-r.org/
The book is free online as well as the software
I think you should star giving them a good book like Johnston and DiNardo, and then get proficient with Stata, which is an excellent reference. You should definitely leave Python, Matlab/Octave, R for a second-stage, as they lack of all the theoretical implementation, explanations, and supporting documentation that dedicated software like Stata have.
Good luck!
Dear Shahin Behdarvand and colleagues,
Some pronouncements made above about R and Python induced me to share the following insights comparing the respective two languages - R and Python:
R and Python are state of the art programming languages oriented towards data science and are both open-source programming languages with quite a large community. New libraries or tools are added continuously to their respective catalogues – where R is largely used for statistical analysis while Python provides a more general approach to data science - eg. machine learning.
Learning either one of them can be a matter of choice depending the scope of tasks the analyst has to execute and accomplish – while learning both of them can be the ideal solution.
Python is a general-purpose language with a readable syntax, while R is built by statisticians and encompasses their specific language. The prime objective of R is Data & Statistical Analysis while the main objective of Python is Deployment & Production. In that regard, R users mainly consists of scholars and R & D professionals, while Python users are mostly programmers and developers. R provides more flexibility to use available libraries while Python provides flexibility to construct new models from scratch. R consists various packages and libraries like tidyverse, ggplot2, caret, zoo, etc; while Python consists packages and libraries like pandas, scipy, scikit-learn, TensorFlow, caret, etc.
R has one of the richest ecosystems to perform data analysis, with more than
10 000 packages available in CRAN (open-source repository) and makes it possible to find a library for whatever the analysis one would want to perform. The rich variety of library makes R the first choice for statistical analysis, especially for specialized analytical work. In that regard, the cutting-edge difference between R and the other statistical products is the output.
Python can pretty much do the same tasks as R – such as data wrangling, engineering, feature selection, web scrapping, and so on. The Python language is used as a tool to deploy and implement machine learning at a large-scale, with codes that are easier to maintain and more robust than R. Although some years ago Python didn’t have many data analysis and machine learning libraries, recently it is catching up and provides cutting-edge API for machine learning or Artificial Intelligence. For example, most of the data science tasks can be done using five Python libraries: Numpy, Pandas, Scipy, Scikit-learn and Seaborn. In addition, Python, on the other hand, makes replicability and accessibility easier than R. If one needs to use the results of his/her analysis in an application or website, Python can be a best choice.
Best!
Dear Shahin.
I will recommend you start with Eviews.
You can Google on line to search packages for biginners.
Regards
Shahin Behdarvand, there are several books and online resources for undergraduate and graduate students. Please visit https://www.econometricsbooks.com/ for information on these.
Recomendo Eviews, pois é fácil de usar. O iniciante pode usar o Microsoft Excel como ferramenta de suporte também.
I agree with Mwoya Byaro. You may first use eviews through menu options and then you can keep up using some code step by step. you can go on by stata Gauss and so on.
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