README

The multilateral package provides one key function, that is multilateral(). The user provides the necessary attributes of a dataset to calculate their choice of multilateral methods.

For some specific index calculation methods this package has been heavily influenced by Graham White’s IndexNumR package.

Installation

devtools::install_github("MjStansfi/multilateral")

library(multilateral)

Usage

library(multilateral)
library(ggplot2)

tpd_index <- multilateral(period = turvey$month,
                          id = turvey$commodity,
                          price = turvey$price,
                          quantity = turvey$quantity,
                          splice_method = "geomean",
                          window_length = 13,
                          index_method = "TPD")

plot <- ggplot(tpd_index$index)+geom_line(aes(x = period, y = index))+theme_bw()

print(plot)

Further detail

str(tpd_index) 
#> List of 3
#>  $ index        :Classes 'data.table' and 'data.frame':  48 obs. of  3 variables:
#>   ..$ period   : Date[1:48], format: "1970-01-31" "1970-02-28" ...
#>   ..$ index    : num [1:48] 1 0.971 0.949 1.047 1.308 ...
#>   ..$ window_id: int [1:48] 1 1 1 1 1 1 1 1 1 1 ...
#>   ..- attr(*, ".internal.selfref")=<externalptr> 
#>  $ index_windows:Classes 'data.table' and 'data.frame':  468 obs. of  3 variables:
#>   ..$ period   : Date[1:468], format: "1970-01-31" "1970-02-28" ...
#>   ..$ index    : num [1:468] 1 0.971 0.949 1.047 1.308 ...
#>   ..$ window_id: int [1:468] 1 1 1 1 1 1 1 1 1 1 ...
#>   ..- attr(*, ".internal.selfref")=<externalptr> 
#>  $ splice_detail:Classes 'data.table' and 'data.frame':  35 obs. of  5 variables:
#>   ..$ period                : Date[1:35], format: "1971-02-28" "1971-03-31" ...
#>   ..$ latest_window_movement: num [1:35] 0.97 1.012 1.097 1.195 0.949 ...
#>   ..$ revision_factor       : num [1:35] 1 1 1 1.01 1.02 ...
#>   ..$ update_factor         : num [1:35] 0.972 1.013 1.099 1.205 0.966 ...
#>   ..$ window_id             : int [1:35] 2 3 4 5 6 7 8 9 10 11 ...
#>   ..- attr(*, ".internal.selfref")=<externalptr> 
#>  - attr(*, "class")= chr [1:2] "list" "multilateral"
#>  - attr(*, "params")=List of 6
#>   ..$ index_method    : chr "TPD"
#>   ..$ window_length   : num 13
#>   ..$ splice_method   : chr "geomean"
#>   ..$ chain_method    : NULL
#>   ..$ check_inputs_ind: logi TRUE
#>   ..$ matched         : NULL

The index_windows returns all individual windows indexes before they were spliced. Below shows how you could (roughly) visualise this data

library(dplyr)
#> Warning: package 'dplyr' was built under R version 4.0.5
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

#Get splice details to relevel each new index
update_factor <- tpd_index$splice_detail%>%
  mutate(update_factor  = cumprod(update_factor))%>%
  select(window_id, update_factor)


index_windows <- merge(tpd_index$index_windows,update_factor)

index_windows <-index_windows%>%mutate(updated_index = index*update_factor)
windows_plot <- ggplot(index_windows)+
  geom_line(aes(x = period, y = updated_index, group = window_id, colour = window_id))+
  theme_bw()

print(windows_plot)

splice_detail gives the user a break down of how the given periods index number is made up of both a ‘revision factor’ (from splicing) and the latest periods movement. This can be useful for diagnostics.

head(tpd_index$splice_detail)
#>        period latest_window_movement revision_factor update_factor window_id
#> 1: 1971-02-28              0.9698029        1.002095     0.9718351         2
#> 2: 1971-03-31              1.0120421        1.001120     1.0131760         3
#> 3: 1971-04-30              1.0973656        1.001151     1.0986290         4
#> 4: 1971-05-31              1.1950159        1.008111     1.2047081         5
#> 5: 1971-06-30              0.9490383        1.017805     0.9659356         6
#> 6: 1971-07-31              1.0336941        1.004028     1.0378582         7

Below shows one way in which you could visualise contribution of revision factor verses the latest movement.

library(dplyr)

#Period of interest
splice_detail <- tpd_index$splice_detail[period=="1973-02-28"]

#Log information to determine contribution
lwm_log <- log(splice_detail$latest_window_movement)
rf_log <- log(splice_detail$revision_factor)
sum_log <- sum(lwm_log+rf_log)

lwm_contrib <- lwm_log/sum_log
rf_contrib <- rf_log/sum_log


ggplot(mapping = aes(fill=c("Latest movement","Revision factor"),
                     y=c(lwm_contrib,rf_contrib),
                     x="1973-02-28"))+
  geom_bar(position="stack", stat="identity", width = 0.2)+
  theme_bw()+
  xlab("Date")+
  ylab("% Contribution")+
  labs(fill = "Contributor")+
  scale_fill_manual(values = c("#085c75","#d2ac2f"))

Method	Name	Requires ID	Requires Features	Requires Quantity	Requires Weight	Can Restrict to Matched Sample
TPD	Time Product Dummy	TRUE	FALSE	FALSE	TRUE	FALSE
TDH	Time Dummy Hedonic	FALSE	TRUE	FALSE	TRUE	FALSE
GEKS-J	GEKS Jevons	TRUE	FALSE	FALSE	FALSE	TRUE
GEKS-T	GEKS Tornqvist	TRUE	FALSE	TRUE	FALSE	TRUE
GEKS-F	GEKS Fisher	TRUE	FALSE	TRUE	FALSE	TRUE
GEKS-IT	GEKS Imputation Tornqvist	TRUE	TRUE	TRUE	FALSE	TRUE

Multilateral

Overview

Installation

Usage

Further detail

Options

splice_method
geomean
window
movement
geomean_short
half

chain_method
geomean
window
movement
half