dineq (version 0.1.0)

rif: Recentered influence function (RIF)

Description

Returns the (optional weighted) recentered influence function of a distributional statistic.

Usage

rif(x, weights = NULL, method = "quantile", quantile = 0.5,
  kernel = "gaussian")

Arguments

x

a numeric vector for which the recentered influence function is computed.

weights

an optional vector of weights of x to be used in the computation of the recentered influence function. Should be NULL or a numeric vector.

method

the distribution statistic for which the recentered influence function is estimated. Options are "quantile", "gini" and "variance". Default is "quantile".

quantile

quantile to be used when method "quantile" is selected. Must be a numeric between 0 and 1. Default is 0.5 (median). Only a single quantile can be selected.

kernel

a character giving the smoothing kernel to be used in method "quantile". Options are "gaussian", "rectangular", "triangular", "epanechnikov", "biweight", "cosine" or "optcosine". Default is "gaussian".

Value

A numeric vector of the recentered influence function of the selected distributional statistic.

Details

The RIF can be used as input for a RIF regression approach. RIF regressions are mostly used to estimate the marginal effect of covariates on distributional statistics of income or wealth.

The RIF is calculated by adding the distributional statistic (quantile, gini or variance) to the influence function. RIF is a numeric vector where each element corresponds to a particular individual<U+2019>s influence on the distributional statistic.

References

Firpo, S., N. Fortin and T. Lemieux (2009) Unconditional quantile regressions. Econometrica, 77(3), p. 953-973.

Heckley G, U.-G. Gerdtham U-G and G. Kjellsson (2016) A general method for decomposing the causes of socioeconomic inequality in health. Journal of Health Economics,48, p. 89<U+2013>106.

Pereira, J. and A. Galego (2016) The drivers of wage inequality across Europe, a recentered influence function regression approach, 10th Annual Meeting of the Portuguese Economic Journal, University of Evora.

See Also

rifr

Examples

# NOT RUN {
data(mex_inc_2008)

#Recentered influence funtion of 20th quantile
rif_q20 <- rif(x=mex_inc_2008$income, weights=mex_inc_2008$factor, method="quantile",
quantile=0.2)

#Recentered influence funtion of the gini coefficient
rif_gini <- rif(x=mex_inc_2008$income, weights=mex_inc_2008$factor, method="gini")

# }