coef.tegarch {betategarch} | R Documentation |
Extraction methods for objects of class 'tegarch' (i.e. the result of estimating a Beta-Skew-t-EGARCH model)
## S3 method for class 'tegarch' coef(object, ...) ## S3 method for class 'tegarch' fitted(object, verbose = FALSE, ...) ## S3 method for class 'tegarch' logLik(object, ...) ## S3 method for class 'tegarch' print(x, ...) ## S3 method for class 'tegarch' residuals(object, standardised = TRUE, ...) ## S3 method for class 'tegarch' summary(object, verbose = FALSE, ...) ## S3 method for class 'tegarch' vcov(object, ...)
object |
an object of class 'tegarch' |
x |
an object of class 'tegarch' |
verbose |
logical. If FALSE (default) then only basic information is returned |
standardised |
logical. If TRUE (default) then the standardised residuals are returned. If FALSE then the scaled (by sigma) residuals are returned |
... |
additional arguments |
Empty
coef: |
A numeric vector containing the parameter estimates |
fitted: |
A zoo object. If verbose=FALSE (default), then the zoo object is a vector containing the fitted conditional standard deviations. If verbose = TRUE, then the zoo object is a matrix containing the return series y, fitted scale (sigma), fitted conditional standard deviation (stdev), fitted log-scale (lambda), dynamic component(s) (lambdadagger in the 1-component specification, lambda1dagger and lambda2dagger in the 2-compoment specification), the score (u), scaled residuals (epsilon) and standardised residuals (residstd) |
logLik: |
The value of the log-likelihood at the maximum |
print: |
Prints the most important parts of the estimation results |
residuals: |
A zoo object. If standardised = TRUE (default), then the zoo object is a vector with the standardised residuals. If standardised = FALSE, then the zoo vector contains the scaled residuals |
summary: |
A list. If verbose = FALSE, then only the most important entries are returned. If verbose = TRUE, then all entries apart from the 1st. (the y series) is returned |
vcov: |
The variance-covariance matrix of the estimated coefficents. The matrix is obtained by inverting the numerically estimated Hessian |
Genaro Sucarrat, http://www.sucarrat.net/
Fernandez and Steel (1998), 'On Bayesian Modeling of Fat Tails and Skewness', Journal of the American Statistical Association 93, pp. 359-371.
Harvey and Sucarrat (2014), 'EGARCH models with fat tails, skewness and leverage'. Computational Statistics and Data Analysis 76, pp. 320-338.
Sucarrat (2013), 'betategarch: Simulation, Estimation and Forecasting of First-Order Beta-Skew-t-EGARCH models'. The R Journal (Volume 5/2), pp. 137-147.
tegarch
, coef
, fitted
, logLik
, predict
, predict.tegarch
, print
, summary
, vcov
#simulate 500 observations from model with default parameter values: set.seed(123) y <- tegarchSim(500) #estimate and store as 'mymodel': mymod <- tegarch(y) #print estimation result: print(mymod) #extract coefficients: coef(mymod) #extract log-likelihood: logLik(mymod) #plot fitted conditional standard deviations: plot(fitted(mymod)) #plot all the fitted series: plot(fitted(mymod, verbose=TRUE)) #histogram of standardised residuals: hist(residuals(mymod))