spreg.GM_Combo_Regimes¶

class
spreg.
GM_Combo_Regimes
(y, x, regimes, yend=None, q=None, w=None, w_lags=1, lag_q=True, cores=False, constant_regi='many', cols2regi='all', regime_err_sep=False, regime_lag_sep=False, vm=False, name_y=None, name_x=None, name_yend=None, name_q=None, name_w=None, name_ds=None, name_regimes=None)[source]¶ GMM method for a spatial lag and error model with regimes and endogenous variables, with results and diagnostics; based on Kelejian and Prucha (1998, 1999) [KP98] [KP99].
 Parameters
 yarray
nx1 array for dependent variable
 xarray
Two dimensional array with n rows and one column for each independent (exogenous) variable, excluding the constant
 regimeslist
List of n values with the mapping of each observation to a regime. Assumed to be aligned with ‘x’.
 yendarray
Two dimensional array with n rows and one column for each endogenous variable
 qarray
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)
 wpysal W object
Spatial weights object (always needed)
 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).
 cols2regilist, ‘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
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.
 w_lagsinteger
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_qboolean
If True, then include spatial lags of the additional instruments (q).
 vmboolean
If True, include variancecovariance matrix in summary results
 coresboolean
Specifies if multiprocessing is to be used Default: no multiprocessing, cores = False Note: Multiprocessing may not work on all platforms.
 name_ystring
Name of dependent variable for use in output
 name_xlist of strings
Names of independent variables for use in output
 name_yendlist of strings
Names of endogenous variables for use in output
 name_qlist of strings
Names of instruments for use in output
 name_wstring
Name of weights matrix for use in output
 name_dsstring
Name of dataset for use in output
 name_regimesstring
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 andpysal
to perform all the analysis.>>> import numpy as np >>> import libpysal >>> from libpysal.examples import load_example
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.
>>> nat = load_example('Natregimes') >>> db = libpysal.io.open(nat.get_path("natregimes.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
.>>> w = libpysal.weights.Rook.from_shapefile(nat.get_path("natregimes.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:
>>> w.transform = 'r'
The Combo class runs an SARAR model, that is a spatial lag+error model. In this case we will run a simple version of that, where we have the spatial effects as well as exogenous variables. Since it is a spatial model, we have to pass in 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.
>>> from spreg import GM_Combo_Regimes >>> model = GM_Combo_Regimes(y, x, regimes, w=w, name_y=y_var, name_x=x_var, name_regimes=r_var, name_ds='NAT')
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 spatial lag of the dependent variable. We can have a summary of the output by typing: model.summary Alternatively, we can check the betas:
>>> print(model.name_z) ['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', '_Global_W_HR90', 'lambda'] >>> print(np.around(model.betas,4)) [[ 1.4607] [ 0.958 ] [ 0.5658] [ 9.113 ] [ 1.1338] [ 0.6517] [0.4583] [ 0.6136]]
And lambda:
>>> print('lambda: ', np.around(model.betas[1], 4)) lambda: [0.6136]
This class also allows the user to run a spatial lag+error model with the extra feature of including nonspatial endogenous regressors. This means that, in addition to the spatial lag and error, we consider some of the variables on the righthand side of the equation as endogenous and we instrument for this. In this case we consider RD90 (resource deprivation) as an endogenous regressor. We use FP89 (families below poverty) for this and hence put it in the instruments parameter, ‘q’.
>>> 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 then we can run and explore the model analogously to the previous combo:
>>> model = GM_Combo_Regimes(y, x, regimes, yd, q, 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') >>> print(model.name_z) ['0_CONSTANT', '0_PS90', '0_UE90', '1_CONSTANT', '1_PS90', '1_UE90', '0_RD90', '1_RD90', '_Global_W_HR90', 'lambda'] >>> print(model.betas) [[ 3.41963782] [ 1.04065841] [ 0.16634393] [ 8.86544628] [ 1.85120528] [0.24908469] [ 2.43014046] [ 3.61645481] [ 0.03308671] [ 0.18684992]] >>> print(np.sqrt(model.vm.diagonal())) [0.53067577 0.13271426 0.06058025 0.76406411 0.17969783 0.07167421 0.28943121 0.25308326 0.06126529] >>> print('lambda: ', np.around(model.betas[1], 4)) lambda: [0.1868]
 Attributes
 summarystring
Summary of regression results and diagnostics (note: use in conjunction with the print command)
 betasarray
kx1 array of estimated coefficients
 uarray
nx1 array of residuals
 e_filteredarray
nx1 array of spatially filtered residuals
 e_predarray
nx1 array of residuals (using reduced form)
 predyarray
nx1 array of predicted y values
 predy_earray
nx1 array of predicted y values (using reduced form)
 ninteger
Number of observations
 kinteger
Number of variables for which coefficients are estimated (including the constant) Only available in dictionary ‘multi’ when multiple regressions (see ‘multi’ below for details)
 yarray
nx1 array for dependent variable
 xarray
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)
 yendarray
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)
 zarray
nxk array of variables (combination of x and yend) Only available in dictionary ‘multi’ when multiple regressions (see ‘multi’ below for details)
 mean_yfloat
Mean of dependent variable
 std_yfloat
Standard deviation of dependent variable
 vmarray
Variance covariance matrix (kxk)
 pr2float
Pseudo R squared (squared correlation between y and ypred) Only available in dictionary ‘multi’ when multiple regressions (see ‘multi’ below for details)
 pr2_efloat
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)
 sig2float
Sigma squared used in computations (based on filtered residuals) Only available in dictionary ‘multi’ when multiple regressions (see ‘multi’ below for details)
 std_errarray
1xk array of standard errors of the betas Only available in dictionary ‘multi’ when multiple regressions (see ‘multi’ below for details)
 z_statlist 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_ystring
Name of dependent variable for use in output
 name_xlist of strings
Names of independent variables for use in output
 name_yendlist of strings
Names of endogenous variables for use in output
 name_zlist of strings
Names of exogenous and endogenous variables for use in output
 name_qlist of strings
Names of external instruments
 name_hlist of strings
Names of all instruments used in ouput
 name_wstring
Name of weights matrix for use in output
 name_dsstring
Name of dataset for use in output
 name_regimesstring
Name of regimes variable for use in output
 titlestring
Name of the regression method used Only available in dictionary ‘multi’ when multiple regressions (see ‘multi’ below for details)
 regimeslist
List of n values with the mapping of each observation to a regime. Assumed to be aligned with ‘x’.
 constant_registring
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 (default).
 cols2regilist, ‘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.
 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.
 krint
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)
 kfint
Number of variables/columns to be considered fixed or global across regimes and hence only obtain one parameter estimate
 nrint
Number of different regimes in the ‘regimes’ list
 multidictionary
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, regimes, yend=None, q=None, w=None, w_lags=1, lag_q=True, cores=False, constant_regi='many', cols2regi='all', regime_err_sep=False, regime_lag_sep=False, vm=False, name_y=None, name_x=None, name_yend=None, name_q=None, name_w=None, name_ds=None, name_regimes=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
(y, x, regimes[, yend, q, w, …])Initialize self.
Attributes

property
mean_y
¶

property
std_y
¶