ra implements the regression approach developed by Pesaran (1984).
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 would be the series of inflation data.
y.series to increase. This vector needs to be of the same length as y.series.
y.series to stay the same. This vector needs to be of the same length as y.series.
y.series to decrease. This vector needs to be of the same length as y.series.
y.series is monthly data and the survey question asks respondents to assess the development of the variable over the next six months then forecast.horizon=6.
survey.up, survey.same and survey.down shall be used for quantification; default value is 1.
survey.up, survey.same and survey.down shall be used for quantification; default value is length(survey.up) - forecast.horizon.
normal": the normal distribution is used. Default value for distrib.type. Parameters distrib.mean and distrib.sd can be used to specify the distribution.
logistic": the logistic distribution is used. Parameters distrib.log.location and distrib.log.scale can be used to specify the distribution.
t": the t distribution is used. Parameter distrib.t.df can be used to specify the distribution.
distrib.type="normal"). Default value is 0.
distrib.type="normal"). Default value is 1.
distrib.type="logistic"). Default value is 0.
distrib.type="logistic"). Default value is 1.
distrib.type="t"). Default value is last.period - first.period.
y. growth.limit defines a limit for percentage change of y. When this limit is exceeded the growth rate is set automatically to the median growth of y over the period covered by the expectations. Default value is NA.
white" (for White standard error) or "small.sample" (for small sample standard error, HC3), see MacKinnon/White (1985) for details. Default value is "white".
FALSE) or not (TRUE). Default value is FALSE.
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 p-value 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 p-value 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.
ra estimates the time-invariant, asymmetric indifference limens using OLS regression with non-robust 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 $\le$ 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).
Pesaran, M. (1984), Expectations formation and macroeconomic modelling, in: Malgrange, M. (1984), Contemporary macroeconomic modelling, 27--55.
quantification-package, cp, bal, ce
## 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")
Run the code above in your browser using DataLab