meboot {meboot} | R Documentation |
Generates maximum entropy bootstrap replicates for dependent data. (See details.)
meboot (x, reps=999, trim=0.10, reachbnd=TRUE, expand.sd=TRUE, force.clt=TRUE, elaps=FALSE, colsubj, coldata, coltimes, ...)
x |
vector of data, ts object or pdata.frame object. |
reps |
number of replicates to generate. |
trim |
the trimming proportion. |
reachbnd |
logical. If TRUE potentially reached bounds (xmin = smallest value - trimmed mean and xmax=largest value + trimmed mean) are given when the random draw happens to be equal to 0 and 1, respectively. |
expand.sd |
logical. If TRUE the standard deviation in the ensemble in expanded. See expand.sd . |
force.clt |
logical.If TRUE the ensemble is forced to satisfy the central limit theorem. See force.clt . |
elaps |
logical. If TRUE elapsed time during computations is displayed. |
colsubj |
the column in x that contains the individual index. It is ignored if the input data x is not a pdata.frame object. |
coldata |
the column in x that contains the data of the variable to create the ensemble. It is ignored if the input data x is not a pdata.frame object. |
coltimes |
an optional argument indicating the column that contains the times at which the observations for each individual are observed. It is ignored if the input data x is not a pdata.frame object. |
... |
possible argument fiv to be passed to expand.sd . |
Seven-steps algorithm:
xmin
) and upper limit for right tail (xmax
). This is done by computing the trim
(e.g. 10
x |
original data provided as input. |
ensemble |
maximum entropy bootstrap replicates. |
xx |
sorted order stats (x[1] is minimum value). |
z |
class intervals limits. |
dv |
deviations of consecutive data values. |
dvtrim |
trimmed mean of dv. |
xmin |
data minimum for ensemble=x[1]-dvtrim. |
xmax |
data x maximum for ensemble=x[n]+dvtrim. |
desintxb |
desired inteval means. |
ordxx |
ordered x values. |
elaps |
elapsed time. |
Vinod, H.D. (2006), Maximum Entropy Ensembles for Time Series Inference in Economics, Journal of Asian Economics, 17(6), pp. 955-978
Vinod, H.D. (2004), Ranking mutual funds using unconventional utility theory and stochastic dominance, Journal of Empirical Finance, 11(3), pp. 353-377.
## Ensemble for the AirPassenger time series data set.seed(345) out <- meboot(x=AirPassengers, reps=100, trim=0.10, elaps=TRUE) ## Ensemble for T=5 toy time series used in Vinod (2004) set.seed(345) out <- meboot(x=c(4, 12, 36, 20, 8), reps=999, trim=0.25, elaps=TRUE) mean(out$ens) # ensemble mean should be close to sample mean 16