Description Usage Arguments Details Value Please cite as: Author(s) References See Also Examples
View source: R/quantification.R
ra
implements the regression approach developed by Pesaran (1984).
1 2 3 4 5 6  ra(y.series, survey.up, survey.same, survey.down, forecast.horizon,
first.period = 1, last.period = (length(survey.up)  forecast.horizon),
distrib.type = "normal", distrib.mean = 0, distrib.sd = 1,
distrib.log.location = 0, distrib.log.scale = 1,
distrib.t.df = (first.period  last.period), growth.limit = NA,
symmetry.error = "white", suppress.warnings = FALSE)

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 
distrib.type 
an optional character vector describing the type of distribution used for quantification. Possible values are:

distrib.mean 
an optional numerical value defining the mean of the normal distribution (used in case 
distrib.sd 
an optional numerical value defining the standard deviation of the normal distribution (used in case 
distrib.log.location 
an optional numerical value defining the location of the logistic distribution (used in case 
distrib.log.scale 
an optional numerical value defining the scale of the logistic distribution (used in case 
distrib.t.df 
an optional numerical value defining the degrees of freedom (df) of the t distribution (used in case 
growth.limit 
serves to limit the effect of outliers when expectations are quantified under the assumption that survey respondents form expectations on the percentage change of 
symmetry.error 
an optional character vector indicating the type of standard error used for testing the symmetry of the estimated upper and lower indifference limens. Can be either " 
suppress.warnings 
a logical value indicating if runtime warnings shall be displayed ( 
ra
estimates the timeinvariant, asymmetric indifference limens using OLS regression with nonrobust standard errors.
The function ra
provides two alternative versions of quantified expectations, depending on the assumed expectation formation process of survey respondents. The basic common assumption of the regression approach is that survey participants are asked to assess whether variable y
will go up or down or stay the same. Survey respondents can now form expectations on either the absolute or the relative change of y
. The reg
function calculates both versions.
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).
y.e.mean.abs
: a numeric vector containing the quantified mean expectations of the variable y
, assuming that survey respondents form expectations on the absolute change in y
. For all periods which are not in scope of the survey the value is NA
.
y.e.mean.perc
: a numeric vector containing the quantified mean expectations of the variable y
, assuming that survey respondents form expectations on the relative change in y
. For all periods which are not in scope of the survey the value is NA
.
delta.y.e.mean.abs
: a numeric vector containing the quantified mean absolute change of the variable y
, assuming that survey respondents form expectations on the absolute change in y
. For all periods which are not in scope of the survey the value is NA
.
delta.y.e.mean.perc
: a numeric vector containing the quantified mean percentage change of the variable y
, assuming that survey respondents form expectations on the relative change in y
. For all periods which are not in scope of the survey the value is NA
.
upper.limit.abs
: a numeric value containing the estimated upper indifference limen when survey respondents form expectations on the absolute change in variable y
.
lower.limit.abs
: a numeric value containing the estimated upper indifference limen when survey respondents form expectations on the absolute change in variable y
.
upper.limit.per
: a numeric value containing the estimated upper indifference limen when survey respondents form expectations on the relative change in variable y
.
lower.limit.perc
: a numeric value containing the estimated upper indifference limen when survey respondents form expectations on the relative change in variable y
.
nob
: a numeric value showing the number of periods for which expectations have been quantified.
mae.abs
: a numeric value showing the mean absolute error (MAE) of expectations when survey respondents form expectations on the absolute change in variable y
.
rmse.abs
: a numeric value showing the root mean squared error (RMSE) of expectations when survey respondents form expectations on the absolute change in variable y
.
mae.perc
: a numeric value showing the mean absolute error (MAE) of expectations when survey respondents form expectations on the relative change in variable y
.
rmse.perc
: a numeric value showing the root mean squared error (RMSE) of expectations when survey respondents form expectations on the relative change in variable y
.
symmetry.abs
: a numeric value containing the pvalue of the test for symmetry of the estimated indifference limens when survey respondents form expectations on the absolute change in variable y
. The standard error used for testing depends on the argument symmetry.error
.
symmetry.perc
: a numeric value containing the pvalue of the test for symmetry of the estimated indifference limens when survey respondents form expectations on the relative change in variable y
. The standard error used for testing depends on the argument symmetry.error
.
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]
MacKinnon, J.G./White, H. (1985), Some heteroskedasticityconsistent covariance matrix estimators with immproved finite sample properties, Journal of Econometrics 29, 305–325.
Pesaran, M. (1984), Expectations formation and macroeconomic modelling, in: Malgrange, M. (1984), Contemporary macroeconomic modelling, 27–55.
quantificationpackage
, cp
, bal
, ce
1 2 3 4 5 6 7 8 9  ## Data preparation: generate a sample dataset with inflation and survey data
inflation<c(1.7, 1.9, 2, 1.9, 2, 2.1, 2.1, 2.1, 2.4, 2.3, 2.4)
answer.up<c(67, 75.1, 76.4, 72.4, 69.7, 49.7, 45.2, 31.6, 14.9, 19.3, 19.2)
answer.same<c(30.1, 19.6, 19.5, 21.3, 20.1, 33.1, 34.4, 33.5, 44.6, 38.1, 35.3)
answer.down<c(2.9, 5.3, 4.1, 6.3, 10.2, 17.2, 20.4, 34.9, 40.5, 42.6, 45.5)
## Call ra for quantification
quant.ra<ra(inflation, answer.up, answer.same, answer.down, first.period=5,
last.period=7, forecast.horizon=4, symmetry.error="small.sample")

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