arch.univariate.base.ARCHModelResult¶
- class arch.univariate.base.ARCHModelResult(params, param_cov, r2, resid, volatility, cov_type, dep_var, names, loglikelihood, is_pandas, optim_output, fit_start, fit_stop, model)[source]¶
Results from estimation of an ARCHModel model
- Parameters:
- params
ndarray
Estimated parameters
- param_cov{
ndarray
,None
} Estimated variance-covariance matrix of params. If none, calls method to compute variance from model when parameter covariance is first used from result
- r2
float
Model R-squared
- resid
ndarray
Residuals from model. Residuals have same shape as original data and contain nan-values in locations not used in estimation
- volatility
ndarray
Conditional volatility from model
- cov_type
str
String describing the covariance estimator used
- dep_var
Series
Dependent variable
- names
list
(str
) Model parameter names
- loglikelihood
float
Loglikelihood at estimated parameters
- is_pandasbool
Whether the original input was pandas
- optim_output
OptimizeResult
Result of log-likelihood optimization
- fit_start
int
Integer index of the first observation used to fit the model
- fit_stop
int
Integer index of the last observation used to fit the model using slice notation fit_start:fit_stop
- model
ARCHModel
The model object used to estimate the parameters
- params
- Attributes:
aic
Akaike Information Criteria
bic
Schwarz/Bayesian Information Criteria
conditional_volatility
Estimated conditional volatility
convergence_flag
scipy.optimize.minimize result flag
fit_start
Start of sample used to estimate parameters
fit_stop
End of sample used to estimate parameters
loglikelihood
Model loglikelihood
model
Model instance used to produce the fit
nobs
Number of data points used to estimate model
num_params
Number of parameters in model
optimization_result
Information about the covergence of the loglikelihood optimization
param_cov
Parameter covariance
params
Model Parameters
pvalues
Array of p-values for the t-statistics
resid
Model residuals
rsquared
R-squared
rsquared_adj
Degree of freedom adjusted R-squared
scale
The scale applied to the original data before estimating the model.
std_err
Array of parameter standard errors
std_resid
Residuals standardized by conditional volatility
tvalues
Array of t-statistics testing the null that the coefficient are 0
Methods
arch_lm_test
([lags, standardized])ARCH LM test for conditional heteroskedasticity
conf_int
([alpha])Parameter confidence intervals
forecast
([params, horizon, start, align, ...])Construct forecasts from estimated model
hedgehog_plot
([params, horizon, step, ...])Plot forecasts from estimated model
plot
([annualize, scale])Plot standardized residuals and conditional volatility
summary
()Constructs a summary of the results from a fit model.
Properties
Akaike Information Criteria
Schwarz/Bayesian Information Criteria
Estimated conditional volatility
scipy.optimize.minimize result flag
Start of sample used to estimate parameters
End of sample used to estimate parameters
Model loglikelihood
Model instance used to produce the fit
Number of data points used to estimate model
Number of parameters in model
Information about the covergence of the loglikelihood optimization
Parameter covariance
Model Parameters
Array of p-values for the t-statistics
Model residuals
R-squared
Degree of freedom adjusted R-squared
The scale applied to the original data before estimating the model.
Array of parameter standard errors
Residuals standardized by conditional volatility
Array of t-statistics testing the null that the coefficient are 0