spreg.Panel_FE_Lag¶

class spreg.Panel_FE_Lag(y, x, w, epsilon=1e-07, vm=False, name_y=None, name_x=None, name_w=None, name_ds=None)[source]¶

ML estimation of the fixed effects spatial lag model with all results and diagnostics [Elh03].

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, no constant
wpysal W object: Spatial weights object
epsilonfloat: tolerance criterion in mimimize_scalar function and inverse_product
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

Attributes:

betasarray: (k+1)x1 array of estimated coefficients (rho last)
rhofloat: estimate of spatial autoregressive coefficient
uarray: (nxt)x1 array of residuals
predyarray: (nxt)x1 array of predicted y values
ninteger: Total number of observations
tinteger: Number of time periods
kinteger: Number of variables for which coefficients are estimated (no constant, excluding the rho)
yarray: (nxt)x1 array for dependent variable
xarray: Two dimensional array with nxt rows and one column for each independent (exogenous) variable, no constant
mean_yfloat: Mean of dependent variable
std_yfloat: Standard deviation of dependent variable
vmarray: Variance covariance matrix (k+1 x k+1), all coefficients
vm1array: Variance covariance matrix (k+2 x k+2), includes sig2
sig2float: Sigma squared used in computations
logllfloat: maximized log-likelihood (including constant terms)
aicfloat: Akaike information criterion
schwarzfloat: Schwarz criterion
predy_earray: predicted values from reduced form
e_predarray: prediction errors using reduced form predicted values
pr2float: Pseudo R squared (squared correlation between y and ypred)
pr2_efloat: Pseudo R squared (squared correlation between y and ypred_e (using reduced form))
utufloat: Sum of squared residuals
std_errarray: 1x(k+1) array of standard errors of the betas
z_statlist of tuples: z statistic; each tuple contains the pair (statistic, p-value), where each is a float
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
titlestr: Name of the regression method used

Examples

>>> import numpy as np
>>> import libpysal
>>> import spreg
>>> nat = libpysal.examples.load_example("NCOVR")
>>> db = libpysal.io.open(nat.get_path("NAT.dbf"), "r")
>>> nat_shp = libpysal.examples.get_path("NAT.shp")
>>> w = libpysal.weights.Queen.from_shapefile(nat_shp)
>>> w.transform = 'r'
>>> name_y = ["HR70", "HR80", "HR90"]
>>> y = np.array([db.by_col(name) for name in name_y]).T
>>> name_x = ["RD70", "RD80", "RD90", "PS70", "PS80", "PS90"]
>>> x = np.array([db.by_col(name) for name in name_x]).T
>>> fe_lag = spreg.Panel_FE_Lag(y, x, w, name_y=name_y, name_x=name_x, name_ds="NAT")
Warning: Assuming panel is in wide format, i.e. y[:, 0] refers to T0, y[:, 1] refers to T1, etc.
Similarly, assuming x[:, 0:T] refers to T periods of k1, x[:, T+1:2T] refers to k2, etc.
>>> np.around(fe_lag.betas, decimals=4)
array([[ 0.8006],
       [-2.6004],
       [ 0.1903]])

__init__(y, x, w, epsilon=1e-07, vm=False, name_y=None, name_x=None, name_w=None, name_ds=None)[source]¶

Methods

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

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¶