spreg.GM_Error_Regimes

class spreg.GM_Error_Regimes(y, x, regimes, w, vm=False, name_y=None, name_x=None, name_w=None, constant_regi='many', cols2regi='all', regime_err_sep=False, regime_lag_sep=False, slx_lags=0, cores=False, name_ds=None, name_regimes=None, latex=False)[source]

GMM method for a spatial error model with regimes, with results and diagnostics; based on Kelejian and Prucha (1998, 1999) [KP98] [KP99].

Parameters:
ynumpy.ndarray or pandas.Series

nx1 array for dependent variable

xnumpy.ndarray or pandas object

Two dimensional array with n rows and one column for each independent (exogenous) variable, excluding the constant

regimeslist or pandas.Series

List of n values with the mapping of each observation to a regime. Assumed to be aligned with ‘x’.

wpysal W object

Spatial weights object

constant_regi: string, optional

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

Always False, kept for consistency, ignored.

slx_lagsinteger

Number of spatial lags of X to include in the model specification. If slx_lags>0, the specification becomes of the SLX-Error type.

vmbool

If True, include variance-covariance matrix in summary results

coresbool

Specifies if multiprocessing is to be used Default: no multiprocessing, cores = False Note: Multiprocessing may not work on all platforms.

name_ystr

Name of dependent variable for use in output

name_xlist of strings

Names of independent variables for use in output

name_wstr

Name of weights matrix for use in output

name_dsstr

Name of dataset for use in output

name_regimesstr

Name of regime variable for use in the output

latexbool

Specifies if summary is to be printed in latex format

hard_boundbool

If true, raises an exception if the estimated spatial autoregressive parameter is outside the maximum/minimum bounds.

Attributes
———-
outputdataframe

regression results pandas dataframe

summarystr

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

predyarray

nx1 array of predicted y values

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)

mean_yfloat

Mean of dependent variable

std_yfloat

Standard deviation of dependent variable

pr2float

Pseudo R squared (squared correlation between y and ypred) Only available in dictionary ‘multi’ when multiple regressions (see ‘multi’ below for details)

vmarray

Variance covariance matrix (kxk)

sig2float

Sigma squared used in computations 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, p-value), where each is a float Only available in dictionary ‘multi’ when multiple regressions (see ‘multi’ below for details)

name_ystr

Name of dependent variable for use in output

name_xlist of strings

Names of independent variables for use in output

name_wstr

Name of weights matrix for use in output

name_dsstr

Name of dataset for use in output

name_regimesstr

Name of regime variable for use in the output

titlestr

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_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

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.

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

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 libpysal
>>> import numpy as np
>>> 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 error 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 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'

We are all set with the preliminaries, we are good to run the model. In this case, we will need the variables 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.

>>> from spreg import GM_Error_Regimes
>>> model = GM_Error_Regimes(y, x, regimes, w=w, name_y=y_var, name_x=x_var, name_regimes=r_var, name_ds='NAT.dbf')

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). Alternatively, we can have a summary of the output by typing: model.summary

>>> print(model.output)
    var_names coefficients   std_err    zt_stat      prob
0  0_CONSTANT     0.074811  0.379864   0.196942  0.843873
1      0_PS90     0.786105  0.152315   5.161043       0.0
2      0_UE90     0.538848  0.051942  10.373969       0.0
3  1_CONSTANT     5.103761  0.471284   10.82949       0.0
4      1_PS90     1.196009   0.19867   6.020074       0.0
5      1_UE90     0.600532  0.057252  10.489217       0.0
6      lambda       0.3641      None       None      None
__init__(y, x, regimes, w, vm=False, name_y=None, name_x=None, name_w=None, constant_regi='many', cols2regi='all', regime_err_sep=False, regime_lag_sep=False, slx_lags=0, cores=False, name_ds=None, name_regimes=None, latex=False)[source]

Methods

__init__(y, x, regimes, w[, vm, name_y, ...])

Attributes

mean_y

std_y

property mean_y
property std_y