facmodCS (version 1.0)

specFfm: Specifies the elements of a fundamental factor model

Description

Factor models have a few parameters that describe how the fitting is done. This function summarizes them and returns a spec object for cross-sectional regressions. It also preps the data. An object of class "ffmSpec" is returned.

Usage

specFfm(
  data,
  asset.var,
  ret.var,
  date.var,
  exposure.vars,
  weight.var = NULL,
  addIntercept = FALSE,
  rob.stats = FALSE
)

Value

an object of class ffmSpec holding the details of the analysis

Arguments

data

data.frame of the balanced panel data containing the variables asset.var, ret.var, exposure.vars, date.var and optionally, weight.var.

asset.var

character; name of the variable for asset names.

ret.var

character; name of the variable for asset returns.

date.var

character; name of the variable containing the dates coercible to class Date.

exposure.vars

vector; names of the variables containing the fundamental factor exposures.

weight.var

character; name of the variable containing the weights used when standardizing style factor exposures. Default is NULL. See Details.

addIntercept

logical; If TRUE, intercept is added in the exposure matrix. Default is FALSE,

rob.stats

logical; If TRUE, robust estimates of covariance, correlation, location and univariate scale are computed as appropriate (see Details). Default is FALSE.

Examples

# \donttest{
library(PCRA)

dateRange <- c("2006-01-31","2010-12-31")
stockItems <-  c("Date", "TickerLast",  "Return","Sector")
factorItems <- c("BP","Beta60M","PM12M1M")
facDatIT <- selectCRSPandSPGMI("monthly",
                               dateRange = dateRange, 
                               stockItems = stockItems, 
                               factorItems = factorItems, 
                               outputType = "data.table")
asset.var="TickerLast" 
ret.var="Return" 
date.var = "Date"
exposure.vars= c("BP","Beta60M","PM12M1M")
spec1 <- specFfm(data = facDatIT,asset.var = asset.var, ret.var = ret.var, 
                 date.var = date.var, exposure.vars = exposure.vars,weight.var = NULL,
                 addIntercept = TRUE, rob.stats = FALSE)
spec1$exposure.vars

#lag the exposures
spec1 <- lagExposures(spec1)
# standardize the exposures Cross-Sectionally
spec1 <- standardizeExposures(spec1, Std.Type = "CrossSection") 
# fit the model
mdlFit <- fitFfmDT(spec1) 
class(mdlFit)
class(mdlFit$reg.listDT)
# }