pysal.model.spreg.
GM_Endog_Error_Hom
(y, x, yend, q, w, max_iter=1, epsilon=1e05, A1='hom_sc', vm=False, name_y=None, name_x=None, name_yend=None, name_q=None, name_w=None, name_ds=None)[source]¶GMM method for a spatial error model with homoskedasticity and endogenous variables, with results and diagnostics; based on Drukker et al. (2013) [Drukker2013], following Anselin (2011) [Anselin2011].
Parameters: 


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 numpy as np
>>> import pysal.lib
Open data on Columbus neighborhood crime (49 areas) using pysal.lib.io.open(). This is the DBF associated with the Columbus 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('columbus.dbf'),'r')
Extract the HOVAL column (home values) 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 = np.array(db.by_col("HOVAL"))
>>> y = np.reshape(y, (49,1))
Extract INC (income) vector from the DBF to be used as independent variables in the regression. 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 class adds a vector of ones to the independent variables passed in.
>>> X = []
>>> X.append(db.by_col("INC"))
>>> X = np.array(X).T
In this case we consider CRIME (crime rates) is an endogenous regressor. We tell the model that this is so by passing it in a different parameter from the exogenous variables (x).
>>> yd = []
>>> yd.append(db.by_col("CRIME"))
>>> yd = np.array(yd).T
Because we have endogenous variables, to obtain a correct estimate of the model, we need to instrument for CRIME. We use DISCBD (distance to the CBD) for this and hence put it in the instruments parameter, ‘q’.
>>> q = []
>>> q.append(db.by_col("DISCBD"))
>>> q = np.array(q).T
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 columbus.shp
.
>>> w = pysal.lib.weights.Rook.from_shapefile(pysal.lib.examples.get_path("columbus.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, his 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:
>>> w.transform = 'r'
We are all set with the preliminars, 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.
>>> reg = GM_Endog_Error_Hom(y, X, yd, q, w=w, A1='hom_sc', name_x=['inc'], name_y='hoval', name_yend=['crime'], name_q=['discbd'], name_ds='columbus')
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. This class offers an error model that assumes
homoskedasticity but that unlike the models from
spreg.error_sp
, it allows for inference on the spatial
parameter. Hence, we find the same number of betas as of standard errors,
which we calculate taking the square root of the diagonal of the
variancecovariance matrix:
>>> print reg.name_z
['CONSTANT', 'inc', 'crime', 'lambda']
>>> print np.around(np.hstack((reg.betas,np.sqrt(reg.vm.diagonal()).reshape(4,1))),4)
[[ 55.3658 23.496 ]
[ 0.4643 0.7382]
[ 0.669 0.3943]
[ 0.4321 0.1927]]
Attributes: 


__init__
(y, x, yend, q, w, max_iter=1, epsilon=1e05, A1='hom_sc', vm=False, name_y=None, name_x=None, name_yend=None, name_q=None, name_w=None, name_ds=None)[source]¶Initialize self. See help(type(self)) for accurate signature.
Methods
__init__ (y, x, yend, q, w[, max_iter, …]) 
Initialize self. 
Attributes
mean_y 

std_y 