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#' text nowcast
#'
#' @param x the input matrix x. It should have 1 observation more that y.
#' @param y the response variable
#' @param intercept TRUE for include intercept in the forecast equation.
#'
#' @importFrom stats lm
#'
#' @return the nowcast h=0 for the variable y.
#' @export
#'
#' @examples
#' set.seed(1)
#' data("stock_data")
#' data("news_data")
#' y=as.matrix(stock_data[,2])
#' w=as.matrix(stock_data[,3])
#' data("news_data")
#' data("optimal_factors")
#' pc=optimal_factors
#' z=cbind(w,pc)
#' t=length(y)
#' ncsts=text_nowcast(z,y[1:(t-1)],TRUE)
text_nowcast <- function(x,y,intercept) {
y=as.vector(y)
x=as.matrix(x)
t=length(y)
yy=y[1:t]
xx=x[1:t,]
if(intercept==TRUE){
eq1=lm(yy~xx)
betas=eq1$coef
betas[is.na(betas)]=0
nx2=x[(t+1),]
ncsts1=t(c(1,nx2))%*%betas
} else {
eq1=lm(yy~xx+0)
betas=eq1$coef
betas[is.na(betas)]=0
nx2=x[(t+1),]
ncsts1=t(nx2)%*%betas
}
return(ncsts1)
}
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