GMM method for a spatial error model with regimes and endogenous variables, with
results and diagnostics; based on Kelejian and Prucha (1998,
1999) [Kelejian1998] [Kelejian1999].
Parameters: 
 y : array
nx1 array for dependent variable
 x : array
Two dimensional array with n rows and one column for each
independent (exogenous) variable, excluding the constant
 yend : array
Two dimensional array with n rows and one column for each
endogenous variable
 q : array
Two dimensional array with n rows and one column for each
external exogenous variable to use as instruments (note:
this should not contain any variables from x)
 regimes : list
List of n values with the mapping of each
observation to a regime. Assumed to be aligned with ‘x’.
 w : pysal W object
Spatial weights object
 constant_regi: [‘one’, ‘many’]
Switcher controlling the constant term setup. It may take
the following values:
 ‘one’: a vector of ones is appended to x and held
 constant across regimes
 ‘many’: a vector of ones is appended to x and considered
 different per regime (default)
 cols2regi : list, ‘all’
Argument indicating whether each
column of x should be considered as different per regime
or held constant across regimes (False).
If a list, k booleans indicating for each variable the
option (True if one per regime, False to be held constant).
If ‘all’ (default), all the variables vary by regime.
 regime_err_sep : boolean
If True, a separate regression is run for each regime.
 regime_lag_sep : boolean
Always False, kept for consistency, ignored.
 vm : boolean
If True, include variancecovariance matrix in summary
results
 cores : boolean
Specifies if multiprocessing is to be used
Default: no multiprocessing, cores = False
Note: Multiprocessing may not work on all platforms.
 name_y : string
Name of dependent variable for use in output
 name_x : list of strings
Names of independent variables for use in output
 name_yend : list of strings
Names of endogenous variables for use in output
 name_q : list of strings
Names of instruments for use in output
 name_w : string
Name of weights matrix for use in output
 name_ds : string
Name of dataset for use in output
 name_regimes : string
Name of regime variable for use in the output

Examples
We first need to import the needed modules, namely numpy to convert the
data we read into arrays that spreg
understands and pysal
to
perform all the analysis.
>>> import pysal.lib
>>> import numpy as np
Open data on NCOVR US County Homicides (3085 areas) using pysal.lib.io.open().
This is the DBF associated with the NAT shapefile. Note that
pysal.lib.io.open() also reads data in CSV format; since the actual class
requires data to be passed in as numpy arrays, the user can read their
data in using any method.
>>> db = pysal.lib.io.open(pysal.lib.examples.get_path("NAT.dbf"),'r')
Extract the HR90 column (homicide rates in 1990) from the DBF file and make it the
dependent variable for the regression. Note that PySAL requires this to be
an numpy array of shape (n, 1) as opposed to the also common shape of (n, )
that other packages accept.
>>> y_var = 'HR90'
>>> y = np.array([db.by_col(y_var)]).reshape(3085,1)
Extract UE90 (unemployment rate) and PS90 (population structure) vectors from
the DBF to be used as independent variables in the regression. Other variables
can be inserted by adding their names to x_var, such as x_var = [‘Var1’,’Var2’,’…]
Note that PySAL requires this to be an nxj numpy array, where j is the
number of independent variables (not including a constant). By default
this model adds a vector of ones to the independent variables passed in.
>>> x_var = ['PS90','UE90']
>>> x = np.array([db.by_col(name) for name in x_var]).T
For the endogenous models, we add the endogenous variable RD90 (resource deprivation)
and we decide to instrument for it with FP89 (families below poverty):
>>> yd_var = ['RD90']
>>> yend = np.array([db.by_col(name) for name in yd_var]).T
>>> q_var = ['FP89']
>>> q = np.array([db.by_col(name) for name in q_var]).T
The different regimes in this data are given according to the North and
South dummy (SOUTH).
>>> r_var = 'SOUTH'
>>> regimes = db.by_col(r_var)
Since we want to run a spatial error model, we need to specify the spatial
weights matrix that includes the spatial configuration of the observations
into the error component of the model. To do that, we can open an already
existing gal file or create a new one. In this case, we will create one
from NAT.shp
.
>>> w = pysal.lib.weights.Rook.from_shapefile(pysal.lib.examples.get_path("NAT.shp"))
Unless there is a good reason not to do it, the weights have to be
rowstandardized so every row of the matrix sums to one. Among other
things, this allows to interpret the spatial lag of a variable as the
average value of the neighboring observations. In PySAL, this can be
easily performed in the following way:
We are all set with the preliminaries, we are good to run the model. In this
case, we will need the variables (exogenous and endogenous), the
instruments and the weights matrix. If we want to
have the names of the variables printed in the output summary, we will
have to pass them in as well, although this is optional.
>>> model = GM_Endog_Error_Regimes(y, x, yend, q, regimes, w=w, name_y=y_var, name_x=x_var, name_yend=yd_var, name_q=q_var, name_regimes=r_var, name_ds='NAT.dbf')
Once we have run the model, we can explore a little bit the output. The
regression object we have created has many attributes so take your time to
discover them. Note that because we are running the classical GMM error
model from 1998/99, the spatial parameter is obtained as a point estimate, so
although you get a value for it (there are for coefficients under
model.betas), you cannot perform inference on it (there are only three
values in model.se_betas). Also, this regression uses a two stage least
squares estimation method that accounts for the endogeneity created by the
endogenous variables included. Alternatively, we can have a summary of the
output by typing: model.summary
>>> print model.name_z
['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', '0_RD90', '1_RD90', 'lambda']
>>> np.around(model.betas, decimals=5)
array([[ 3.59718],
[ 1.0652 ],
[ 0.15822],
[ 9.19754],
[ 1.88082],
[0.24878],
[ 2.46161],
[ 3.57943],
[ 0.25564]])
>>> np.around(model.std_err, decimals=6)
array([ 0.522633, 0.137555, 0.063054, 0.473654, 0.18335 , 0.072786,
0.300711, 0.240413])
Attributes: 
 summary : string
Summary of regression results and diagnostics (note: use in
conjunction with the print command)
 betas : array
kx1 array of estimated coefficients
 u : array
nx1 array of residuals
 e_filtered : array
nx1 array of spatially filtered residuals
 predy : array
nx1 array of predicted y values
 n : integer
Number of observations
 k : integer
Number of variables for which coefficients are estimated
(including the constant)
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
 y : array
nx1 array for dependent variable
 x : array
Two dimensional array with n rows and one column for each
independent (exogenous) variable, including the constant
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
 yend : array
Two dimensional array with n rows and one column for each
endogenous variable
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
 z : array
nxk array of variables (combination of x and yend)
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
 mean_y : float
Mean of dependent variable
 std_y : float
Standard deviation of dependent variable
 vm : array
Variance covariance matrix (kxk)
 pr2 : float
Pseudo R squared (squared correlation between y and ypred)
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
 sig2 : float
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
Sigma squared used in computations
 std_err : array
1xk array of standard errors of the betas
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
 z_stat : list of tuples
z statistic; each tuple contains the pair (statistic,
pvalue), where each is a float
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
 name_y : string
Name of dependent variable for use in output
 name_x : list of strings
Names of independent variables for use in output
 name_yend : list of strings
Names of endogenous variables for use in output
 name_z : list of strings
Names of exogenous and endogenous variables for use in
output
 name_q : list of strings
Names of external instruments
 name_h : list of strings
Names of all instruments used in ouput
 name_w : string
Name of weights matrix for use in output
 name_ds : string
Name of dataset for use in output
 name_regimes : string
Name of regimes variable for use in output
 title : string
Name of the regression method used
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
 regimes : list
List of n values with the mapping of each
observation to a regime. Assumed to be aligned with ‘x’.
 constant_regi : [‘one’, ‘many’]
Ignored if regimes=False. Constant option for regimes.
Switcher controlling the constant term setup. It may take
the following values:
 ‘one’: a vector of ones is appended to x and held
 constant across regimes
 ‘many’: a vector of ones is appended to x and considered
 different per regime
 cols2regi : list, ‘all’
Ignored if regimes=False. Argument indicating whether each
column of x should be considered as different per regime
or held constant across regimes (False).
If a list, k booleans indicating for each variable the
option (True if one per regime, False to be held constant).
If ‘all’, all the variables vary by regime.
 regime_err_sep : boolean
If True, a separate regression is run for each regime.
 kr : int
Number of variables/columns to be “regimized” or subject
to change by regime. These will result in one parameter
estimate by regime for each variable (i.e. nr parameters per
variable)
 kf : int
Number of variables/columns to be considered fixed or
global across regimes and hence only obtain one parameter
estimate
 nr : int
Number of different regimes in the ‘regimes’ list
 multi : dictionary
Only available when multiple regressions are estimated,
i.e. when regime_err_sep=True and no variable is fixed
across regimes.
Contains all attributes of each individual regression


__init__
(y, x, yend, q, regimes, w, cores=False, vm=False, constant_regi='many', cols2regi='all', regime_err_sep=False, regime_lag_sep=False, name_y=None, name_x=None, name_yend=None, name_q=None, name_w=None, name_ds=None, name_regimes=None, summ=True, add_lag=False)[source]
Initialize self. See help(type(self)) for accurate signature.
Methods
__init__ (y, x, yend, q, regimes, w[, cores, …]) 
Initialize self. 
Attributes