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export BLAS=/home/skipper/.local/lib/libopenblas.a
export LAPACK=/home/skipper/.local/lib/libopenblas.a
pip install numpy
pip install scipy
pip install nose
pip install patsy
pip install Cython
pip install pandas

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% stochastic independence

\newcommand\independent{\protect\mathpalette{\protect\independenT}{\perp}}
\def\independenT#1#2{\mathrel{\rlap{$#1#2$}\mkern2mu{#1#2}}}

% conditional probability pipe

\mid

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library(fpp)
data(usmelec)
write.csv(as.data.frame(list(date=as.Date(usmelec), usmelec=coredata(usmelec))), "usmelec.csv", row.names=FALSE)

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Create the chroot

sudo apt-get install debootstrap schroot
CHROOT=/tmp/saucy-i386; mkdir -p $CHROOT; sudo debootstrap --arch=i386 --include=python-pandas,sudo,wget --components=main,universe,multiverse saucy $CHROOT http://debian.lcs.mit.edu/ubuntu

echo -e "[saucy-i386]\ndescription=Ubuntu saucy  i386 architecture\ndirectory=$CHROOT\ntype=directory\nusers=$USER\nroot-groups=root\n" | sudo bash -c 'cat - > /etc/schroot/chroot.d/saucy-i386'

Enter the chroot

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"""
Frisch-Waugh-Lovell Theorem.

In a multiple regression, the effect of any single covariate can be obtained 
by removing the effects of the other covariates from both the dependent and 
independent variables.
"""
import numpy as np
import statsmodels.api as sm

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class MonkeyOpen(pysal.IOHandlers.gal.GalIO):
    FORMATS = ['gal']
    MODES = ['r', 'w']
    def __init__(self, *args, **kwargs):
        self._typ = str
        pysal.core.FileIO.FileIO.__init__(self, *args, **kwargs)
        self.file = urlopen(self.dataPath)

    @staticmethod
    def getType(*args, **kwargs):

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def wide_to_long(df, stubnames, i, j):
    """
    User-friendly wide panel to long format.

    Parameters
    ----------
    df : DataFrame
        The wide-format DataFrame
    stubnames : list
        A list of stub names. The wide format variables are assumed to

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CUSIP,ARDSSIC,SCISECT,LOGK,SUMPAT,LOGR70,LOGR71,LOGR72,LOGR73,LOGR74,LOGR75,LOGR76,LOGR77,LOGR78,LOGR79,PAT70,PAT71,PAT72,PAT73,PAT74,PAT75,PAT76,PAT77,PAT78,PAT79
800,15,0,6.08360,354.00000,0.99684,0.88311,0.94196,1.06678,1.02901,0.92327,1.02309,0.97240,1.09500,1.07624,30.00000,28.00000,22.00000,34.00000,31.00000,32.00000,41.00000,60.00000,57.00000,77.00000
1030,14,1,1.97492,13.00000,-0.45815,-0.21637,0.08434,-0.15087,-0.68464,-1.48519,-1.19495,-0.60968,-0.58082,-0.60915,3.00000,1.00000,2.00000,1.00000,2.00000,3.00000,2.00000,1.00000,1.00000,1.00000
2824,4,1,5.64899,493.00000,3.39054,3.40697,3.44199,3.52962,3.58542,3.67343,3.77871,3.82205,3.88021,3.90665,48.00000,43.00000,61.00000,63.00000,58.00000,49.00000,42.00000,63.00000,77.00000,80.00000
4644,13,0,0.68377,2.00000,0.54340,0.48454,0.58779,0.48840,0.53714,0.43436,0.33836,0.36561,0.43860,0.42459,1.00000,0.00000,0.00000,1.00000,0.00000,0.00000,0.00000,1.00000,0.00000,0.00000
7842,16,0,2.06433,12.00000,0.39511,-0.88895,-0.31471,-0.21752,-0.36157,-1.29773,-1.6

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import numpy as np
from scipy import special, stats
from statsmodels.base.model import GenericLikelihoodModel

def vuong_test(p1, p2):
    r"""
    Vuong-test for non-nested models.

    Parameters
    ----------

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set more off
capture program drop ZIP_FE_model

program define ZIP_FE_model
version 9.1

args todo b lnf
tempvar theta1 lambda last nonz w sln0 sln r0 r nb0 nb1 nb00 nb2 L2

local by "$MY_panel"