[Returnanalytics-commits] r3615 - in pkg/FactorAnalytics: . R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Mon Mar 16 06:15:22 CET 2015
Author: pragnya
Date: 2015-03-16 06:15:22 +0100 (Mon, 16 Mar 2015)
New Revision: 3615
Modified:
pkg/FactorAnalytics/DESCRIPTION
pkg/FactorAnalytics/R/fitTsfm.R
pkg/FactorAnalytics/R/fitTsfm.control.R
pkg/FactorAnalytics/man/fitTsfm.Rd
pkg/FactorAnalytics/man/fitTsfm.control.Rd
Log:
Added control parameter efficiency for lm.Rob
Modified: pkg/FactorAnalytics/DESCRIPTION
===================================================================
--- pkg/FactorAnalytics/DESCRIPTION 2015-03-16 04:25:23 UTC (rev 3614)
+++ pkg/FactorAnalytics/DESCRIPTION 2015-03-16 05:15:22 UTC (rev 3615)
@@ -1,36 +1,36 @@
-Package: factorAnalytics
-Type: Package
-Title: Factor Analytics
-Version: 2.0.13
-Date: 2015-03-15
-Author: Eric Zivot, Sangeetha Srinivasan and Yi-An Chen
-Maintainer: Sangeetha Srinivasan <sangee at uw.edu>
-Description: An R package for the estimation and risk analysis of linear factor
- models for asset returns and portfolios. It contains model fitting methods
- for the three major types of factor models: time series (or, macroeconomic)
- factor model, fundamental factor model and statistical factor model. They
- allow for different types of distributions to be specified for modeling the
- fat-tailed behavior of financial returns, including Edgeworth expansions.
- Risk analysis measures such as VaR and ES, as well as performance
- attribution for factor models (factor-contributed vs idiosyncratic returns)
- are included.
-License: GPL-2
-Depends:
- R (>= 3.0.0),
- xts (>= 0.9)
-Imports:
- PerformanceAnalytics(>= 1.1.0),
- corrplot,
- robust,
- leaps,
- lars,
- strucchange,
- lmtest,
- sandwich,
- lattice,
- MASS
-Suggests:
- testthat, quantmod, knitr
-LazyLoad: yes
-LazyDataCompression: xz
-URL: http://r-forge.r-project.org/R/?group_id=579
+Package: factorAnalytics
+Type: Package
+Title: Factor Analytics
+Version: 2.0.14
+Date: 2015-03-15
+Author: Eric Zivot, Sangeetha Srinivasan and Yi-An Chen
+Maintainer: Sangeetha Srinivasan <sangee at uw.edu>
+Description: An R package for the estimation and risk analysis of linear factor
+ models for asset returns and portfolios. It contains model fitting methods
+ for the three major types of factor models: time series (or, macroeconomic)
+ factor model, fundamental factor model and statistical factor model. They
+ allow for different types of distributions to be specified for modeling the
+ fat-tailed behavior of financial returns, including Edgeworth expansions.
+ Risk analysis measures such as VaR and ES, as well as performance
+ attribution for factor models (factor-contributed vs idiosyncratic returns)
+ are included.
+License: GPL-2
+Depends:
+ R (>= 3.0.0),
+ xts (>= 0.9)
+Imports:
+ PerformanceAnalytics(>= 1.1.0),
+ corrplot,
+ robust,
+ leaps,
+ lars,
+ strucchange,
+ lmtest,
+ sandwich,
+ lattice,
+ MASS
+Suggests:
+ testthat, quantmod, knitr
+LazyLoad: yes
+LazyDataCompression: xz
+URL: http://r-forge.r-project.org/R/?group_id=579
Modified: pkg/FactorAnalytics/R/fitTsfm.R
===================================================================
--- pkg/FactorAnalytics/R/fitTsfm.R 2015-03-16 04:25:23 UTC (rev 3614)
+++ pkg/FactorAnalytics/R/fitTsfm.R 2015-03-16 05:15:22 UTC (rev 3615)
@@ -1,549 +1,546 @@
-#' @title Fit a time series factor model using time series regression
-#'
-#' @description Fits a time series (a.k.a. macroeconomic) 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.
-#'
-#' Arguments \code{mkt.name} and \code{mkt.timing} allow for market-timing
-#' factors to be added to any of the above methods. A wrapper function
-#' \code{\link{fitTsfmMT}} is built based on this functionality. Please refer to
-#' \code{fitTsfmMT} for detail.
-#'
-#' \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 eturns.
-#' @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 Eric Zivot, Sangeetha Srinivasan and 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)
-#' fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
-#' factor.names=colnames(managers[,(7:9)]), data=managers)
-#' summary(fit)
-#' fitted(fit)
-#' # plot actual returns vs. fitted factor model returns for HAM1
-#' plot(fit, plot.single=TRUE, asset.name="HAM1", which=1)
-#' # group plot; type selected from menu prompt; auto-looped for multiple plots
-#' # plot(fit)
-#'
-#'
-#' # example using "subsets" variable selection
-#' fit.sub <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
-#' factor.names=colnames(managers[,(7:9)]),
-#' data=managers, variable.selection="subsets",
-#' method="exhaustive", nvmin=2)
-#'
-#' # example using "lars" variable selection and subtracting risk-free rate
-#' fit.lar <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
-#' factor.names=colnames(managers[,(7:9)]),
-#' rf.name="US.3m.TR", data=managers,
-#' variable.selection="lars", lars.criterion="cv")
-#'
-#' @importFrom PerformanceAnalytics checkData
-#' @importFrom robust lmRob step.lmRob
-#' @importFrom leaps regsubsets
-#' @importFrom lars lars cv.lars
-#'
-#' @export
-
-fitTsfm <- function(asset.names, factor.names, mkt.name=NULL, rf.name=NULL,
- data=data, fit.method=c("OLS","DLS","Robust"),
- variable.selection=c("none","stepwise","subsets","lars")
- , control=fitTsfm.control(...), ...) {
-
- # record the call as an element to be returned
- call <- match.call()
-
- # set defaults and check input vailidity
- fit.method = fit.method[1]
- if (!(fit.method %in% c("OLS","DLS","Robust"))) {
- stop("Invalid argument: fit.method must be 'OLS', 'DLS' or 'Robust'")
- }
- variable.selection = variable.selection[1]
- if (!(variable.selection %in% c("none","stepwise","subsets","lars"))) {
- stop("Invalid argument: variable.selection must be either 'none',
- 'stepwise','subsets' or 'lars'")
- }
- if (missing(factor.names) && !is.null(mkt.name)) {
- factor.names <- NULL
- }
-
- # extract arguments to pass to different fit and variable selection functions
- decay <- control$decay
- nvmin <- control$nvmin
- lars.criterion <- control$lars.criterion
- m1 <- match(c("weights","model","x","y","qr"),
- names(control), 0L)
- lm.args <- control[m1, drop=TRUE]
- m2 <- match(c("weights","model","x","y","nrep"),
- names(control), 0L)
- lmRob.args <- control[m2, drop=TRUE]
- m3 <- match(c("scope","scale","direction","trace","steps","k"),
- names(control), 0L)
- step.args <- control[m3, drop=TRUE]
- m4 <- match(c("weights","nvmax","force.in","force.out","method",
- "really.big"), names(control), 0L)
- regsubsets.args <- control[m4, drop=TRUE]
- m5 <- match(c("type","normalize","eps","max.steps","trace"),
- names(control), 0L)
- lars.args <- control[m5, drop=TRUE]
- m6 <- match(c("K","type","normalize","eps","max.steps","trace"),
- names(control), 0L)
- cv.lars.args <- control[m6, drop=TRUE]
-
- # convert data into an xts object and hereafter work with xts objects
- data.xts <- checkData(data)
- # convert index to 'Date' format for uniformity
- time(data.xts) <- as.Date(time(data.xts))
-
- # extract columns to be used in the time series regression
- dat.xts <- merge(data.xts[,asset.names], data.xts[,factor.names])
- ### After merging xts objects, the spaces in names get converted to periods
-
- # convert all asset and factor returns to excess return form if specified
- if (!is.null(rf.name)) {
- dat.xts <- "[<-"(dat.xts,,vapply(dat.xts, function(x) x-data.xts[,rf.name],
- FUN.VALUE = numeric(nrow(dat.xts))))
- }
-
-
-
- # spaces get converted to periods in colnames of xts object after merge
- asset.names <- gsub(" ",".", asset.names, fixed=TRUE)
- factor.names <- gsub(" ",".", factor.names, fixed=TRUE)
-
- # Selects regression procedure based on specified variable.selection method.
- # Each method returns a list of fitted factor models for each asset.
- if (variable.selection == "none") {
- reg.list <- NoVariableSelection(dat.xts, asset.names, factor.names,
- fit.method, lm.args, lmRob.args, decay)
- } else if (variable.selection == "stepwise") {
- reg.list <- SelectStepwise(dat.xts, asset.names, factor.names, fit.method,
- lm.args, lmRob.args, step.args, decay)
- } else if (variable.selection == "subsets") {
- reg.list <- SelectAllSubsets(dat.xts, asset.names, factor.names, fit.method,
- lm.args, lmRob.args, regsubsets.args,
- nvmin, decay)
- } else if (variable.selection == "lars") {
- result.lars <- SelectLars(dat.xts, asset.names, factor.names, lars.args,
- cv.lars.args, lars.criterion)
- input <- list(call=call, data=dat.xts, asset.names=asset.names,
- factor.names=factor.names, fit.method=NULL,
- variable.selection=variable.selection)
- result <- c(result.lars, input)
- class(result) <- "tsfm"
- return(result)
- }
-
- # extract coefficients from fitted factor models returned above
- coef.mat <- makePaddedDataFrame(lapply(reg.list, coef))
- alpha <- coef.mat[, 1, drop=FALSE]
- # to get alpha of class numeric, do: aplha <- coef.mat[,1]
- beta <- coef.mat[, -1, drop=FALSE]
- # reorder and expand columns of beta to match factor.names
- tmp <- matrix(NA, length(asset.names), length(factor.names))
- colnames(tmp) <- factor.names
- rownames(tmp) <- asset.names
- beta <- merge(beta, tmp, all.x=TRUE, sort=FALSE)[,factor.names, drop=FALSE]
- rownames(beta) <- asset.names
- # extract r2 and residual sd
- r2 <- sapply(reg.list, function(x) summary(x)$r.squared)
- resid.sd <- sapply(reg.list, function(x) summary(x)$sigma)
- # create list of return values.
- result <- list(asset.fit=reg.list, alpha=alpha, beta=beta, r2=r2,
- resid.sd=resid.sd, call=call, data=dat.xts,
- asset.names=asset.names, factor.names=factor.names,
- fit.method=fit.method, variable.selection=variable.selection)
- class(result) <- "tsfm"
- return(result)
-}
-
-
-### method variable.selection = "none"
-#
-NoVariableSelection <- function(dat.xts, asset.names, factor.names, fit.method,
- lm.args, lmRob.args, decay){
- # initialize list object to hold the fitted objects
- reg.list <- list()
-
- # loop through and estimate model for each asset to allow unequal histories
- for (i in asset.names) {
- # completely remove NA cases
- reg.xts <- na.omit(dat.xts[, c(i, factor.names)])
-
- # formula to pass to lm or lmRob
- fm.formula <- as.formula(paste(i," ~ ."))
-
- # fit based on time series regression method chosen
- if (fit.method == "OLS") {
- reg.list[[i]] <- do.call(lm, c(list(fm.formula,data=reg.xts),lm.args))
- } else if (fit.method == "DLS") {
- lm.args$weights <- WeightsDLS(nrow(reg.xts), decay)
- reg.list[[i]] <- do.call(lm, c(list(fm.formula,data=reg.xts),lm.args))
- } else if (fit.method == "Robust") {
- reg.list[[i]] <- do.call(lmRob, c(list(fm.formula,data=reg.xts),
- lmRob.args))
- }
- }
- reg.list
-}
-
-
-### method variable.selection = "stepwise"
-#
-SelectStepwise <- function(dat.xts, asset.names, factor.names, fit.method,
- lm.args, lmRob.args, step.args, decay) {
- # initialize list object to hold the fitted objects
- reg.list <- list()
-
- # loop through and estimate model for each asset to allow unequal histories
- for (i in asset.names) {
- # completely remove NA cases
- reg.xts <- na.omit(dat.xts[, c(i, factor.names)])
-
- # formula to pass to lm or lmRob
- fm.formula <- as.formula(paste(i," ~ ."))
-
- # fit based on time series regression method chosen
- if (fit.method == "OLS") {
- lm.fit <- do.call(lm, c(list(fm.formula,data=reg.xts),lm.args))
- reg.list[[i]] <- do.call(step, c(list(lm.fit),step.args))
- } else if (fit.method == "DLS") {
- lm.args$weights <- WeightsDLS(nrow(reg.xts), decay)
- lm.fit <- do.call(lm, c(list(fm.formula,data=reg.xts),lm.args))
- reg.list[[i]] <- do.call(step, c(list(lm.fit),step.args))
- } else if (fit.method == "Robust") {
- lmRob.fit <- do.call(lmRob, c(list(fm.formula,data=reg.xts), lmRob.args))
- reg.list[[i]] <- do.call(step.lmRob, c(list(lmRob.fit), step.args))
- }
- }
- reg.list
-}
-
-
-### method variable.selection = "subsets"
-#
-SelectAllSubsets <- function(dat.xts, asset.names, factor.names, fit.method,
- lm.args, lmRob.args, regsubsets.args, nvmin,
- decay) {
-
- # initialize list object to hold the fitted objects
- reg.list <- list()
-
- # loop through and estimate model for each asset to allow unequal histories
- for (i in asset.names) {
- # completely remove NA cases
- reg.xts <- na.omit(dat.xts[, c(i, factor.names)])
-
- # formula to pass to lm or lmRob
- fm.formula <- as.formula(paste(i," ~ ."))
-
- if (fit.method=="DLS" && !"weights" %in% names(regsubsets.args)) {
- regsubsets.args$weights <- WeightsDLS(nrow(reg.xts), decay)
- }
-
- # choose best subset of factors depending on specified subset size
- fm.subsets <- do.call(regsubsets, c(list(fm.formula,data=reg.xts),
- regsubsets.args))
- sum.sub <- summary(fm.subsets)
-
- # choose best model of a given subset size nvmax=nvmin (or)
- # best model amongst subset sizes in [nvmin, nvmax]
- nvmax <- length(sum.sub$bic)
- best.size <- which.min(sum.sub$bic[nvmin:nvmax]) + nvmin -1
- names.sub <- names(which(sum.sub$which[best.size,-1]==TRUE))
- bic <- min(sum.sub$bic[nvmin:nvmax])
-
- # completely remove NA cases for chosen subset
- reg.xts <- na.omit(dat.xts[,c(i,names.sub)])
-
- # fit based on time series regression method chosen
- if (fit.method == "OLS") {
- reg.list[[i]] <- do.call(lm, c(list(fm.formula,data=reg.xts),lm.args))
- } else if (fit.method == "DLS") {
- lm.args$weights <- WeightsDLS(nrow(reg.xts), decay)
- reg.list[[i]] <- do.call(lm, c(list(fm.formula,data=reg.xts),lm.args))
- } else if (fit.method == "Robust") {
- reg.list[[i]] <- do.call(lmRob, c(list(fm.formula,data=reg.xts),
- lmRob.args))
- }
- }
- reg.list
-}
-
-
-### method variable.selection = "lars"
-#
-SelectLars <- function(dat.xts, asset.names, factor.names, lars.args,
- cv.lars.args, lars.criterion) {
- # initialize list object to hold the fitted objects and, vectors and matrices
- # for the other results
- asset.fit <- list()
- fitted.list <- list()
- alpha <- rep(NA, length(asset.names))
- beta <- matrix(NA, length(asset.names), length(factor.names))
- r2 <- rep(NA, length(asset.names))
- resid.sd <- rep(NA, length(asset.names))
- names(alpha)=names(r2)=names(resid.sd)=rownames(beta)=asset.names
- colnames(beta) <- factor.names
-
- # loop through and estimate model for each asset to allow unequal histories
- for (i in asset.names) {
- # completely remove NA cases
- reg.xts <- na.omit(dat.xts[, c(i, factor.names)])
- # convert to matrix
- reg.mat <- as.matrix(reg.xts)
- # fit lars regression model
- lars.fit <- do.call(lars, c(list(x=reg.mat[,factor.names],y=reg.mat[,i]),
- lars.args))
- lars.sum <- summary(lars.fit)
- lars.cv <- do.call(cv.lars, c(list(x=reg.mat[,factor.names],y=reg.mat[,i],
- mode="step"),cv.lars.args))
- # including plot.it=FALSE to cv.lars strangely gives an error: "Argument s
- # out of range". And, specifying index=seq(nrow(lars.fit$beta)-1) resolves
- # the issue, but care needs to be taken for small N
-
- # get the step that minimizes the "Cp" statistic or
- # the K-fold "cv" mean-squared prediction error
- if (lars.criterion=="Cp") {
- s <- which.min(lars.sum$Cp)-1 # 2nd row is "step 1"
- } else {
- s <- which.min(lars.cv$cv)-1
- }
- # get factor model coefficients & fitted values at the step obtained above
- coef.lars <- predict(lars.fit, s=s, type="coef", mode="step")
- fitted.lars <- predict(lars.fit, reg.mat[,factor.names], s=s, type="fit",
- mode="step")
- fitted.list[[i]] <- xts(fitted.lars$fit, index(reg.xts))
- # extract and assign the results
- asset.fit[[i]] = lars.fit
- alpha[i] <- (fitted.lars$fit -
- reg.xts[,factor.names]%*%coef.lars$coefficients)[1]
- beta.names <- names(coef.lars$coefficients)
- beta[i, beta.names] <- coef.lars$coefficients
- r2[i] <- lars.fit$R2[s+1]
- resid.sd[i] <- sqrt(lars.sum$Rss[s+1]/(nrow(reg.xts)-lars.sum$Df[s+1]))
-
- }
- if (length(asset.names)>1) {
- fitted.xts <- do.call(merge, fitted.list)
- } else {
- fitted.xts <- fitted.list[[1]]
- }
- results.lars <- list(asset.fit=asset.fit, alpha=alpha, beta=beta, r2=r2,
- resid.sd=resid.sd, fitted=fitted.xts)
- # As a special case for variable.selection="lars", fitted values are also
- # returned by fitTsfm. Else, step s from the best fit is needed to get
- # fitted values & residuals.
-}
-
-
-### calculate exponentially decaying weights for fit.method="DLS"
-## t = number of observations; d = decay factor
-#
-WeightsDLS <- function(t,d) {
- # more weight given to more recent observations
- w <- d^seq((t-1),0,-1)
- # ensure that the weights sum to unity
- w/sum(w)
-}
-
-### make a data frame (padded with NAs) from unequal vectors with named rows
-## l = list of unequal vectors
-#
-makePaddedDataFrame <- function(l) {
- DF <- do.call(rbind, lapply(lapply(l, unlist), "[",
- unique(unlist(c(sapply(l,names))))))
- DF <- as.data.frame(DF)
- names(DF) <- unique(unlist(c(sapply(l,names))))
- # as.matrix(DF) # if matrix output needed
- DF
-}
-
-#' @param object a fit object of class \code{tsfm} which is returned by
-#' \code{fitTsfm}
-
-#' @rdname fitTsfm
-#' @method coef tsfm
-#' @export
-
-coef.tsfm <- function(object, ...) {
- # cbind alpha and beta; works for all fit and var selection methods
- coef.mat <- cbind(object$alpha, object$beta)
- # name for alpha/intercept column
- colnames(coef.mat)[1] <- "(Intercept)"
- return(coef.mat)
-}
-
-#' @rdname fitTsfm
-#' @method fitted tsfm
-#' @export
-
-fitted.tsfm <- function(object, ...) {
- if (object$variable.selection=="lars") {
- # generic method 'fitted' does not exist for "lars" fit objects
- # so, use fitted values returned by 'fitTsfm'
- fitted.xts <- object$fitted
- } else {
- if (length(object$asset.names)>1) {
- # get fitted values from each linear factor model fit
- # and convert them into xts/zoo objects
- fitted.list = sapply(object$asset.fit,
- function(x) checkData(fitted(x,...)))
- # this is a list of xts objects, indexed by the asset name
- # merge the objects in the list into one xts object
- fitted.xts <- do.call(merge, fitted.list)
- } else {
- fitted.xts <- checkData(fitted(object$asset.fit[[1]],...))
- colnames(fitted.xts) <- object$asset.names
- }
- }
- time(fitted.xts) <- as.Date(time(fitted.xts))
- return(fitted.xts)
-}
-
-
-#' @rdname fitTsfm
-#' @method residuals tsfm
-#' @export
-
-residuals.tsfm <- function(object, ...) {
- if (object$variable.selection=="lars") {
- # generic method 'residuals' does not exist for "lars" fit objects
- # so, calculate them from the actual and fitted values
- residuals.xts <- object$data[,object$asset.names] - object$fitted
- } else {
- if (length(object$asset.names)>1) {
- # get residuals from each linear factor model fit
- # and convert them into xts/zoo objects
- residuals.list = sapply(object$asset.fit,
- function(x) checkData(residuals(x,...)))
- # this is a list of xts objects, indexed by the asset name
- # merge the objects in the list into one xts object
- residuals.xts <- do.call(merge, residuals.list)
- } else {
- residuals.xts <- checkData(residuals(object$asset.fit[[1]],...))
- colnames(residuals.xts) <- object$asset.names
- }
- }
- time(residuals.xts) <- as.Date(time(residuals.xts))
- return(residuals.xts)
-}
+#' @title Fit a time series factor model using time series regression
+#'
+#' @description Fits a time series (a.k.a. macroeconomic) 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.
+#'
+#' Argument \code{mkt.name} can be used to add market-timing factors to any of
+#' the above methods. Please refer to \code{\link{fitTsfmMT}}, a wrapper to
+#' \code{fitTsfm} for details.
+#'
+#' \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 returns. 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 Eric Zivot, Sangeetha Srinivasan and 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
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