Displaying 4 results from an estimated 4 matches for "dlmsvd2var".
2018 Mar 26
0
"dlm" Package: Calculating State Confidence Intervals
...It May Concern,
I estimated a model with 6 states (3 time-varying Regression parameters and 3 quarterly seasonality trends). The model is saved in the object titled "mod."
Following the example in the documentation and using the commands below, I am attempting to use the function "dlmSvd2var" to implement SVD and calculate the 90% confidence errors for each time-varying state.
outF <- dlmFilter(y,mod)
v <- unlist(dlmSvd2var(outF$U.C, outF$D.C))
pl <- dropFirst(outF$m) + qnorm(0.05, sd=sqrt(v[-1]))
pu <- dropFirst(outF$m) + qnorm(0.95, sd=sqrt(v[-1]))
Since the datase...
2018 Mar 28
0
"dlm" Package: Calculating State Confidence Intervals
...It May Concern,
I estimated a model with 6 states (3 time-varying Regression parameters and 3 quarterly seasonality trends). The model is saved in the object titled "mod."
Following the example in the documentation and using the commands below, I am attempting to use the function "dlmSvd2var" to implement SVD and calculate the 90% confidence errors for each time-varying state.
outF <- dlmFilter(y,mod)
v <- unlist(dlmSvd2var(outF$U.C, outF$D.C))
pl <- dropFirst(outF$m) + qnorm(0.05, sd=sqrt(v[-1]))
pu <- dropFirst(outF$m) + qnorm(0.95, sd=sqrt(v[-1]))
Since the datase...
2011 Jun 03
0
Package dlm generates unstable results?
...- function(x){
return(dlm(
m0 = x[1],
C0 = x[2],
FF = 1,
GG = 1,
V = x[3],
W = x[4],
JFF = 1,
X = X
))
}
ModFit <- dlmMLE(Y,rep(1,4),BuildMod,debug=T)
dlmMod <- BuildMod(ModFit$par)
V <- dlmMod$V
W <- dlmMod$W
ModFilt <- dlmFilter(Y,dlmMod)
v <- tail(dlmSvd2var(ModFilt$U.C,ModFilt$D.C),1)
m <- tail(ModFilt$m,1)
The results are:
V = 5.945003e-05
W = 0.0003086623
v = 9.850526e-05 (the estimated variance for a_t after we observe the last pair of observations)
m = -0.02965614 (the estimated mean for a_t after we observe the last pair of observations)...
2011 Jun 03
0
How to reconcile Kalman filter result (by package dlm) with linear regression?
...diag(nFactor),
V = tail(x,1)^2,
W = crossprod(L1),
JFF = matrix(1:4,nr=1),
X = X
))
}
ModFit <- dlmMLE(Y,rep(0.1,nTotal),BuildMod,debug=T)
dlmMod <- BuildMod(ModFit$par)
V = dlmMod$V
W = dlmMod$W
m0 = dlmMod$m0
C0 = dlmMod$C0
ModFilt <- dlmFilter(Y,dlmMod)
v <- tail(dlmSvd2var(ModFilt$U.C,ModFilt$D.C),1)
m <- tail(ModFilt$m,1)
Here is the value of Y:
0.0125678739370109
-0.00241285475528163
0.00386919876129071
-0.00352839097011217
0.00285344714211614
0.00374266510625097
0.00797807743013259
-0.00543459628953192
-0.0138447399853609
-0.0102614592879934
-0.0225111772602...