'''
Spatial Two Stages Least Squares with Regimes
'''
__author__ = "Luc Anselin luc.anselin@asu.edu, Pedro V. Amaral pedro.amaral@asu.edu, David C. Folch david.folch@asu.edu"
import numpy as np
from . import regimes as REGI
from . import user_output as USER
from . import summary_output as SUMMARY
import multiprocessing as mp
from .twosls_regimes import TSLS_Regimes, _optimal_weight
from .twosls import BaseTSLS
from .utils import set_endog, set_endog_sparse, sp_att, set_warn, sphstack, spdot
from .robust import hac_multi
[docs]class GM_Lag_Regimes(TSLS_Regimes, REGI.Regimes_Frame):
"""
Spatial two stage least squares (S2SLS) with regimes;
:cite:`Anselin1988`
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
regimes : list
List of n values with the mapping of each
observation to a regime. Assumed to be aligned with 'x'.
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); cannot be
used in combination with h
constant_regi: string
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.
w : pysal W object
Spatial weights object
w_lags : integer
Orders of W to include as instruments for the spatially
lagged dependent variable. For example, w_lags=1, then
instruments are WX; if w_lags=2, then WX, WWX; and so on.
lag_q : boolean
If True, then include spatial lags of the additional
instruments (q).
regime_lag_sep: boolean
If True (default), the spatial parameter for spatial lag is also
computed according to different regimes. If False,
the spatial parameter is fixed accross regimes.
Option valid only when regime_err_sep=True
regime_err_sep: boolean
If True, a separate regression is run for each regime.
robust : string
If 'white', then a White consistent estimator of the
variance-covariance matrix is given.
If 'hac', then a HAC consistent estimator of the
variance-covariance matrix is given.
If 'ogmm', then Optimal GMM is used to estimate
betas and the variance-covariance matrix.
Default set to None.
gwk : pysal W object
Kernel spatial weights needed for HAC estimation. Note:
matrix must have ones along the main diagonal.
sig2n_k : boolean
If True, then use n-k to estimate sigma^2. If False, use n.
spat_diag : boolean
If True, then compute Anselin-Kelejian test
vm : boolean
If True, include variance-covariance 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_gwk : string
Name of kernel 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
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_pred : array
nx1 array of residuals (using reduced form)
predy : array
nx1 array of predicted y values
predy_e : array
nx1 array of predicted y values (using reduced form)
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)
kstar : integer
Number of endogenous variables.
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)
q : array
Two dimensional array with n rows and one column for each
external exogenous variable used as instruments
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)
h : array
nxl array of instruments (combination of x and q)
Only available in dictionary 'multi' when multiple regressions
(see 'multi' below for details)
robust : string
Adjustment for robust standard errors
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)
pr2_e : float
Pseudo R squared (squared correlation between y and ypred_e
(using reduced form))
Only available in dictionary 'multi' when multiple regressions
(see 'multi' below for details)
utu : float
Sum of squared residuals
sig2 : float
Sigma squared used in computations
Only available in dictionary 'multi' when multiple regressions
(see 'multi' below for details)
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,
p-value), where each is a float
Only available in dictionary 'multi' when multiple regressions
(see 'multi' below for details)
ak_test : tuple
Anselin-Kelejian test; tuple contains the pair (statistic,
p-value)
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_gwk : string
Name of kernel 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)
sig2n : float
Sigma squared (computed with n in the denominator)
sig2n_k : float
Sigma squared (computed with n-k in the denominator)
hth : float
:math:`H'H`.
Only available in dictionary 'multi' when multiple regressions
(see 'multi' below for details)
hthi : float
:math:`(H'H)^{-1}`.
Only available in dictionary 'multi' when multiple regressions
(see 'multi' below for details)
varb : array
:math:`(Z'H (H'H)^{-1} H'Z)^{-1}`.
Only available in dictionary 'multi' when multiple regressions
(see 'multi' below for details)
zthhthi : array
:math:`Z'H(H'H)^{-1}`.
Only available in dictionary 'multi' when multiple regressions
(see 'multi' below for details)
pfora1a2 : array
n(zthhthi)'varb
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: string
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_lag_sep: boolean
If True, the spatial parameter for spatial lag is also
computed according to different regimes. If False (default),
the spatial parameter is fixed accross regimes.
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
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 libpysal
>>> from libpysal import examples
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; since the actual class
requires data to be passed in as numpy arrays, the user can read their
data in using any method.
>>> db = libpysal.io.open(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
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 lag model, we need to specify
the spatial weights matrix that includes the spatial configuration of the
observations. 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``.
>>> from libpysal import weights
>>> w = weights.Rook.from_shapefile(examples.get_path("NAT.shp"))
Unless there is a good reason not to do it, the weights have to be
row-standardized 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:
>>> w.transform = 'r'
This class runs a lag model, which means that includes the spatial lag of
the dependent variable on the right-hand side of the equation. 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.
>>> from spreg import GM_Lag_Regimes
>>> model=GM_Lag_Regimes(y, x, regimes, w=w, regime_lag_sep=False, regime_err_sep=False, name_y=y_var, name_x=x_var, name_regimes=r_var, name_ds='NAT', name_w='NAT.shp')
>>> model.betas
array([[ 1.28897623],
[ 0.79777722],
[ 0.56366891],
[ 8.73327838],
[ 1.30433406],
[ 0.62418643],
[-0.39993716]])
Once the model is run, we can have a summary of the output by typing:
model.summary . Alternatively, we can obtain the standard error of
the coefficient estimates by calling:
>>> model.std_err
array([0.44682888, 0.14358192, 0.05655124, 1.06044865, 0.20184548,
0.06118262, 0.12387232])
In the example above, all coefficients but the spatial lag vary
according to the regime. It is also possible to have the spatial lag
varying according to the regime, which effective will result in an
independent spatial lag model estimated for each regime. To run these
models, the argument regime_lag_sep must be set to True:
>>> model=GM_Lag_Regimes(y, x, regimes, w=w, regime_lag_sep=True, name_y=y_var, name_x=x_var, name_regimes=r_var, name_ds='NAT', name_w='NAT.shp')
>>> print(np.hstack((np.array(model.name_z).reshape(8,1),model.betas,np.sqrt(model.vm.diagonal().reshape(8,1)))))
[['0_CONSTANT' '1.3658476998618099' '0.3985472089832652']
['0_PS90' '0.8087573074246643' '0.11324884794883601']
['0_UE90' '0.5694681319188577' '0.04625087717092595']
['0_W_HR90' '-0.43424389464634316' '0.13350159258670305']
['1_CONSTANT' '7.90731073341874' '1.6360187416950998']
['1_PS90' '1.2746570332609135' '0.2470987049452741']
['1_UE90' '0.6016769336173784' '0.07993322102145078']
['1_W_HR90' '-0.2960338343846942' '0.19934459782427025']]
Alternatively, we can type: 'model.summary' to see the organized results output.
The class is flexible enough to accomodate a spatial lag model that,
besides the spatial lag of the dependent variable, includes other
non-spatial endogenous regressors. As an example, we will add the endogenous
variable RD90 (resource deprivation) and we decide to instrument for it with
FP89 (families below poverty):
>>> yd_var = ['RD90']
>>> yd = 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
And we can run the model again:
>>> model = GM_Lag_Regimes(y, x, regimes, yend=yd, q=q, w=w, regime_lag_sep=False, regime_err_sep=False, name_y=y_var, name_x=x_var, name_yend=yd_var, name_q=q_var, name_regimes=r_var, name_ds='NAT', name_w='NAT.shp')
>>> model.betas
array([[ 3.42195202],
[ 1.03311878],
[ 0.14308741],
[ 8.99740066],
[ 1.91877758],
[-0.32084816],
[ 2.38918212],
[ 3.67243761],
[ 0.06959139]])
Once the model is run, we can obtain the standard error of the coefficient
estimates. Alternatively, we can have a summary of the output by typing:
model.summary
>>> model.std_err
array([0.49163311, 0.12237382, 0.05633464, 0.72555909, 0.17250521,
0.06749131, 0.27370369, 0.25106224, 0.05804213])
"""
[docs] def __init__(self, y, x, regimes, yend=None, q=None,
w=None, w_lags=1, lag_q=True,
robust=None, gwk=None, sig2n_k=False,
spat_diag=False, constant_regi='many',
cols2regi='all', regime_lag_sep=False, regime_err_sep=True,
cores=False, vm=False, name_y=None, name_x=None,
name_yend=None, name_q=None, name_regimes=None,
name_w=None, name_gwk=None, name_ds=None):
n = USER.check_arrays(y, x)
y = USER.check_y(y, n)
USER.check_weights(w, y, w_required=True)
USER.check_robust(robust, gwk)
USER.check_spat_diag(spat_diag, w)
x_constant,name_x,warn = USER.check_constant(x,name_x,just_rem=True)
set_warn(self,warn)
name_x = USER.set_name_x(name_x, x_constant, constant=True)
name_y = USER.set_name_y(name_y)
name_yend = USER.set_name_yend(name_yend, yend)
name_q = USER.set_name_q(name_q, q)
name_q.extend(
USER.set_name_q_sp(name_x, w_lags, name_q, lag_q, force_all=True))
self.name_regimes = USER.set_name_ds(name_regimes)
self.constant_regi = constant_regi
self.n = n
cols2regi = REGI.check_cols2regi(
constant_regi, cols2regi, x_constant, yend=yend, add_cons=False)
self.cols2regi = cols2regi
self.regimes_set = REGI._get_regimes_set(regimes)
self.regimes = regimes
USER.check_regimes(self.regimes_set, self.n, x_constant.shape[1])
if regime_err_sep == True and robust == 'hac':
set_warn(
self, "Error by regimes is incompatible with HAC estimation for Spatial Lag models. Hence, error and lag by regimes have been disabled for this model.")
regime_err_sep = False
regime_lag_sep = False
self.regime_err_sep = regime_err_sep
self.regime_lag_sep = regime_lag_sep
if regime_lag_sep == True:
if not regime_err_sep:
raise Exception("regime_err_sep must be True when regime_lag_sep=True.")
cols2regi += [True]
w_i, regi_ids, warn = REGI.w_regimes(
w, regimes, self.regimes_set, transform=True, get_ids=True, min_n=len(cols2regi) + 1)
set_warn(self, warn)
else:
cols2regi += [False]
if regime_err_sep == True and set(cols2regi) == set([True]) and constant_regi == 'many':
self.y = y
self.GM_Lag_Regimes_Multi(y, x_constant, w_i, w, regi_ids,
yend=yend, q=q, w_lags=w_lags, lag_q=lag_q, cores=cores,
robust=robust, gwk=gwk, sig2n_k=sig2n_k, cols2regi=cols2regi,
spat_diag=spat_diag, vm=vm, name_y=name_y, name_x=name_x,
name_yend=name_yend, name_q=name_q, name_regimes=self.name_regimes,
name_w=name_w, name_gwk=name_gwk, name_ds=name_ds)
else:
if regime_lag_sep == True:
w = REGI.w_regimes_union(w, w_i, self.regimes_set)
yend2, q2 = set_endog(y, x_constant, w, yend, q, w_lags, lag_q)
name_yend.append(USER.set_name_yend_sp(name_y))
TSLS_Regimes.__init__(self, y=y, x=x_constant, yend=yend2, q=q2,
regimes=regimes, w=w, robust=robust, gwk=gwk,
sig2n_k=sig2n_k, spat_diag=spat_diag, vm=vm,
constant_regi=constant_regi, cols2regi=cols2regi, regime_err_sep=regime_err_sep,
name_y=name_y, name_x=name_x, name_yend=name_yend, name_q=name_q,
name_regimes=name_regimes, name_w=name_w, name_gwk=name_gwk,
name_ds=name_ds, summ=False)
if regime_lag_sep:
self.sp_att_reg(w_i, regi_ids, yend2[:, -1].reshape(self.n, 1))
else:
self.rho = self.betas[-1]
self.predy_e, self.e_pred, warn = sp_att(w, self.y, self.predy,
yend2[:, -1].reshape(self.n, 1), self.rho)
set_warn(self, warn)
self.regime_lag_sep = regime_lag_sep
self.title = "SPATIAL " + self.title
SUMMARY.GM_Lag(
reg=self, w=w, vm=vm, spat_diag=spat_diag, regimes=True)
[docs] def GM_Lag_Regimes_Multi(self, y, x, w_i, w, regi_ids, cores=False,
yend=None, q=None, w_lags=1, lag_q=True,
robust=None, gwk=None, sig2n_k=False, cols2regi='all',
spat_diag=False, vm=False, name_y=None, name_x=None,
name_yend=None, name_q=None, name_regimes=None,
name_w=None, name_gwk=None, name_ds=None):
# pool = mp.Pool(cores)
self.name_ds = USER.set_name_ds(name_ds)
name_yend.append(USER.set_name_yend_sp(name_y))
self.name_w = USER.set_name_w(name_w, w_i)
self.name_gwk = USER.set_name_w(name_gwk, gwk)
results_p = {}
"""
for r in self.regimes_set:
w_r = w_i[r].sparse
if system() == 'Windows':
is_win = True
results_p[r] = _work(*(y,x,regi_ids,r,yend,q,w_r,w_lags,lag_q,robust,sig2n_k,self.name_ds,name_y,name_x,name_yend,name_q,self.name_w,name_regimes))
else:
results_p[r] = pool.apply_async(_work,args=(y,x,regi_ids,r,yend,q,w_r,w_lags,lag_q,robust,sig2n_k,self.name_ds,name_y,name_x,name_yend,name_q,self.name_w,name_regimes, ))
is_win = False
"""
x_constant,name_x = REGI.check_const_regi(self,x,name_x,regi_ids)
self.name_x_r = name_x
for r in self.regimes_set:
w_r = w_i[r].sparse
if cores:
pool = mp.Pool(None)
results_p[r] = pool.apply_async(_work, args=(
y, x_constant, regi_ids, r, yend, q, w_r, w_lags, lag_q, robust, sig2n_k, self.name_ds, name_y, name_x, name_yend, name_q, self.name_w, name_regimes, ))
else:
results_p[r] = _work(*(y, x_constant, regi_ids, r, yend, q, w_r, w_lags, lag_q, robust,
sig2n_k, self.name_ds, name_y, name_x, name_yend, name_q, self.name_w, name_regimes))
self.kryd = 0
self.kr = len(cols2regi)+1
self.kf = 0
self.nr = len(self.regimes_set)
self.name_x_r = name_x + name_yend
self.name_regimes = name_regimes
self.vm = np.zeros((self.nr * self.kr, self.nr * self.kr), float)
self.betas = np.zeros((self.nr * self.kr, 1), float)
self.u = np.zeros((self.n, 1), float)
self.predy = np.zeros((self.n, 1), float)
self.predy_e = np.zeros((self.n, 1), float)
self.e_pred = np.zeros((self.n, 1), float)
"""
if not is_win:
pool.close()
pool.join()
"""
if cores:
pool.close()
pool.join()
results = {}
self.name_y, self.name_x, self.name_yend, self.name_q, self.name_z, self.name_h = [
], [], [], [], [], []
counter = 0
for r in self.regimes_set:
"""
if is_win:
results[r] = results_p[r]
else:
results[r] = results_p[r].get()
"""
if not cores:
results[r] = results_p[r]
else:
results[r] = results_p[r].get()
results[r].predy_e, results[r].e_pred, warn = sp_att(w_i[r], results[r].y, results[
r].predy, results[r].yend[:, -1].reshape(results[r].n, 1), results[r].rho)
set_warn(results[r], warn)
results[r].w = w_i[r]
self.vm[(counter * self.kr):((counter + 1) * self.kr),
(counter * self.kr):((counter + 1) * self.kr)] = results[r].vm
self.betas[
(counter * self.kr):((counter + 1) * self.kr), ] = results[r].betas
self.u[regi_ids[r], ] = results[r].u
self.predy[regi_ids[r], ] = results[r].predy
self.predy_e[regi_ids[r], ] = results[r].predy_e
self.e_pred[regi_ids[r], ] = results[r].e_pred
self.name_y += results[r].name_y
self.name_x += results[r].name_x
self.name_yend += results[r].name_yend
self.name_q += results[r].name_q
self.name_z += results[r].name_z
self.name_h += results[r].name_h
if r == self.regimes_set[0]:
self.hac_var = np.zeros((self.n, results[r].h.shape[1]), float)
self.hac_var[regi_ids[r], ] = results[r].h
counter += 1
self.multi = results
if robust == 'hac':
hac_multi(self, gwk, constant=True)
if robust == 'ogmm':
set_warn(
self, "Residuals treated as homoskedastic for the purpose of diagnostics.")
self.chow = REGI.Chow(self)
if spat_diag:
pass
#self._get_spat_diag_props(y, x, w, yend, q, w_lags, lag_q)
SUMMARY.GM_Lag_multi(
reg=self, multireg=self.multi, vm=vm, spat_diag=spat_diag, regimes=True, w=w)
[docs] def sp_att_reg(self, w_i, regi_ids, wy):
predy_e_r, e_pred_r = {}, {}
self.predy_e = np.zeros((self.n, 1), float)
self.e_pred = np.zeros((self.n, 1), float)
counter = 1
for r in self.regimes_set:
self.rho = self.betas[(self.kr - self.kryd) * self.nr + self.kf - (
self.yend.shape[1] - self.nr * self.kryd) + self.kryd * counter - 1]
self.predy_e[regi_ids[r], ], self.e_pred[regi_ids[r], ], warn = sp_att(w_i[r],
self.y[regi_ids[r]], self.predy[
regi_ids[r]],
wy[regi_ids[r]], self.rho)
counter += 1
def _get_spat_diag_props(self, y, x, w, yend, q, w_lags, lag_q):
self._cache = {}
yend, q = set_endog(y, x[:,1:], w, yend, q, w_lags, lag_q)
#x = USER.check_constant(x)
x = REGI.regimeX_setup(
x, self.regimes, [True] * x.shape[1], self.regimes_set)
self.z = sphstack(x, REGI.regimeX_setup(
yend, self.regimes, [True] * (yend.shape[1] - 1) + [False], self.regimes_set))
self.h = sphstack(
x, REGI.regimeX_setup(q, self.regimes, [True] * q.shape[1], self.regimes_set))
hthi = np.linalg.inv(spdot(self.h.T, self.h))
zth = spdot(self.z.T, self.h)
self.varb = np.linalg.inv(spdot(spdot(zth, hthi), zth.T))
def _work(y, x, regi_ids, r, yend, q, w_r, w_lags, lag_q, robust, sig2n_k, name_ds, name_y, name_x, name_yend, name_q, name_w, name_regimes):
y_r = y[regi_ids[r]]
x_r = x[regi_ids[r]]
if yend is not None:
yend_r = yend[regi_ids[r]]
else:
yend_r = yend
if q is not None:
q_r = q[regi_ids[r]]
else:
q_r = q
yend_r, q_r = set_endog_sparse(y_r, x_r[:,1:], w_r, yend_r, q_r, w_lags, lag_q)
#x_constant = USER.check_constant(x_r)
if robust == 'hac' or robust == 'ogmm':
robust2 = None
else:
robust2 = robust
model = BaseTSLS(
y_r, x_r, yend_r, q_r, robust=robust2, sig2n_k=sig2n_k)
model.title = "SPATIAL TWO STAGE LEAST SQUARES ESTIMATION - REGIME %s" % r
if robust == 'ogmm':
_optimal_weight(model, sig2n_k, warn=False)
model.rho = model.betas[-1]
model.robust = USER.set_robust(robust)
model.name_ds = name_ds
model.name_y = '%s_%s' % (str(r), name_y)
model.name_x = ['%s_%s' % (str(r), i) for i in name_x]
model.name_yend = ['%s_%s' % (str(r), i) for i in name_yend]
model.name_z = model.name_x + model.name_yend
model.name_q = ['%s_%s' % (str(r), i) for i in name_q]
model.name_h = model.name_x + model.name_q
model.name_w = name_w
model.name_regimes = name_regimes
return model
def _test():
import doctest
start_suppress = np.get_printoptions()['suppress']
np.set_printoptions(suppress=True)
doctest.testmod()
np.set_printoptions(suppress=start_suppress)
if __name__ == '__main__':
_test()
import numpy as np
import libpysal
from libpysal import examples
db = libpysal.io.open(examples.get_path("columbus.dbf"), 'r')
y_var = 'CRIME'
y = np.array([db.by_col(y_var)]).reshape(49, 1)
x_var = ['INC']
x = np.array([db.by_col(name) for name in x_var]).T
yd_var = ['HOVAL']
yd = np.array([db.by_col(name) for name in yd_var]).T
q_var = ['DISCBD']
q = np.array([db.by_col(name) for name in q_var]).T
r_var = 'NSA'
regimes = db.by_col(r_var)
w = libpysal.weights.Queen.from_shapefile(libpysal.examples.get_path("columbus.shp"))
w.transform = 'r'
model = GM_Lag_Regimes(y, x, regimes, yend=yd, q=q, w=w, constant_regi='many', spat_diag=True, sig2n_k=False, lag_q=True, name_y=y_var,
name_x=x_var, name_yend=yd_var, name_q=q_var, name_regimes=r_var, name_ds='columbus', name_w='columbus.gal', regime_err_sep=True, robust='white')
print(model.summary)