spreg.PanelRE

class spreg.PanelRE(y, x, w, slx_lags=0, slx_vars='all', time_effects=False, spat_diag=True, vm=False, name_y=None, name_x=None, name_w=None, name_ds=None, latex=False)

Random Effects (One-Way or Two-Way) GLS for Panel Data.

Includes the Swamy-Arora estimator for variance components and the classic Hausman Specification Test (not robust to spatial autocorrelation).

Parameters:

ynumpy.ndarray or pandas object

nxt or (nxt)x1 array for dependent variable

xnumpy.ndarray or pandas object

nx(txk) or (nxt)xk array for independent (exogenous) variables.

wpysal W object

Spatial weights object. Required to determine cross-sectional dimension (N).

slx_lagsinteger

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

slx_varseither “All” (default) or list of booleans to select x variables

to be lagged

time_effectsbool

If True, then include time dummies in the model specification (first omitted). Default: False

spat_diagbool

If True (default), then compute the ‘LMC_spatial’ BSK test.

vmbool

if True, include variance-covariance matrix in summary results

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

latexbool

Specifies if summary is to be printed in latex format

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

(nxt)x1 array of residuals

predyarray

(nxt)x1 array of predicted y values

ninteger

Total number of observations (N * T)

tinteger

Number of time periods

kinteger

Number of variables

thetafloat

The quasi-demeaning weight (0 = Pooled, 1 = FE)

hausman_statfloat

Chi-square statistic for the Hausman test

hausman_pfloat

P-value for the Hausman test

sigma2_mufloat

Estimated variance of the random effects

sigma2_epsilonfloat

Estimated variance of the idiosyncratic errors

Examples

>>> import libpysal
>>> import geopandas as gpd
>>> from spreg import PanelRE

In this example, we will use data on NCOVR US County Homicides (3085 areas) included in libpysal.

>>> libpysal.examples.load_example('NCOVR')
>>> db = gpd.read_file(libpysal.examples.get_path("NAT.shp"))
>>> name_ds = "NCOVR.shp"

Extract the HR (homicide rates) data in the 70’s, 80’s and 90’s from the dataframe and make it the dependent variable for the regression. Note that the data can also be passed in the long format instead of wide format (i.e. a vector with n*t rows and a single column for the dependent variable and a matrix of dimension n*txk for the independent variables).

>>> y_name = ['HR70','HR80','HR90']
>>> y = db[y_name]

Extract RD and PS in the same time periods from the dataframe to be used as independent variables in the regression. IMPORTANT: The data must be organized in a way that all time periods of a given variable are side-by-side and in the correct time order. That is: x[:, 0:T] refers to T periods of k1, x[:, T+1:2T] refers to k2, etc. By default a vector of ones will be added to the independent variables passed in.

>>> x_names = ['RD70','RD80','RD90','PS70','PS80','PS90']
>>> x = db[x_names]

Even if we are not running a spatial model, we need to specify the spatial weights matrix that includes the spatial configuration of the observations for the diagnostics. 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.Queen.from_dataframe(db, use_index=True)
>>> w.transform = 'r'
>>> name_w = "NCOVR.gal"

We are all set with the preliminaries, we are good to run the model.

>>> reg = PanelRE(y, x, w)

Once we have run the model, we can explore a little bit the output with the command print(reg.summary).

__init__(y, x, w, slx_lags=0, slx_vars='all', time_effects=False, spat_diag=True, vm=False, name_y=None, name_x=None, name_w=None, name_ds=None, latex=False)

Methods

__init__(y, x, w[, slx_lags, slx_vars, ...])

Attributes

mean_y
sig2n
sig2n_k
std_y
utu
vm

property mean_y

property sig2n

property sig2n_k

property std_y

property utu

property vm