spreg.SURlagIV¶

class
spreg.
SURlagIV
(bigy, bigX, bigyend=None, bigq=None, w=None, regimes=None, vm=False, regime_lag_sep=False, w_lags=1, lag_q=True, nonspat_diag=True, spat_diag=False, name_bigy=None, name_bigX=None, name_bigyend=None, name_bigq=None, name_ds=None, name_w=None, name_regimes=None)[source]¶ User class for spatial lag estimation using IV
 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 (optional)
 bigqdictionary
with matrix of instruments by equation (optional)
 wspatial weights object, required
 vmboolean
listing of full variancecovariance matrix, default = False
 w_lagsinteger
order of spatial lags for WX instruments, default = 1
 lag_qboolean
flag to apply spatial lag to other instruments, default = True
 nonspat_diagboolean
flag for nonspatial diagnostics, default = True
 spat_diagboolean
flag for spatial diagnostics, default = False
 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 equations. default = None, but should be specified. is done when sur_stackZ is used.
 name_dsstring
name for the data set
 name_wstring
name for the spatial weights
Examples
First import libpysal to load the spatial analysis tools.
>>> import libpysal >>> from libpysal.examples import load_example >>> from libpysal.weights import Queen >>> import spreg >>> np.set_printoptions(suppress=True) #prevent scientific format
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.
>>> nat = load_example('Natregimes') >>> db = libpysal.io.open(nat.get_path('natregimes.dbf'), 'r')
The specification of the model to be estimated can be provided as lists. Each equation should be listed separately. Although not required, in this example we will specify additional endogenous regressors. 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 = spreg.sur_dictxy(db,y_var,x_var) >>> bigyend,bigyendvars = spreg.sur_dictZ(db,yend_var) >>> bigq,bigqvars = spreg.sur_dictZ(db,q_var)
To run a spatial lag model, we need to specify the spatial weights matrix. To do that, we can open an already existing gal file or create a new one. In this example, we will create a new one from NAT.shp and transform it to rowstandardized.
>>> w = Queen.from_shapefile(nat.get_path("natregimes.shp")) >>> w.transform='r'
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 = spreg.SURlagIV(bigy,bigX,bigyend,bigq,w=w,name_bigy=bigyvars,name_bigX=bigXvars,name_bigyend=bigyendvars,name_bigq=bigqvars,name_ds="NAT",name_w="nat_queen") >>> reg.b3SLS {0: array([[ 6.95472387], [ 1.44044301], [0.00771893], [ 3.65051153], [ 0.00362663]]), 1: array([[ 5.61101925], [ 1.38716801], [0.15512029], [ 3.1884457 ], [ 0.25832185]])}
>>> reg.tsls_inf {0: array([[ 0.49128435, 14.15620899, 0. ], [ 0.11516292, 12.50787151, 0. ], [ 0.03204088, 0.2409087 , 0.80962588], [ 0.1876025 , 19.45875745, 0. ], [ 0.05450628, 0.06653605, 0.94695106]]), 1: array([[ 0.44969956, 12.47726211, 0. ], [ 0.10440241, 13.28674277, 0. ], [ 0.04150243, 3.73761961, 0.00018577], [ 0.19133145, 16.66451427, 0. ], [ 0.04394024, 5.87893596, 0. ]])}
 Attributes
 wspatial weights object
 bigydictionary
with y values
 bigZdictionary
with matrix of exogenous and endogenous variables for each equation
 bigyenddictionary
with matrix of endogenous variables for each equation; contains Wy only if no other endogenous specified
 bigqdictionary
with matrix of instrumental variables for each equation; contains WX only if no other endogenous specified
 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
 residsarray
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
 joinrhotuple
test on joint significance of spatial autoregressive coefficient. tuple with test statistic, degrees of freedom, pvalue
 surchowarray
list with tuples for Chow test on regression coefficients each tuple contains test value, degrees of freedom, pvalue
 name_wstring
name for the spatial weights
 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

__init__
(bigy, bigX, bigyend=None, bigq=None, w=None, regimes=None, vm=False, regime_lag_sep=False, w_lags=1, lag_q=True, nonspat_diag=True, spat_diag=False, name_bigy=None, name_bigX=None, name_bigyend=None, name_bigq=None, name_ds=None, name_w=None, name_regimes=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
(bigy, bigX[, bigyend, bigq, w, …])Initialize self.