Description Usage Arguments Details Value Please cite as: Author(s) References See Also Examples
View source: R/quantification.R
ce
implements the Conditional Expectations approach for the quantification of qualitative survey data. The method calculates expectations on a distribution of past realizations of the variable of interest (variable y
), conditional on the expectation of either an increase or a decrese in y
. These conditional expectations are then weighted with the share of survey respondents expecting variable y
to rise or fall, respectively. For details see
1 2 3 4 5 
y.series 
a numerical vector containing the variable whose change is the subject of the qualitative survey question. If, for example the survey asks participants to assess whether inflation will increase, decrease or stay the same, 
survey.up 
a numerical vector containing the number or the share of survey respondents expecting the variable contained in 
survey.same 
a numerical vector containing the number or the share of survey respondents expecting the variable contained in 
survey.down 
a numerical vector containing the number or the share of survey respondents expecting the variable contained in 
forecast.horizon 
a numeric value defining the number of periods the survey question looks in to the future. If the data in 
first.period 
an optional numeric value indexing the first period for which survey data in 
last.period 
an optional numeric value indexing the last period for which survey data in 
exp.horizon.type 
an optional character vector indicating the type of experience horizon to be used. The experience horizon is the time period over which the distribution of variable
Default value is " 
mov.horizon.length 
an optional numeric value indicating the length of the (moving) forecast horizon. Is only considered when 
fix.horizon.start 
an optional numeric value indicating the first period of the (fixed) forecast horizon. Is only considered when 
fix.horizon.end 
an optional numeric value indicating the last period of the (fixed) forecast horizon. Is only considered when 
distrib.param 
an optional character vector indicating the distribution parameter that shall be used for calculating conditional expectations based on the distribution of variable 
suppress.warnings 
a logical value indicating if runtime warnings shall be displayed ( 
The survey result vectors survey.up
, survey.down
and survey.same
as well as the variable y.series
must be of the same length and must cover the forecasted horizon (i.e. last.period
+ forecast.horizon
≤ length(survey.up)
).
Data in survey.up
, survey.down
and survey.same
outside the survey period interval [first.period, last.period]
are ignored. Similiarly, y.series
data with a period index greater than last.period
is ignored.
survey.up
, survey.down
and survey.same
need not sum up to 100%
or 1
(which may happen, for example, if the survey has a 'Don't know' answer option).
ce
returns a list containing the quantified survey data and some meta information. The list has the following elements:
y.e
: a numeric vector containing the quantified expectations of the variable y
.
nob
: a numeric value showing the number of periods for which expectations have been quantified.
mae
: a numeric value showing the mean absolute error (MAE) of expectations.
rmse
: a numeric value showing the root mean squared error (RMSE) of expectations.
Zuckarelli, Joachim (2014). Quantification of qualitative survey data in R.
R package version 1.0.0. http://CRAN.Rproject.org/package=quantification
Joachim Zuckarelli, [email protected]
Zuckarelli, J. (2015): A new method for quantification of qualitative expectations, Economics and Business Letters 3(5), Special Issue Energy demand forecasting, 123128.
quantificationpackage
, cp
, bal
, ra
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  ## Data preparation: generate a sample dataset with inflation and survey data
inflation<c(1.5, 1.5, 1.5, 1.1, 0.9, 1.3, 1.3, 1.2, 1.7, 1.7, 1.5, 2, 1.4, 1.9, 1.9, 2.3, 2.8,
2.5, 2.1, 2.1, 1.9, 1.9, 1.5, 1.6, 2.1, 1.8, 2.1, 1.5, 1.3, 1.1, 1.1, 1.3, 1.3, 1.3, 1.1,
1.1, 1, 1.2, 1.1, 0.9)
answer.up<c(72.7, 69.7, 60.9, 53.7, 54.9, 54.8, 56.1, 51.7, 62.2, 54.2, 39.8, 18.6, 5.4, 8.2,
8.6, 8.5, 16, 18.9, 7.7, 6.5, 6.4, 7, 7.4, 6.8, 9.5, 17.1, 13.1, 21.5, 22.7, 26.9, 32.4,
20.2, 20.4, 15.8, 11.4, 7.9, 11.3, 10, 11.3, 9.7)
answer.same<c(24.1, 22.8, 24.3, 26.2, 31.1, 35.4, 33, 35.5, 27.4, 24.8, 32.1, 44.8, 41.8,
37.9, 33.2, 30.9, 29.9, 22.1, 17.2, 15.5, 21.8, 25.2, 23.2, 24.2, 32.9, 31.2, 42.2, 50.5,
52.5, 56.3, 53.8, 62.8, 65.6, 63, 60.3, 61.1, 57.8, 63, 61.4, 61.9)
answer.down<c(3.2, 7.5, 14.8, 20.1, 14, 9.8, 10.9, 12.8, 10.4, 21, 28.1, 36.6, 52.8, 53.9,
58.2, 60.6, 54.1, 59, 75.1, 78, 71.8, 67.8, 69.4, 69, 57.6, 51.7, 44.7, 28, 24.8, 16.8,
13.8, 17, 14, 21.2, 28.3, 31, 30.9, 27, 27.3, 28.4)
## Call ce for quantification
quant.ce<ce(inflation, answer.up, answer.same, answer.down, first.period=30, last.period=36,
forecast.horizon=4, exp.horizon.type = "fix", fix.horizon.start = 1, fix.horizon.end = 29)

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