# coef.lgarch: Extraction methods for 'lgarch' objects In lgarch: Simulation and Estimation of Log-GARCH Models

## Description

Extraction methods for objects of class 'lgarch' (i.e. the result of estimating a log-GARCH model)

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```## S3 method for class 'lgarch' coef(object, arma = FALSE, ...) ## S3 method for class 'lgarch' fitted(object, verbose = FALSE, ...) ## S3 method for class 'lgarch' logLik(object, arma = FALSE, ...) ## S3 method for class 'lgarch' print(x, arma = FALSE, ...) ## informal method for class 'lgarch' rss(object, ...) ## S3 method for class 'lgarch' residuals(object, arma = FALSE, ...) ## S3 method for class 'lgarch' summary(object, ...) ## S3 method for class 'lgarch' vcov(object, arma = FALSE, ...) ```

## Arguments

 `object` an object of class 'lgarch' `x` an object of class 'lgarch' `verbose` logical. If FALSE (default), then only basic information is returned `arma` logical. If FALSE (default), then information relating to the log-garch model is returned. If TRUE, then information relating to the ARMA representation is returned `...` additional arguments

## Details

Note: The rss function is not a formal S3 method.

## Value

 `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 conditional standard deviations and additional information `logLik:` The value of the log-likelihood (contributions at zeros excluded) at the maximum `print:` Prints the most important parts of the estimation results `residuals:` A `zoo` object with the residuals. If arma = FALSE (default), then the standardised residuals are returned. If arma = TRUE, then the residuals of the ARMA representation is returned `rss:` A numeric; the Residual Sum of Squares of the ARMA representation `summary:` A print of the items in the `lgarch` object `vcov:` The variance-covariance matrix

## Author(s)

Genaro Sucarrat, http://www.sucarrat.net/

`lgarch`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36``` ```##simulate 500 observations w/default parameter values: set.seed(123) y <- lgarchSim(500) ##estimate a log-garch(1,1): mymod <- lgarch(y) ##print results: print(mymod) ##extract coefficients: coef(mymod) ##extract Gaussian log-likelihood (zeros excluded) of the log-garch model: logLik(mymod) ##extract the Residual Sum of Squares of the ARMA representation: rss(mymod) ##extract log-likelihood (zeros excluded) of the ARMA representation: logLik(mymod, arma=TRUE) ##extract variance-covariance matrix: vcov(mymod) ##extract and plot the fitted conditional standard deviation: sdhat <- fitted(mymod) plot(sdhat) ##extract and plot standardised residuals: zhat <- residuals(mymod) plot(zhat) ##extract and plot all the fitted series: myhat <- fitted(mymod, verbose=TRUE) plot(myhat) ```