Description Usage Arguments Author(s) See Also Examples
Plot the distribution of the multiple estimates from EstEval, and possibly multiple EstEval objects.
1 2 3 | ## S3 method for class 'factorsEstEval'
distribution(obj, ..., bandwidth = "nrd0",
cumulate=TRUE, graphs.per.page = 5, Title=NULL)
|
obj |
EstEval object. |
bandwidth |
bandwidth for distribution smoothing. |
cumulate |
logical indicating if the distribution across time and repititions should be plotted (TRUE) or a time series of standard deviation across repititions should be plotted (FALSE). |
graphs.per.page |
number of graphs on an output page. |
Title |
string indicating a title for the plot. |
... |
additional EstEval objects which will be plotted on the same graph. |
Paul Gilbert
distribution
,
EstEval
,
estTSF.ML
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | if (require("CDNmoney")){
data("CanadianMoneyData.asof.6Feb2004", package="CDNmoney")
### Construct data
cpi <- 100 * M1total / M1real
seriesNames(cpi) <- "CPI"
popm <- M1total / M1PerCapita
seriesNames(popm) <- "Population of Canada"
z <- tframed(tbind(
MB2001,
MB486 + MB452 + MB453 ,
NonbankCheq,
MB472 + MB473 + MB487p,
MB475,
NonbankNonCheq + MB454 + NonbankTerm + MB2046 + MB2047 + MB2048 +
MB2057 + MB2058 + MB482),
names=c("currency", "personal cheq.", "NonbankCheq",
"N-P demand & notice", "N-P term", "Investment")
)
z <- tfwindow(z, start=c(1986,1))
if( all(c(2003,12) ==end(z))) z <-tfwindow(z, end=c(2003,11))
MBcomponents <- 1e8 * z/matrix(tfwindow(popm * cpi,tf=tframe(z)), Tobs(z),6)
### Specify "true" parameters and factors
Omega <- diag(c(72.63, 1233, 87.33,
629.4, 3968, 12163))
Boblq <- t(matrix(c(
8.84, 5.20,
23.82, -12.57,
5.18, -1.97,
36.78, 16.94,
-2.84, 31.02,
2.60, 47.63), 2,6))
PhiOblq <- matrix(c( 1.0, 0.00949, 0.00949, 1.0),2,2)
etaBart <- MBcomponents %*% solve(Omega) %*% Boblq %*% (
solve( t(Boblq) %*% solve(Omega) %*% Boblq ) )
DetaBart <- diff(etaBart, lag=1)
SDE <- cov(DetaBart)
RR1 <- chol(SDE) # upper triangular: SDE = RR1' RR1
RR2 <- chol(PhiOblq) # ditto
PP <- t(RR2) %*% solve(t(RR1))
Psi <- 0.5 * Omega
etaTrue <- tframed(etaBart %*% t(PP), tf=tframe(MBcomponents))
### run Monte Carlo N.B. replications would typically be much larger
require("EvalEst")
EE.ML5 <- EstEval(TSFmodel(Boblq, f=etaTrue, positive.measures=FALSE),
replications=5, quiet=FALSE,
simulation.args=list(Cov=Psi, noIC=TRUE),
estimation="estTSF.ML", estimation.args=list(2, BpermuteTarget=Boblq),
criterion ="TSFmodel")
distribution(factors(EE.ML5))
distribution(factors(EE.ML5), cumulate=FALSE)
distribution(diff(factors(EE.ML5)))
distribution(diff(factors(EE.ML5)), cumulate=FALSE)
}
|
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