[Returnanalytics-commits] r3596 - pkg/FactorAnalytics/R
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Wed Feb 4 01:29:05 CET 2015
Author: chenyian
Date: 2015-02-04 01:29:05 +0100 (Wed, 04 Feb 2015)
New Revision: 3596
Removed:
pkg/FactorAnalytics/R/fitTsfmTiming.r
Log:
delete fitTsfmTiming.r.
Deleted: pkg/FactorAnalytics/R/fitTsfmTiming.r
===================================================================
--- pkg/FactorAnalytics/R/fitTsfmTiming.r 2015-02-04 00:26:42 UTC (rev 3595)
+++ pkg/FactorAnalytics/R/fitTsfmTiming.r 2015-02-04 00:29:05 UTC (rev 3596)
@@ -1,169 +0,0 @@
-#' @title Fit a time serie market timing factor model using time series regression
-#'
-#' @description This is a wrapper function to fits a time series market timing factor model for one
-#' or more asset returns or excess returns using time series regression.
-#' Users can choose between ordinary least squares-OLS, discounted least
-#' squares-DLS (or) robust regression. Several variable selection options
-#' including Stepwise, Subsets, Lars are available as well. An object of class
-#' \code{"tsfm"} is returned.
-#'
-#' @details
-#' Typically, factor models are fit using excess returns. \code{rf.name} gives
-#' the option to supply a risk free rate variable to subtract from each asset
-#' return and factor to compute excess returns.
-#'
-#' Estimation method "OLS" corresponds to ordinary least squares using
-#' \code{\link[stats]{lm}}, "DLS" is discounted least squares (weighted least
-#' squares with exponentially declining weights that sum to unity), and,
-#' "Robust" is robust regression (using \code{\link[robust]{lmRob}}).
-#'
-#' If \code{variable.selection="none"}, uses all the factors and performs no
-#' variable selection. Whereas, "stepwise" performs traditional stepwise
-#' LS or Robust regression (using \code{\link[stats]{step}} or
-#' \code{\link[robust]{step.lmRob}}), that starts from the initial set of
-#' factors and adds/subtracts factors only if the regression fit, as measured
-#' by the Bayesian Information Criterion (BIC) or Akaike Information Criterion
-#' (AIC), improves. And, "subsets" enables subsets selection using
-#' \code{\link[leaps]{regsubsets}}; chooses the best performing subset of any
-#' given size or within a range of subset sizes. Different methods such as
-#' exhaustive search (default), forward or backward stepwise, or sequential
-#' replacement can be employed.See \code{\link{fitTsfm.control}} for more
-#' details on the control arguments.
-#'
-#' \code{variable.selection="lars"} corresponds to least angle regression
-#' using \code{\link[lars]{lars}} with variants "lasso" (default), "lar",
-#' "stepwise" or "forward.stagewise". Note: If \code{variable.selection="lars"},
-#' \code{fit.method} will be ignored.
-#'
-#' Market timing accounts for
-#' the price movement of the general stock market relative to fixed income
-#' securities. It includes
-#' $down.market = max(0, R_f-R_m)$ as a factor, following Henriksson & Merton
-#' (1981). The coefficient of this down-market factor can be interpreted as the
-#' number of "free" put options on the market provided by the manager's
-#' market-timings kills.
-#'
-#' \subsection{Data Processing}{
-#'
-#' Note about NAs: Before model fitting, incomplete cases are removed for
-#' every asset (return data combined with respective factors' return data)
-#' using \code{\link[stats]{na.omit}}. Otherwise, all observations in
-#' \code{data} are included.
-#'
-#' Note about \code{asset.names} and \code{factor.names}: Spaces in column
-#' names of \code{data} will be converted to periods as \code{fitTsfm} works
-#' with \code{xts} objects internally and colnames won't be left as they are.
-#' }
-#'
-#' @param asset.names vector containing names of assets, whose returns or
-#' excess returns are the dependent variable.
-#' @param factor.names vector containing names of the macroeconomic factors.
-#' @param mkt.name name of the column for market excess returns (Rm-Rf); this
-#' is necessary to add market timing factors. Default is NULL.
-#' @param rf.name name of the column of risk free rate variable to calculate
-#' excess returns for all assets (in \code{asset.names}) and factors (in
-#' \code{factor.names}). Default is NULL, and no action is taken.
-#' @param data vector, matrix, data.frame, xts, timeSeries or zoo object
-#' containing column(s) named in \code{asset.names}, \code{factor.names} and
-#' optionally, \code{mkt.name} and \code{rf.name}.
-#' @param fit.method the estimation method, one of "OLS", "DLS" or "Robust".
-#' See details. Default is "OLS".
-#' @param variable.selection the variable selection method, one of "none",
-#' "stepwise","subsets","lars". See details. Default is "none".
-#' \code{mkt.name} is required if any of these options are to be implemented.
-#' @param control list of control parameters. The default is constructed by
-#' the function \code{\link{fitTsfm.control}}. See the documentation for
-#' \code{\link{fitTsfm.control}} for details.
-#' @param ... arguments passed to \code{\link{fitTsfm.control}}
-#'
-#' @return fitTsfm returns an object of class \code{"tsfm"} for which
-#' \code{print}, \code{plot}, \code{predict} and \code{summary} methods exist.
-#'
-#' The generic accessor functions \code{coef}, \code{fitted} and
-#' \code{residuals} extract various useful features of the fit object.
-#' Additionally, \code{fmCov} computes the covariance matrix for asset returns
-#' based on the fitted factor model
-#'
-#' An object of class \code{"tsfm"} is a list containing the following
-#' components:
-#' \item{asset.fit}{list of fitted objects for each asset. Each object is of
-#' class \code{lm} if \code{fit.method="OLS" or "DLS"}, class \code{lmRob} if
-#' the \code{fit.method="Robust"}, or class \code{lars} if
-#' \code{variable.selection="lars"}.}
-#' \item{alpha}{length-N vector of estimated alphas.}
-#' \item{beta}{N x K matrix of estimated betas.}
-#' \item{r2}{length-N vector of R-squared values.}
-#' \item{resid.sd}{length-N vector of residual standard deviations.}
-#' \item{fitted}{xts data object of fitted values; iff
-#' \code{variable.selection="lars"}}
-#' \item{call}{the matched function call.}
-#' \item{data}{xts data object containing the assets and factors.}
-#' \item{asset.names}{asset.names as input.}
-#' \item{factor.names}{factor.names as input.}
-#' \item{fit.method}{fit.method as input.}
-#' \item{variable.selection}{variable.selection as input.}
-#' Where N is the number of assets, K is the number of factors and T is the
-#' number of time periods.
-#'
-#' @author Yi-An Chen.
-#'
-#' @references
-#' Christopherson, J. A., Carino, D. R., & Ferson, W. E. (2009). Portfolio
-#' performance measurement and benchmarking. McGraw Hill Professional.
-#'
-#' Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle
-#' regression. The Annals of statistics, 32(2), 407-499.
-#'
-#' Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Friedman, J., &
-#' Tibshirani, R. (2009). The elements of statistical learning (Vol. 2, No. 1).
-#' New York: Springer.
-#'
-#' Henriksson, R. D., & Merton, R. C. (1981). On market timing and investment
-#' performance. II. Statistical procedures for evaluating forecasting skills.
-#' Journal of business, 513-533.
-#'
-#' Treynor, J., & Mazuy, K. (1966). Can mutual funds outguess the market.
-#' Harvard business review, 44(4), 131-136.
-#'
-#' @seealso The \code{tsfm} methods for generic functions:
-#' \code{\link{plot.tsfm}}, \code{\link{predict.tsfm}},
-#' \code{\link{print.tsfm}} and \code{\link{summary.tsfm}}.
-#'
-#' And, the following extractor functions: \code{\link[stats]{coef}},
-#' \code{\link[stats]{fitted}}, \code{\link[stats]{residuals}},
-#' \code{\link{fmCov}}, \code{\link{fmSdDecomp}}, \code{\link{fmVaRDecomp}}
-#' and \code{\link{fmEsDecomp}}.
-#'
-#' \code{\link{paFm}} for Performance Attribution.
-#'
-#' @examples
-#' # load data from the database
-#' data(managers)
-#'
-#' # example: Market-timing factors with robust fit
-#' fit <- fitTsfmTiming(asset.names=colnames(managers[,(1:6)]), factor.names=NULL,
-#' mkt.name="SP500.TR",rf.name="US.3m.TR",data=managers)
-#' summary(fit)
-#' fitted(fit)
-#'
-#' @importFrom PerformanceAnalytics checkData
-#' @importFrom robust lmRob step.lmRob
-#' @importFrom leaps regsubsets
-#' @importFrom lars lars cv.lars
-#'
-#' @export
-
-fitTsfmTiming <- function(asset.names, factor.names=NULL, mkt.name=NULL, rf.name=NULL,
- data=data, fit.method=c("OLS","DLS","Robust"),
- variable.selection=c("none","stepwise","subsets","lars"), control=fitTsfm.control(...),...) {
- if (is.null(mkt.name)){
- stop("Missing argument: mkt.name has to be specified for market timing model.")
- }
-
- factor.names <- union(factor.names,mkt.name)
-
- fit.Timing <- fitTsfm(asset.names=asset.names,factor.names=factor.names,mkt.name=mkt.name,rf.name=rf.name,
- data=data,fit.method=fit.method,variable.selection=variable.selection,control=control,mkt.timing="HM")
-
-return(fit.Timing)
-}
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