statsmodels.tsa.ardl.UECM.fit¶
- UECM.fit(*, cov_type='nonrobust', cov_kwds=None, use_t=True)[source]¶
Estimate the model parameters.
- Parameters:
- cov_type
str
The covariance estimator to use. The most common choices are listed below. Supports all covariance estimators that are available in
OLS.fit
.‘nonrobust’ - The class OLS covariance estimator that assumes homoskedasticity.
‘HC0’, ‘HC1’, ‘HC2’, ‘HC3’ - Variants of White’s (or Eiker-Huber-White) covariance estimator. HC0 is the standard implementation. The other make corrections to improve the finite sample performance of the heteroskedasticity robust covariance estimator.
‘HAC’ - Heteroskedasticity-autocorrelation robust covariance estimation. Supports cov_kwds.
maxlags integer (required) : number of lags to use.
- kernel callable or str (optional)kernel
currently available kernels are [‘bartlett’, ‘uniform’], default is Bartlett.
- use_correction bool (optional)If true, use small sample
correction.
- cov_kwds
dict
,optional
A dictionary of keyword arguments to pass to the covariance estimator. nonrobust and HC# do not support cov_kwds.
- use_tbool,
optional
A flag indicating that inference should use the Student’s t distribution that accounts for model degree of freedom. If False, uses the normal distribution. If None, defers the choice to the cov_type. It also removes degree of freedom corrections from the covariance estimator when cov_type is ‘nonrobust’.
- cov_type
- Returns:
UECMResults
Estimation results.
See also
statsmodels.tsa.ardl.ARDL
Autoregressive distributed lag model estimation
statsmodels.tsa.ar_model.AutoReg
Ordinary Least Squares estimation.
statsmodels.regression.linear_model.OLS
Ordinary Least Squares estimation.
statsmodels.regression.linear_model.RegressionResults
See
get_robustcov_results
for a detailed list of available covariance estimators and options.
Notes
Use
OLS
to estimate model parameters and to estimate parameter covariance.