Which program is the most accurate in studying time series, R, Python, Stata, or EViews?
I am not sure to understand what you mean by accurate.
But I would say EViews, which is a specialised program with several decades of existence: most if not all bugs should have been detected and corrected by now.
Same for Stata, which is less specialised (albeit if you master EViews programming, you can do lots of things: I know CGE models running on eEViews).
But they are very expensive if you have to pay the list-price.
R and Python are open sources.
I don’t know Python. Not focused on statistics, as far as I know (but I know little).
Lots of fancy time%series analysis options in R.
R is great, indeed, but, being open source, it is not fool-proof and authors (often you g researcher) may stop maintaining their programs after a while. It is recommended to use only programs from recognised libraries.
So, if accuracy and risk-avoidance are your key criteria, R may not be your choice.
Accuracy is the same for all the programs don't know what you means by this. I would suggest EViews if you only want to stick to the Time series. Stata and R are also good for this purpose if you have good command.
E-views is good for time series data , then Stata i see it good for cross sectional data
The accuracy of studying time series depends more on the models and methods used rather than the program itself. However, R and Python are often considered the most versatile and powerful for time series analysis due to their extensive libraries (such as `forecast` in R and `statsmodels` in Python) and active communities. Stata and EViews are also accurate and user-friendly, particularly in econometrics and financial applications.
Python is often considered the most versatile and accurate for studying time series due to its extensive libraries like Pandas, NumPy, and statsmodels, combined with its flexibility for advanced modeling. However, R also has strong capabilities with packages like forecast and TTR. The choice can depend on your specific needs and familiarity with the tools.
I will suggest R for it robustness in algorithms but must always make sure all the required packages are called and installed successfully.
The accuracy of time series analysis depends on the specific needs and expertise. R and Python offer extensive libraries and flexibility for advanced analysis, while Stata and EViews are user-friendly with specialized features for econometrics. R and Python are often preferred for their comprehensive packages and customization options.
R, Python, Stata, and EViews are very mature programs. Their implementations of most time series algorithms have the same accuracy.
Most statistical work is done in what is known as fixed precision which in rare cases may not be sufficient. R and Python have facilities for arbitrary precision arithmetic. Results can have any level of required precision. Stata works in double precision and uses quad precision when necessary. I can find no information about Eviews but it is probably limited to double precision. For most users, this is not important. For reference purposes, a list of arbitrary precision software is at
https://en.wikipedia.org/wiki/List_of_arbitrary-precision_arithmetic_software
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