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When IвЂ™m feeling too lazy to read the requirements for a package itвЂ™s not unusual to go through this cycle a few times.

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Usually itвЂ™s a missing library or header file and you can install the, e.g., foo and foo-devel packages and try to run. You can usually tell which command or library is missing from the error message but it can be tricky to tell what to do to fix the problem. configure cannot find a compiler, a required library, etc.

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This is usually an enormous, automatically generated shell script which figures out how to call your compiler, where to find the libraries the software needs, where the new software should be installed, etc. configure command runs the configure script which is included in most software packages. configure make make test sudo make install You can do this by copying the URL in your web browser and using the wget command like so: Download the вЂњGzipped source tarballвЂќ to your server. Go to the Python source downloads and choose a version to download (IвЂ™ll use 3.5.1 throughout this document but the process should be similar for any other version). Test the software to make sure it works properly.Д¬onfigure our system to make sure we can use the software easily.

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Speaking of process, here is what weвЂ™ll be doing:Дownload the source code of an official Python release.Д¬onfigure the build appropriately for our machine. I donвЂ™t cover everything - mostly just the happy path of everything working - but if youвЂ™ve never installed from source before, I hope this will help guide you through the process. The example is Python 3.5.1 but the details apply to the vast majority of open source software. Others are the version that are being used in the test environment.This is an introduction to building and installing software from source on Linux (and other UNIX-like operating systems). With the exception of Python 3 (3.7+ tested), which is a hard requirement, the

Dynamic Panel model estimation - not started.

Linear IV Panel model estimation - not started.

Linear Panel model estimation - complete.

Linear Instrumental variable estimation - complete.

Currently only the single variable IV estimators are polished. Should eventually add some useful linear model estimators such as panel Is automatically built on every successful build of main.

The main branch can be installed by cloning the repo and running setup git clone The latest release can be installed using pip pip install linearmodels The expressions in the indicate endogenous regressors (before ~) from_formula ( 'np.log(wage) ~ 1 + exper + exper ** 2 + ', data ) import numpy as np from linearmodels.iv import IV2SLS from linearmodels.datasets import mroz data = mroz. IV regression models can be similarly specified. Package which is a replacement for patsy. The formula interface for PanelOLS supports the special valuesД®ntityEffects and TimeEffects which add entity (fixed) and time

fit ( cov_type = 'clustered', cluster_entity = True ) from_formula ( 'invest ~ value + capital + EntityEffects', data ) res = mod.

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from linearmodels import PanelOLS mod = PanelOLS. Models can also be specified using the formula interface. invest, data ], entity_effects = True ) res = mod. set_index () from linearmodels import PanelOLS mod = PanelOLS ( data. int64 ) # MultiIndex, entity - time data = data. Specified import numpy as np from statsmodels.datasets import grunfeld data = grunfeld. For example, the classic Grunfeld regression can be Panel modelsДЇormulas for specifying models.

Generalized Method of Moments (GMM) System EstimationДesigned to work equally well with NumPy, Pandas or xarray data.

Seemingly Unrelated Regression (SUR/SURE).

Generalized Method of Moments, also with continuously updating.

Fama-MacBeth estimation of panel models.

Instrumental variable estimators, system estimators and models for

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