- summary : string
Summary of regression results and diagnostics (note: use in
conjunction with the print command)
- betas : array
kx1 array of estimated coefficients
- u : array
nx1 array of residuals
- predy : array
nx1 array of predicted y values
- n : integer
Number of observations
- k : integer
Number of variables for which coefficients are estimated
(including the constant)
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- y : array
nx1 array for dependent variable
- x : array
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)
- robust : string
Adjustment for robust standard errors
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- mean_y : float
Mean of dependent variable
- std_y : float
Standard deviation of dependent variable
- vm : array
Variance covariance matrix (kxk)
- r2 : float
R squared
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- ar2 : float
Adjusted R squared
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- utu : float
Sum of squared residuals
- sig2 : float
Sigma squared used in computations
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- sig2ML : float
Sigma squared (maximum likelihood)
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- f_stat : tuple
Statistic (float), p-value (float)
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- logll : float
Log likelihood
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- aic : float
Akaike information criterion
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- schwarz : float
Schwarz information criterion
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- std_err : array
1xk array of standard errors of the betas
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- t_stat : list of tuples
t 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)
- mulColli : float
Multicollinearity condition number
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- jarque_bera : dictionary
‘jb’: Jarque-Bera statistic (float); ‘pvalue’: p-value
(float); ‘df’: degrees of freedom (int)
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- breusch_pagan : dictionary
‘bp’: Breusch-Pagan statistic (float); ‘pvalue’: p-value
(float); ‘df’: degrees of freedom (int)
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- koenker_bassett : dictionary
‘kb’: Koenker-Bassett statistic (float); ‘pvalue’:
p-value (float); ‘df’: degrees of freedom (int)
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- white : dictionary
‘wh’: White statistic (float); ‘pvalue’: p-value (float);
‘df’: degrees of freedom (int)
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- lm_error : tuple
Lagrange multiplier test for spatial error model; 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)
- lm_lag : tuple
Lagrange multiplier test for spatial lag model; 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)
- rlm_error : tuple
Robust lagrange multiplier test for spatial error model;
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)
- rlm_lag : tuple
Robust lagrange multiplier test for spatial lag model;
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)
- lm_sarma : tuple
Lagrange multiplier test for spatial SARMA model; 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)
- moran_res : tuple
Moran’s I for the residuals; tuple containing the triple
(Moran’s I, standardized Moran’s I, p-value)
- name_y : string
Name of dependent variable for use in output
- name_x : list of strings
Names of independent variables for use in output
- name_w : string
Name of weights matrix for use in output
- name_gwk : string
Name of kernel weights matrix for use in output
- name_ds : string
Name of dataset for use in output
- name_regimes : string
Name of regime variable for use in the output
- title : string
Name of the regression method used
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- sig2n : float
Sigma squared (computed with n in the denominator)
- sig2n_k : float
Sigma squared (computed with n-k in the denominator)
- xtx : float
X’X
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- xtxi : float
(X’X)^-1
Only available in dictionary ‘multi’ when multiple regressions
(see ‘multi’ below for details)
- regimes : list
List of n values with the mapping of each
observation to a regime. Assumed to be aligned with ‘x’.
- constant_regi: [‘one’, ‘many’]
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
- cols2regi : list, ‘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.
- kr : int
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)
- kf : int
Number of variables/columns to be considered fixed or
global across regimes and hence only obtain one parameter
estimate
- nr : int
Number of different regimes in the ‘regimes’ list
- multi : dictionary
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