6.6.4.2.1. statsmodels.sandbox.panel.random_panel.PanelSample

class statsmodels.sandbox.panel.random_panel.PanelSample(nobs, k_vars, n_groups, exog=None, within=True, corr_structure=<function eye>, corr_args=(), scale=1, seed=None)

data generating process for panel with within correlation

allows various within correlation structures, but no random intercept yet

Parameters:

nobs : int

total number of observations

k_vars : int

number of explanatory variables to create in exog, including constant

n_groups int

number of groups in balanced sample

exog : None or ndarray

default is None, in which case a exog is created

within : bool

If True (default), then the exog vary within a group. If False, then only variation across groups is used. TODO: this option needs more work

corr_structure : ndarray or ??

Default is np.eye.

corr_args : tuple

arguments for the corr_structure

scale : float

scale of noise, standard deviation of normal distribution

seed : None or int

If seed is given, then this is used to create the random numbers for the sample.

Notes

The behavior for panel robust covariance estimators seems to differ by a large amount by whether exog have mostly within group or across group variation. I do not understand why this should be the case from the theory, and this would warrant more investigation.

This is just used in one example so far and needs more usage to see what will be useful to add.

__init__(nobs, k_vars, n_groups, exog=None, within=True, corr_structure=<function eye>, corr_args=(), scale=1, seed=None)

6.6.4.2.1.1. Methods

__init__(nobs, k_vars, n_groups[, exog, ...])
generate_panel()	generate endog for a random panel dataset with within correlation
get_y_true()