Extraction functions for 'gets' objects

Extraction functions for objects of class 'gets'

Usage

# S3 method for class 'gets'
coef(object, spec=NULL, ...)
  # S3 method for class 'gets'
fitted(object, spec=NULL, ...)
  # S3 method for class 'gets'
logLik(object, ...)
  # S3 method for class 'gets'
plot(x, spec=NULL, col=c("red","blue"),
    lty=c("solid","solid"), lwd=c(1,1), ...)
  # S3 method for class 'gets'
predict(object, spec=NULL, n.ahead=12, newmxreg=NULL,
    newvxreg=NULL, newindex=NULL, n.sim=5000, innov=NULL, probs=NULL,
    ci.levels=NULL, quantile.type=7, return=TRUE, verbose=FALSE, plot=NULL,
    plot.options=list(), ...)
  # S3 method for class 'gets'
print(x, signif.stars=TRUE, ...)
  # S3 method for class 'gets'
residuals(object, std=NULL, ...)
  # S3 method for class 'gets'
sigma(object, ...)
  # S3 method for class 'gets'
summary(object, ...)
  # S3 method for class 'gets'
vcov(object, spec=NULL, ...)

Arguments

object: an object of class 'gets'
x: an object of class 'gets'
spec: NULL, "mean", "variance" or, in some instances, "both". When NULL is a valid value, then it is automatically determined whether information pertaining to the mean or variance specification should be returned
signif.stars: logical. If TRUE, then p-values are additionally encoded visually, see printCoefmat
std: logical. If FALSE (default), then the mean residuals are returned. If TRUE, then the standardised residuals are returned
n.ahead: integer that determines how many steps ahead predictions should be generated (the default is 12)
newmxreg: a matrix of n.ahead rows and NCOL(mxreg) columns with the out-of-sample values of the mxreg regressors
newvxreg: a matrix of n.ahead rows and NCOL(vxreg) columns with the out-of-sample values of the vxreg regressors
newindex: NULL (default) or the date-index for the zoo object returned by predict.arx. If NULL, then the function uses the in-sample index to generate the out-of-sample index
n.sim: integer, the number of replications used for the generation of the forecasts
innov: NULL (default) or a vector of length n.ahead * n.sim containing the standardised errors (that is, zero mean and unit variance) used for the forecast simulations. If NULL, then a classica bootstrap procedure is used to draw from the standardised in-sample residuals
probs: NULL (default) or a vector with the quantile-levels (values strictly between 0 and 1) of the forecast distribution. If NULL, then no quantiles are returned unless ci.levels is non-NULL
ci.levels: NULL (default) or a vector with the confidence levels (expressed as values strictly between 0 and 1) of the forecast distribution. The upper and lower values of the confidence interval(s) are returned as quantiles
quantile.type: an integer between 1 and 9 that selects which algorithm to be used in computing the quantiles, see the argument type in quantile
return: logical. If TRUE (default), then the out-of-sample predictions are returned. The value FALSE, which does not return the predictions, may be of interest if only a prediction plot is of interest
verbose: logical with default FALSE. If TRUE, then additional information (typically the quantiles and/or the simulated series) used in the generation of forecasts is returned. If FALSE, then only the forecasts are returned
plot: NULL (default) or logical. If NULL, then the value set by options$plot (see options) determines whether a plot is produced or not. If TRUE, then the out-of-sample forecasts are plotted.
plot.options: a list of options related to the plotting of forecasts, see 'Details'
col: colours of fitted (default=red) and actual (default=blue) lines
lty: types of fitted (default=solid) and actual (default=solid) lines
lwd: widths of fitted (default=1) and actual (default=1) lines
...: additional arguments

Details

The plot.options argument is a list that controls the prediction plot, see 'Details' in predict.arx

Value

coef:: a numeric vector containing parameter estimates
fitted:: a zoo object with fitted values
logLik:: a numeric, the log-likelihood (normal density)
plot:: a plot of the fitted values and the residuals
predict:: a vector of class zoo containing the out-of-sample forecasts, or a matrix of class zoo containing the out-of-sample forecasts together with prediction-quantiles, or - if return=FALSE - NULL
print:: a print of the estimation results
residuals:: a zoo object with the residuals
sigma:: the regression standard error ('SE of regression')
summary:: a print of the items in the gets object
vcov:: a variance-covariance matrix

Author

Felix Pretis, http://www.felixpretis.org/
James Reade, https://sites.google.com/site/jjamesreade/
Moritz Schwarz, https://www.inet.ox.ac.uk/people/moritz-schwarz/
Genaro Sucarrat, http://www.sucarrat.net/

Examples

##Simulate from an AR(1):
set.seed(123)
y <- arima.sim(list(ar=0.4), 100)

##Simulate four independent Gaussian regressors:
xregs <- matrix(rnorm(4*100), 100, 4)

##estimate an AR(2) with intercept and four conditioning
##regressors in the mean, and a log-ARCH(3) in the variance:
mymod <- arx(y, mc=TRUE, ar=1:2, mxreg=xregs, arch=1:3)

##General-to-Specific (GETS) model selection of the mean:
meanmod <- getsm(mymod)
#> 
#> GUM mean equation:
#> 
#>        reg.no. keep      coef std.error  t-stat   p-value    
#> mconst       1    0  0.043088  0.094883  0.4541 0.6508240    
#> ar1          2    0  0.406777  0.107385  3.7880 0.0002723 ***
#> ar2          3    0 -0.014965  0.110010 -0.1360 0.8920956    
#> mxreg1       4    0 -0.071650  0.096881 -0.7396 0.4614659    
#> mxreg2       5    0 -0.043913  0.101653 -0.4320 0.6667745    
#> mxreg3       6    0 -0.033421  0.095433 -0.3502 0.7269975    
#> mxreg4       7    0 -0.079345  0.094225 -0.8421 0.4019547    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> GUM log-variance equation:
#> 
#>             coef std.error  t-stat p-value
#> vconst -0.185723  0.339150  0.2999  0.5840
#> arch1   0.071754  0.105315  0.6813  0.4974
#> arch2  -0.091189  0.105499 -0.8644  0.3897
#> arch3   0.052295  0.105789  0.4943  0.6223
#> 
#> Diagnostics:
#> 
#>                   Chi-sq df p-value
#> Ljung-Box AR(3)   2.9699  3 0.39629
#> Ljung-Box ARCH(4) 4.3880  4 0.35603
#> 
#> 6 path(s) to search
#> Searching: 
#> 1 
#> 2 
#> 3 
#> 4 
#> 5 
#> 6 
#> 
#>   Path 1: 1 3 6 5 4 7 
#>   Path 2: 3 6 5 1 4 7 
#>   Path 3: 4 3 6 5 1 7 
#>   Path 4: 5 3 6 1 4 7 
#>   Path 5: 6 3 5 1 4 7 
#>   Path 6: 7 3 6 1 5 4 
#> 
#> Terminal models:
#> 
#>                 info(sc)     logl  n k
#> spec 1 (1-cut): 2.616031 -125.893 98 1
#> 
#> Retained regressors (final model):
#> 
#>   ar1 

##General-to-Specific (GETS) model selection of the variance:
varmod <- getsv(mymod)
#> GUM log-variance equation:
#> 
#>        reg.no. keep      coef std.error  t-stat p-value
#> vconst       1    1 -0.185723  0.339150  0.2999  0.5840
#> arch1        2    0  0.071754  0.105315  0.6813  0.4974
#> arch2        3    0 -0.091189  0.105499 -0.8644  0.3897
#> arch3        4    0  0.052295  0.105789  0.4943  0.6223
#> 
#> Diagnostics:
#> 
#>                   Chi-sq df p-value
#> Ljung-Box AR(3)   2.9699  3 0.39629
#> Ljung-Box ARCH(4) 4.3880  4 0.35603
#> 
#> 3 path(s) to search
#> Searching: 
#> 1 
#> 2 
#> 3 
#> 
#>   Path 1: 2 4 3 
#>   Path 2: 3 4 2 
#>   Path 3: 4 2 3 
#> 
#> Terminal models:
#> 
#>                 info(sc)      logl  n k
#> spec 1 (1-cut): 2.675753 -124.8213 95 1
#> 
#> Retained regressors (final model):
#> 
#>   vconst 

##print results:
print(meanmod)
#> 
#> Date: Sat Jul 27 15:29:48 2024 
#> Dependent var.: y 
#> Method: Ordinary Least Squares (OLS) 
#> Variance-Covariance: Ordinary 
#> No. of observations (mean eq.): 98 
#> Sample: 3 to 100 
#> 
#> SPECIFIC mean equation:
#> 
#>         coef std.error t-stat   p-value    
#> ar1 0.395234  0.095397  4.143 7.328e-05 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> SPECIFIC log-variance equation:
#> 
#>            coef std.error t-stat p-value
#> vconst 0.132172  0.314689 0.1764  0.6745
#> arch1  0.168115  0.105649 1.5913  0.1150
#> arch2  0.041319  0.107019 0.3861  0.7003
#> arch3  0.012057  0.108241 0.1114  0.9116
#> 
#> Diagnostics and fit:
#> 
#>                   Chi-sq df p-value
#> Ljung-Box AR(3)   2.4359  3  0.4870
#> Ljung-Box ARCH(4) 6.6894  4  0.1532
#>                            
#> SE of regression    0.90884
#> R-squared           0.14564
#> Log-lik.(n=95)   -125.89303
print(varmod)
#> 
#> Date: Sat Jul 27 15:29:48 2024 
#> Method: Ordinary Least Squares (OLS) 
#> No. of observations (variance eq.): 95 
#> Sample: 6 to 100 
#> 
#> SPECIFIC log-variance equation:
#> 
#>            coef std.error t-stat p-value
#> vconst -0.21006   0.21965 0.9146  0.3389
#> 
#> Diagnostics and fit:
#> 
#>                   Chi-sq df p-value
#> Ljung-Box AR(3)   3.9427  3  0.2677
#> Ljung-Box ARCH(4) 5.2478  4  0.2628
#>                           
#> SE of regression    0.9003
#> Log-lik.(n=95)   -124.8213

##plot the fitted vs. actual values, and the residuals:
plot(meanmod)

plot(varmod)


##generate and plot predictions of the mean:
predict(meanmod, plot=TRUE)

#>           101           102           103           104           105 
#> -8.149836e-01 -3.221089e-01 -1.273082e-01 -5.031648e-02 -1.988676e-02 
#>           106           107           108           109           110 
#> -7.859914e-03 -3.106501e-03 -1.227793e-03 -4.852651e-04 -1.917931e-04 
#>           111           112 
#> -7.580305e-05 -2.995991e-05 

##print the entries of object 'gets':
summary(meanmod)
#>                      Length Class      Mode     
#> date                  1     -none-     character
#> gets.type             1     -none-     character
#> time.started          1     -none-     character
#> time.finished         1     -none-     character
#> call                  2     -none-     call     
#> gum.mean              6     data.frame list     
#> gum.variance          4     data.frame list     
#> gum.diagnostics       6     -none-     numeric  
#> no.of.estimations     1     -none-     numeric  
#> paths                 6     -none-     list     
#> terminals             1     -none-     list     
#> terminals.results     4     -none-     numeric  
#> best.terminal         1     -none-     numeric  
#> specific.spec         1     -none-     numeric  
#> version               1     -none-     character
#> aux                  21     -none-     list     
#> n                     1     -none-     numeric  
#> k                     1     -none-     numeric  
#> df                    1     -none-     numeric  
#> coefficients          1     -none-     numeric  
#> mean.fit             98     zoo        numeric  
#> residuals            98     zoo        numeric  
#> rss                   1     -none-     numeric  
#> sigma2                1     -none-     numeric  
#> vcov.mean             1     -none-     numeric  
#> logl                  1     -none-     numeric  
#> Elnz2                 1     -none-     numeric  
#> vcov.var              9     -none-     numeric  
#> var.fit              98     zoo        numeric  
#> std.residuals        98     zoo        numeric  
#> ustar.residuals      98     zoo        numeric  
#> variance.results      4     data.frame list     
#> mean.results          4     data.frame list     
#> specific.diagnostics  6     -none-     numeric  
summary(varmod)
#>                      Length Class      Mode     
#> date                  1     -none-     character
#> gets.type             1     -none-     character
#> time.started          1     -none-     character
#> time.finished         1     -none-     character
#> call                  2     -none-     call     
#> gum.mean              4     data.frame list     
#> gum.variance          6     data.frame list     
#> gum.diagnostics       6     -none-     numeric  
#> no.of.estimations     1     -none-     numeric  
#> paths                 3     -none-     list     
#> terminals             1     -none-     list     
#> terminals.results     4     -none-     numeric  
#> best.terminal         1     -none-     numeric  
#> specific.spec         1     -none-     numeric  
#> version               1     -none-     character
#> aux                  18     -none-     list     
#> n                     1     -none-     numeric  
#> k                     1     -none-     numeric  
#> df                    1     -none-     numeric  
#> mean.fit             95     zoo        numeric  
#> residuals            95     zoo        numeric  
#> rss                   1     -none-     numeric  
#> sigma2                1     -none-     numeric  
#> logl                  1     -none-     numeric  
#> Elnz2                 1     -none-     numeric  
#> vcov.var              0     -none-     numeric  
#> var.fit              95     zoo        numeric  
#> std.residuals        95     zoo        numeric  
#> ustar.residuals      95     zoo        numeric  
#> variance.results      4     data.frame list     
#> specific.diagnostics  6     -none-     numeric  

##extract coefficients of the simplified (specific) model:
coef(meanmod) #mean spec
#>       ar1 
#> 0.3952335 
coef(varmod) #variance spec
#>    vconst     Elnz2 
#> -0.210060 -1.178012 

##extract log-likelihood:
logLik(mymod)
#> 'log Lik.' -123.8917 (df=7)

##extract coefficient-covariance matrix of simplified
##(specific) model:
vcov(meanmod) #mean spec
#>             ar1
#> ar1 0.009100632
vcov(varmod) #variance spec
#> <0 x 0 matrix>

##extract and plot the fitted values:
mfit <- fitted(meanmod) #mean fit
plot(mfit)

vfit <- fitted(varmod) #variance fit
plot(vfit)


##extract and plot residuals:
epshat <- residuals(meanmod)
plot(epshat)


##extract and plot standardised residuals:
zhat <- residuals(varmod)
plot(zhat)

Extraction functions for 'gets' objects

Usage

Arguments

Details

Value

Author

See also

Examples