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5

There is now a Python version of the well known stargazer R package, which does exactly this. See also this related question: https://stackoverflow.com/q/35051673/2858145


3

I use all three programs. Python can do everything that R can do and R can do everything that Python does, but I must say R is superior to Python when it comes to the packages. For that reason for most econometric analysis I usually default to R. I find also producing nice standard statistics graphics with R easier (but for maps I prefer Python). However, ...


3

FE logit requires the idiosyncratic errors to be IID across $i$ and $t$, quite a strong assumption. Also the regressors should be strictly exogenous, but it's the same for linear FE models. In your application, the fact that FE logit wouldn't converge will make a good argument against FE logit, and will satisfy some referees but not all. An important ...


3

You can use the stargazer package (install with pip install stargazer). From https://github.com/mwburke/stargazer/blob/master/examples.ipynb: import pandas as pd from sklearn import datasets import statsmodels.api as sm from stargazer.stargazer import Stargazer from IPython.core.display import HTML diabetes = datasets.load_diabetes() df = pd.DataFrame(...


2

In panel regressions you have multiple dimensions and that is why also you have 3 different $R^2$. The within $R^2$ tells you how much variation within your panel variables is on average explained by your model. The between $R^2$, tells you how much variation between your panel variables is explained by the model, and overall $R^2$ gives you the combination ...


2

In this lecture it is stated at chapter 2.3 that "The Markov switching model and its variants discussed in the preceding sections are only suitable for stationary data". Perhaps you'd like to read into that more thoroughly. Possible solution: Since differentiation is allowed, you should also be able to produce an Error-Correcting Model -and its ...


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I would advise you to think deeper about your research question first, as this will guide the decision to use country fixed effects. If you would like to exploit cross country variation, for example by studying how the same industry functions differently across countries, then do not use country dummies because it will absorb the variation you want. If you ...


1

I'd rate my experience: Stata 9/10, Python 7/10, R 3/10 on a) There are many econometric approaches specific to a certain field for which packages have been developed for R and Stata, but not (yet) for Python. One example is the Heckman Selection approach in Labor Economics, which I had to do myself in Python. There are numerous other examples. on b) ...


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