summary.tsfm | R Documentation |
summary
method for object of class tsfm
.
Returned object is of class summary.tsfm
.
## S3 method for class 'tsfm'
summary(object, se.type = c("Default", "HC", "HAC"), ...)
## S3 method for class 'summary.tsfm'
print(x, digits = 3, labels = TRUE, ...)
object |
an object of class |
se.type |
one of "Default", "HC" or "HAC" option for computing HC/HAC standard errors and t-statistics. Default is "Default". If "HC" or "HAC" options are selected, you will need to first load the suggested 'lmtest' package. |
... |
futher arguments passed to or from other methods. |
x |
an object of class |
digits |
number of significants digits to use when printing. Default is 3. |
labels |
option to print labels and legend in the summary. Default is
|
The default summary
method for a fitted lm
object
computes the standard errors and t-statistics under the assumption of
homoskedasticty. Argument se.type
gives the option to compute
heteroskedasticity-consistent (HC) or
heteroskedasticity-autocorrelation-consistent (HAC) standard errors and
t-statistics using coeftest
. This option is meaningful
only if fit.method = "LS" or "DLS"
.
Standard errors are currently not available for
variable.selection="lars"
as there seems to be no consensus on a
statistically valid method of calculating standard errors for the lasso
predictions.
Returns an object of class summary.tsfm
.
The print method for class summary.tsfm
outputs the call,
coefficients (with standard errors and t-statistics), r-squared and
residual volatilty (under the homoskedasticity assumption) for all assets.
Object of class summary.tsfm
is a list of length N + 2 containing:
call |
the function call to |
se.type |
standard error type as input |
sum.list |
list of summaries of the N fit objects (of class |
Sangeetha Srinivasan & Yi-An Chen.
fitTsfm
, summary.lm
# load data
data(managers, package = 'PerformanceAnalytics')
fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
factor.names=colnames(managers[,7:9]),
data=managers)
# summary of factor model fit for all assets
summary(fit)
# Summary of factor model, using lmtest
library(lmtest)
summary(fit, "HAC")
# summary of lm fit for a single asset
summary(fit$asset.fit[[1]])
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