| Type: | Package |
| Title: | Autoregressive Integrated Moving Average (ARIMA) Based Disaggregation Methods |
| Version: | 0.1.0 |
| Description: | We have the code for disaggregation as found in Wei and Stram (1990, <doi:10.1111/j.2517-6161.1990.tb01799.x>), and Hodgess and Wei (1996, "Temporal Disaggregation of Time Series" in Statistical Science I, Nova Publishing). The disaggregation models have different orders of the moving average component. These are based on ARIMA models rather than differencing or using similar time series. |
| Depends: | R (≥ 4.5), polynom, ltsa, zoo, xts, tsbox,tswge |
| License: | GPL-2 | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2026-01-26 18:57:57 UTC; e |
| Author: | Erin Hodgess [aut, cre] |
| Maintainer: | Erin Hodgess <erinm.hodgess@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2026-01-30 11:00:14 UTC |
Lower Bound Disaggregation Method Function
Description
This uses the Lower Bound method for temporal disaggregation of time series
Usage
lower3(x, m = 1)
Arguments
x |
Aggregate Series; must be a ts, xts, or zoo object |
m |
order of disaggregation; 3, 4, 12 |
Details
This function uses the lower bound method found in Hodgess and Wei (1996, "Temporal Disaggregation of Time Series"). We fit an aggregate (p,d,q) model, and produce a disaggregate model of (p,d,0). We generate the disaggregate series based on the disaggregate model.
Value
bigy |
order of the disaggregate model |
fin1 |
final disaggregate series |
Author(s)
Erin Hodgess
References
Hodgess and Wei (1996, "Temporal Disaggregation of Time Series"), in M. Ahsanullah and D. Bhoj (Eds), "Applied Statistical Science I".
Examples
library(tswge)
data(tx.unemp.adj)
#Monthly seasonally adjusted Texas unemployment data
#Create a quarterly sum
my.un.q <- aggregate(tx.unemp.adj,nfreq=4)
e.low <- lower3(my.un.q,3)
sum(e.low$fin1[1:3])
my.un.q[1]
Upper Bound Disaggregation Method Function
Description
This uses the Upper Bound method for temporal disaggregation of time series
Usage
upper3(x, m = 1)
Arguments
x |
Aggregate Series; must be a ts, xts, or zoo object |
m |
order of disaggregation; 3, 4, 12 |
Details
This function uses the upper bound method found in Hodgess and Wei (1996, "Temporal Disaggregation of Time Series"). We fit an aggregate (p,d,q) model, and produce a disaggregate model of (p,d,(p+d)). We generate the disaggregate series based on the disaggregate model.
Value
bigy |
order of the disaggregate model |
fin1 |
final disaggregate series |
Author(s)
Erin Hodgess
References
Hodgess and Wei (1996, "Temporal Disaggregation of Time Series"), in M. Ahsanullah and D. Bhoj (Eds), "Applied Statistical Science I".
Examples
library(tswge)
data(tx.unemp.adj)
#Monthly seasonally adjusted Texas unemployment data
#Create a quarterly sum
my.un.q <- aggregate(tx.unemp.adj,nfreq=4)
e.upp <- upper3(my.un.q,3)
sum(e.upp$fin1[1:3])
my.un.q[1]
Wei Stram Disaggregation Method Function
Description
This uses the Wei Stram method for temporal disaggregation of time series
Usage
weidis3(x, m = 1)
Arguments
x |
Aggregate Series; must be a ts, xts, or zoo object |
m |
order of disaggregation; 3, 4, 12 |
Details
This function uses the method found in Wei and Stram (1990, <doi:10.1111/j.2517-6161.1990.tb01799.x>). We fit an aggregate (p,d,q) model, and produce a disaggregate model of (p,d,(p+d+1)). We generate the disaggregate series based on the disaggregate model.
Value
bigy |
order of the disaggregate model |
fin1 |
final disaggregate series |
Author(s)
Erin Hodgess
References
Wei and Stram (1990, <doi:10.1111/j.2517-6161.1990.tb01799.x>)
Examples
library(tswge)
data(tx.unemp.adj)
#Monthly seasonally adjusted Texas unemployment data
#Create a quarterly sum
my.un.q <- aggregate(tx.unemp.adj,nfreq=4)
e.wei <- weidis3(my.un.q,3)
sum(e.wei$fin1[1:3])
my.un.q[1]