# R/knsmooth.R In smoots: Nonparametric Estimation of the Trend and Its Derivatives in TS

#### Documented in knsmooth

#' Estimation of Nonparametric Trend Functions via Kernel Regression
#'
#' This function estimates the nonparametric trend function in an equidistant
#' time series with Nadaraya-Watson kernel regression.
#'
#' @param y a numeric vector that contains the time series data ordered from
#' past to present.
#' @param mu an integer \code{0}, \code{1}, \code{2}, ... that represents the
#' smoothness parameter of the second order kernel function that will be used;
#' is set to \code{1} by default.
#'
#'\tabular{cl}{
#'\strong{Number (\code{mu})} \tab \strong{Kernel}\cr
#'\code{0} \tab Uniform Kernel\cr
#'\code{1} \tab Epanechnikov Kernel\cr
#'\code{2} \tab Bisquare Kernel\cr
#'\code{3} \tab Triweight Kernel\cr
#'\code{...} \tab ...
#' }
#' @param b a real number \eqn{0 <} \code{b} \eqn{< 0.5}; represents the
#' relative bandwidth that will be used for the smoothing process; is set to
#' \code{0.15} by default.
#' @param bb can be set to \code{0} or \code{1}; the parameter controlling the
#' bandwidth used at the boundary; is set to \code{0} by default.
#'
#' \tabular{cl}{
#' \strong{Number (\code{bb})} \tab \strong{Estimation procedure at boundary
#' points}\cr
#' \code{0} \tab Fixed bandwidth on one side with possible large
#' bandwidth on the other side at the boundary\cr
#' \code{1} \tab The k-nearest neighbor method will be used
#' }
#'
#'@export
#'
#'@details
#'
#'The trend is estimated based on the additive
#'nonparametric regression model for an equidistant time series
#'\deqn{y_t = m(x_t) + \epsilon_t,}
#'where \eqn{y_t} is the observed time series, \eqn{x_t} is the rescaled time
#'on the interval \eqn{[0, 1]}, \eqn{m(x_t)} is a smooth and deterministic
#'trend function and \eqn{\epsilon_t} are stationary errors with
#'\eqn{E(\epsilon_t) = 0}.
#'
#'This function is part of the package \code{smoots} and is used for
#'the estimation of trends in equidistant time series. The applied method
#'is a kernel regression with arbitrarily selectable second order
#'kernel, relative bandwidth and boundary method. Especially the chosen
#'bandwidth has a strong impact on the final result and has thus to be
#'selected carefully. This approach is not recommended by the authors of this
#'package.
#'
#'@return The output object is a list with different components:
#'
#'\describe{
#'\item{b}{the chosen (relative) bandwidth; input argument.}
#'\item{bb}{the chosen bandwidth option at the boundaries; input argument.}
#'\item{mu}{the chosen smoothness parameter for the second order kernel; input
#'argument.}
#'\item{n}{the number of observations.}
#'\item{orig}{the original input series; input argument.}
#'\item{res}{a vector with the estimated residual series.}
#'\item{ye}{a vector with the estimates of the nonparametric trend.}
#'}
#'
#'@references
#'Feng, Y. (2009). Kernel and Locally Weighted Regression. Verlag für
#'Wissenschaft und Forschung, Berlin.
#'
#'@author
#'\itemize{
#'\item Yuanhua Feng (Department of Economics, Paderborn University), \cr
#'Author of the Algorithms \cr
#'\item Dominik Schulz (Research Assistant) (Department of Economics, Paderborn
#'University), \cr
#'Package Creator and Maintainer
#'}
#'
#'@examples
#'# Logarithm of test data
#'test_data <- gdpUS
#'y <- log(test_data$GDP) #' #'#Applied knmooth function for the trend with two different bandwidths #'trend1 <- knsmooth(y, mu = 1, b = 0.28, bb = 1)$ye
#'trend2 <- knsmooth(y, mu = 1, b = 0.05, bb = 1)\$ye
#'
#'# Plot of the results
#'t <- seq(from = 1947, to = 2019.25, by = 0.25)
#'plot(t, y, type = "l", xlab = "Year", ylab = "log(US-GDP)", bty = "n",
#'  lwd = 2,
#'  main = "Estimated trend for log-quarterly US-GDP, Q1 1947 - Q2 2019")
#'points(t, trend1, type = "l", col = "red", lwd = 1)
#'points(t, trend2, type = "l", col = "blue", lwd = 1)
#'legend("bottomright", legend = c("Trend (b = 0.28)", "Trend (b = 0.05)"),
#'  fill = c("red", "blue"), cex = 0.6)
#'title(sub = expression(italic("Figure 1")), col.sub = "gray47",
#'  cex.sub = 0.6, adj = 0)
#'
#'

knsmooth <- function(y, mu = 1, b = 0.15, bb = c(0, 1)) {

if (length(y) <= 1 || !all(!is.na(y)) || !is.numeric(y)) {
stop("The argument 'y' must be a numeric vector with length > 1 and ",
"without NAs.")
}

if (length(mu) != 1 || is.na(mu) || !is.numeric(mu) || mu < 0) {
stop("The argument 'mu' must be a single non-zero and non-NA integer ",
"value.")
}
mu <- floor(mu)

if (length(b) != 1 || is.na(b) || !is.numeric(b) || (b <= 0 || b >= 0.5)) {
stop("The argument 'b' must be a single non-NA double value with ",
"0 < b < 0.5.")
}

if (!length(bb) %in% c(1, 2) || !all(!is.na(bb)) || !is.numeric(b)) {
stop("The argument 'bb' must be a single non-NA integer ",
"(either 0 or 1).")
}
bb <- floor(bb)

if (all(bb == c(0, 1))) bb <- 0
if (length(bb) != 1 || !(bb %in% c(0, 1))) {
stop("The argument 'bb' must be a single non-NA integer ",
"(either 0 or 1).")
}

n <- length(y)           #number of observations
hn <- trunc(b * n + 0.5)
gr <- rep(0, n)         #vector of estimates
if (hn > trunc((n - 1) / 2)) {hn <- trunc((n - 1) / 2)} ### maximal bandwidth

####### kernel smoothing
hr <- c(hn + bb * (hn - (0:hn)))    #pointwise right bandwith of i
ht <- c(1 + (0:hn)) + hr       #pointwise total bandwith of i

for (i in (1:(hn + 1))) {
u <- ((1:ht[i]) - i) / (hr[i] + 0.5)

###### The kernel functions
wk <- 1 / 2 * (1 - u^2)^mu
wk <- wk / sum(wk) ##### so that the sum of all weights is 1.

if (i <= hn) {
gr[i] <- sum(wk * y[1:ht[i]])
gr[n - i + 1] <- sum(wk * y[n:(n - ht[i] + 1)])
}

if(i == hn + 1) {

for(j in i:(n-hn)) {
gr[j] <- sum(wk * y[(j - hn):(j + hn)])
}
}
}

result <- list(ye = gr, orig = y, res = y - gr, mu = mu, b = b, bb = bb,
n = n)

class(result) <- "smoots"  # set package attribute
attr(result, "function") <- "knsmooth"  # set function attribute for
# own print function

return(result)  ####The results are given in gr
}


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smoots documentation built on Sept. 11, 2023, 9:07 a.m.