kern_smooth {timevarcorr}

Smoothing by kernel regression

Description

The function perform the smoothing of a time-series by non-parametric kernel regression.

Usage

kern_smooth(
  x,
  t = seq_along(x),
  h,
  t.for.pred = t,
  kernel = c("epanechnikov", "box", "normal"),
  param_smoother = list(),
  output = c("dataframe", "list")
)

Arguments

x

a numeric vector of the series to be smoothed.

t

a (numeric or Date) vector of time points. If missing, observations are considered to correspond to sequential time steps (i.e., 1, 2 ...).

h

a scalar indicating the bandwidth used by the smoothing function.

t.for.pred

a (numeric or Date) vector of time points at which to evaluate the smoothed fit. If missing, t is used.

kernel

a character string indicating which kernel to use: "epanechnikov" (the default), "box", or "normal" (abbreviations also work).

param_smoother

a list of additional parameters to provide to the internal smoothing function (see Details).

output

a character string indicating if the output should be a "dataframe" (default) or a list (for faster computation when the function is called repeatedly).

Details

The function is essentially a wrapper that calls different underlying functions depending on the kernel that is selected:

• lpridge::lpepa() for "epanechnikov".

• stats::ksmooth() for "normal" and "box". The argument param_smoother can be used to pass additional arguments to these functions.

Value

a dataframe of time points (t.for.pred) and corresponding fitted values.

References

A short post we found useful: http://users.stat.umn.edu/~helwig/notes/smooth-notes.html

See Also

tcor

Examples

## Smooth 10 first values of a vector

kern_smooth(stockprice$DAX[1:20], h = 5)


## Prediction at time step 2 and 3

kern_smooth(stockprice$DAX, h = 1, t.for.pred = c(2, 3))


## Smoothing using a vector of dates for time

kern_smooth(x = stockprice$DAX[1:10], t = stockprice$DateID[1:10], h = 5)


## Smoothing conserves original order

kern_smooth(x = stockprice$DAX[10:1], t = stockprice$DateID[10:1], h = 5)


## Effect of the bandwidth

plot(stockprice$DAX[1:100] ~ stockprice$DateID[1:100],
     las = 1, ylab = "DAX index", xlab = "Date")
points(kern_smooth(stockprice$DAX[1:100], stockprice$DateID[1:100], h = 1),
       type = "l", col = "grey")
points(kern_smooth(stockprice$DAX[1:100], stockprice$DateID[1:100], h = 3),
       type = "l", col = "blue")
points(kern_smooth(stockprice$DAX[1:100], stockprice$DateID[1:100], h = 10),
       type = "l", col = "red")
legend("topright", fill = c("grey", "blue", "red"),
       legend = c("1", "3", "10"), bty = "n", title = "Bandwidth (h)")


## Effect of the kernel

plot(stockprice$DAX[1:100] ~ stockprice$DateID[1:100],
     las = 1, ylab = "DAX index", xlab = "Date")
points(kern_smooth(stockprice$DAX[1:100], stockprice$DateID[1:100], h = 10),
       type = "l", col = "orange")
points(kern_smooth(stockprice$DAX[1:100], stockprice$DateID[1:100], h = 10, kernel = "box"),
       type = "l", col = "blue")
points(kern_smooth(stockprice$DAX[1:100], stockprice$DateID[1:100], h = 10, kernel = "norm"),
       type = "l", col = "red")
legend("topright", fill = c("orange", "blue", "red"),
       legend = c("epanechnikov", "box", "normal"), bty = "n", title = "Kernel method")