spreg.ThreeSLS¶

class
spreg.
ThreeSLS
(bigy, bigX, bigyend, bigq, regimes=None, nonspat_diag=True, name_bigy=None, name_bigX=None, name_bigyend=None, name_bigq=None, name_ds=None, name_regimes=None)[source]¶ User class for 3SLS estimation
 Parameters
 bigydictionary
with vector for dependent variable by equation
 bigXdictionary
with matrix of explanatory variables by equation (note, already includes constant term)
 bigyenddictionary
with matrix of endogenous variables by equation
 bigqdictionary
with matrix of instruments by equation
 regimeslist
List of n values with the mapping of each observation to a regime. Assumed to be aligned with ‘x’.
 nonspat_diag: boolean
flag for nonspatial diagnostics, default = True.
 name_bigydictionary
with name of dependent variable for each equation. default = None, but should be specified. is done when sur_stackxy is used
 name_bigXdictionary
with names of explanatory variables for each equation. default = None, but should be specified. is done when sur_stackxy is used
 name_bigyenddictionary
with names of endogenous variables for each equation. default = None, but should be specified. is done when sur_stackZ is used
 name_bigqdictionary
with names of instrumental variables for each equation. default = None, but should be specified. is done when sur_stackZ is used.
 name_dsstring
name for the data set.
 name_regimesstring
name of regime variable for use in the output.
Examples
First import libpysal to load the spatial analysis tools.
>>> import libpysal
Open data on NCOVR US County Homicides (3085 areas) using libpysal.io.open(). This is the DBF associated with the NAT shapefile. Note that libpysal.io.open() also reads data in CSV format.
>>> db = libpysal.io.open(libpysal.examples.get_path("NAT.dbf"),'r')
The specification of the model to be estimated can be provided as lists. Each equation should be listed separately. In this example, equation 1 has HR80 as dependent variable, PS80 and UE80 as exogenous regressors, RD80 as endogenous regressor and FP79 as additional instrument. For equation 2, HR90 is the dependent variable, PS90 and UE90 the exogenous regressors, RD90 as endogenous regressor and FP99 as additional instrument
>>> y_var = ['HR80','HR90'] >>> x_var = [['PS80','UE80'],['PS90','UE90']] >>> yend_var = [['RD80'],['RD90']] >>> q_var = [['FP79'],['FP89']]
The SUR method requires data to be provided as dictionaries. PySAL provides two tools to create these dictionaries from the list of variables: sur_dictxy and sur_dictZ. The tool sur_dictxy can be used to create the dictionaries for Y and X, and sur_dictZ for endogenous variables (yend) and additional instruments (q).
>>> bigy,bigX,bigyvars,bigXvars = pysal.spreg.sur_utils.sur_dictxy(db,y_var,x_var) >>> bigyend,bigyendvars = pysal.spreg.sur_utils.sur_dictZ(db,yend_var) >>> bigq,bigqvars = pysal.spreg.sur_utils.sur_dictZ(db,q_var)
We can now run the regression and then have a summary of the output by typing: print(reg.summary)
Alternatively, we can just check the betas and standard errors, asymptotic t and pvalue of the parameters:
>>> reg = ThreeSLS(bigy,bigX,bigyend,bigq,name_bigy=bigyvars,name_bigX=bigXvars,name_bigyend=bigyendvars,name_bigq=bigqvars,name_ds="NAT") >>> reg.b3SLS {0: array([[ 6.92426353], [ 1.42921826], [ 0.00049435], [ 3.5829275 ]]), 1: array([[ 7.62385875], [ 1.65031181], [0.21682974], [ 3.91250428]])}
>>> reg.tsls_inf {0: array([[ 0.23220853, 29.81916157, 0. ], [ 0.10373417, 13.77770036, 0. ], [ 0.03086193, 0.01601807, 0.98721998], [ 0.11131999, 32.18584124, 0. ]]), 1: array([[ 0.28739415, 26.52753638, 0. ], [ 0.09597031, 17.19606554, 0. ], [ 0.04089547, 5.30204786, 0.00000011], [ 0.13586789, 28.79638723, 0. ]])}
 Attributes
 bigydictionary
with y values
 bigZdictionary
with matrix of exogenous and endogenous variables for each equation
 bigZHZHdictionary
with matrix of cross products Zhat_r’Zhat_s
 bigZHydictionary
with matrix of cross products Zhat_r’y_end_s
 n_eqint
number of equations
 nint
number of observations in each crosssection
 bigKarray
vector with number of explanatory variables (including constant, exogenous and endogenous) for each equation
 b2SLSdictionary
with 2SLS regression coefficients for each equation
 tslsEarray
N x n_eq array with OLS residuals for each equation
 b3SLSdictionary
with 3SLS regression coefficients for each equation
 varbarray
variancecovariance matrix
 sigarray
Sigma matrix of interequation error covariances
 bigEarray
n by n_eq array of residuals
 corrarray
interequation 3SLS error correlation matrix
 tsls_infdictionary
with standard error, asymptotic t and pvalue, one for each equation
 surchowarray
list with tuples for Chow test on regression coefficients each tuple contains test value, degrees of freedom, pvalue
 name_dsstring
name for the data set
 name_bigydictionary
with name of dependent variable for each equation
 name_bigXdictionary
with names of explanatory variables for each equation
 name_bigyenddictionary
with names of endogenous variables for each equation
 name_bigqdictionary
with names of instrumental variables for each equations
 name_regimesstring
name of regime variable for use in the output

__init__
(self, bigy, bigX, bigyend, bigq, regimes=None, nonspat_diag=True, name_bigy=None, name_bigX=None, name_bigyend=None, name_bigq=None, name_ds=None, name_regimes=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
(self, bigy, bigX, bigyend, bigq[, …])Initialize self.