Manipulating Finite Filters
Usage
finite_filters(
sfilter,
rfilters = NULL,
lfilters = NULL,
first_to_last = FALSE
)
is.finite_filters(x)
# S4 method for class 'finite_filters'
show(object)Arguments
- sfilter
the symmetric filter (
moving_average()object) or amatrixorlistwith all the coefficients.- rfilters
the right filters (used on the last points).
- lfilters
the left filters (used on the first points).
- first_to_last
boolean indicating if the first element of
rfiltersis the first asymmetric filter (when only one observation is missing) or the last one (real-time estimates).- x
object to test the class.
- object
finite_filtersobject.
Examples
ff_lp <- lp_filter()
ff_simple_ma <- finite_filters(moving_average(c(1, 1, 1), lags = -1)/3,
rfilters = list(moving_average(c(1, 1), lags = -1)/2))
ff_lp
#> q=6 q=5 q=4 q=3 q=2
#> t-6 -0.01934985 -0.016609040 -0.011623676 -0.009152423 -0.016139228
#> t-5 -0.02786378 -0.025914479 -0.022541271 -0.020981640 -0.024948087
#> t-4 0.00000000 0.001157790 0.002918842 0.003566851 0.002620762
#> t-3 0.06549178 0.065858066 0.066006963 0.065743350 0.067817618
#> t-2 0.14735651 0.146931288 0.145468029 0.144292794 0.149387420
#> t-1 0.21433675 0.213120014 0.210044599 0.207957742 0.216072726
#> t 0.24005716 0.238048915 0.233361346 0.230362866 0.241498208
#> t+1 0.21433675 0.211536998 0.205237273 0.201327171 0.215482871
#> t+2 0.14735651 0.143765257 0.135853376 0.131031652 0.148207710
#> t+3 0.06549178 0.061109020 0.051584983 0.045851637 0.000000000
#> t+4 0.00000000 -0.005174272 -0.016310464 0.000000000 0.000000000
#> t+5 -0.02786378 -0.033829557 0.000000000 0.000000000 0.000000000
#> t+6 -0.01934985 0.000000000 0.000000000 0.000000000 0.000000000
#> q=1 q=0
#> t-6 -0.037925830 -0.07371504
#> t-5 -0.035216813 -0.04601336
#> t-4 0.003869912 0.01806602
#> t-3 0.080584644 0.11977342
#> t-2 0.173672322 0.23785375
#> t-1 0.251875504 0.34104960
#> t 0.288818862 0.40298562
#> t+1 0.274321400 0.00000000
#> t+2 0.000000000 0.00000000
#> t+3 0.000000000 0.00000000
#> t+4 0.000000000 0.00000000
#> t+5 0.000000000 0.00000000
#> t+6 0.000000000 0.00000000
ff_simple_ma
#> q=1 q=0
#> t-1 0.3333333 0.5
#> t 0.3333333 0.5
#> t+1 0.3333333 0.0
ff_lp * ff_simple_ma
#> q=7 q=6 q=5 q=4 q=3
#> t-7 -0.006449948 -0.006449948 -0.005536347 -0.003874559 -0.003050808
#> t-6 -0.015737874 -0.015737874 -0.014174506 -0.011388316 -0.010044688
#> t-5 -0.015737874 -0.015737874 -0.013788576 -0.010415368 -0.008855737
#> t-4 0.012542669 0.012542669 0.013700459 0.015461511 0.016109520
#> t-3 0.070949432 0.070949432 0.071315715 0.071464612 0.071200998
#> t-2 0.142395015 0.142395015 0.141969789 0.140506531 0.139331295
#> t-1 0.200583472 0.200583472 0.199366739 0.196291325 0.194204467
#> t 0.222910217 0.222910217 0.220901976 0.216214406 0.213215926
#> t+1 0.200583472 0.200583472 0.197783724 0.191483998 0.187573896
#> t+2 0.142395015 0.142395015 0.138803758 0.130891877 0.133712093
#> t+3 0.070949432 0.070949432 0.066566668 0.054324221 0.066603036
#> t+4 0.012542669 0.012542669 0.001730137 0.009039762 0.000000000
#> t+5 -0.015737874 -0.018962848 -0.018639536 0.000000000 0.000000000
#> t+6 -0.015737874 -0.018962848 0.000000000 0.000000000 0.000000000
#> t+7 -0.006449948 0.000000000 0.000000000 0.000000000 0.000000000
#> q=2 q=1 q=0
#> t-7 -0.005379743 -0.01264194 -0.02457168
#> t-6 -0.013695772 -0.02438088 -0.03990947
#> t-5 -0.012822184 -0.02309091 -0.03388746
#> t-4 0.015163431 0.01641258 0.03060869
#> t-3 0.073275267 0.08604229 0.12523106
#> t-2 0.144425922 0.16871082 0.23289226
#> t-1 0.202319451 0.23812223 0.39446059
#> t 0.224351268 0.31739216 0.31517601
#> t+1 0.226430881 0.23343365 0.00000000
#> t+2 0.145931478 0.00000000 0.00000000
#> t+3 0.000000000 0.00000000 0.00000000
#> t+4 0.000000000 0.00000000 0.00000000
#> t+5 0.000000000 0.00000000 0.00000000
#> t+6 0.000000000 0.00000000 0.00000000
#> t+7 0.000000000 0.00000000 0.00000000