dftse {corbouli} | R Documentation |
Remove irrelevant frequencies
dftse(x, low_freq = NULL, high_freq = NULL)
x |
Vector, |
low_freq |
Number indicating the lowest period of oscillation as fractions of |
high_freq |
Number indicating the highest period of oscillation as radians of |
This is a pure R implementation of removing the irrelevant frequencies. First,
DFT is applied on the data and this result is filtered according to
low_freq
and high_freq
. Finally, an inverse DFT is performed on
these relevant frequencies. Both low_freq
and high_freq
must be
either between 0 and 1, meaning that they are frequencies of the period as
radians, or both >1, indicating that both are starting and ending periods of the
cycle.
low_freq
and high_freq
are used for keeping the relevant
frequencies. These are meant to be the ones inside the range
[ low \_ freq, high \_ freq ]
. Therefore, values outside this range are
removed.
For 2-dimensional objects x
, this transformation is applied per column.
Filtered object with length/dimensions same with the input x. Note that for
inputs with dimensions (e.g. matrix
, data.frame
) a matrix
object will be returned.
Corbae, D., Ouliaris, S., & Phillips, P. (2002), Band Spectral Regression with Trending-Data. Econometrica 70(3), pp. 1067-1109.
Corbae, D. & Ouliaris, S. (2006), Extracting Cycles from Nonstationary Data, in Corbae D., Durlauf S.N., & Hansen B.E. (eds.). Econometric Theory and Practice: Frontiers of Analysis and Applied Research. Cambridge: Cambridge University Press, pp. 167–177. doi:10.1017/CBO9781139164863.008.
Shaw, E.S. (1947), Burns and Mitchell on Business Cycles. Journal of Political Economy, 55(4): pp. 281-298. doi:10.1086/256533.
# Apply on ts object
data(USgdp)
res <- dftse(USgdp, low_freq = 0.0625, high_freq = 0.3333)
head(res)
# Apply on vector
res <- dftse(c(USgdp), low_freq = 0.0625, high_freq = 0.3333)
head(res)
# Apply on matrix per column
mat <- matrix(USgdp, ncol = 4)
res <- dftse(mat, low_freq = 0.0625, high_freq = 0.3333)
head(res)
# Apply on data.frame per column
dfmat <- as.data.frame(mat)
res <- dftse(dfmat, low_freq = 0.0625, high_freq = 0.3333)
head(res)