dineq (version 0.1.0)

rifr: Recentered influence function regression (RIF Regression)

Description

Recentered influence function regression of a distributional statistic.

Usage

rifr(formula, data, weights = NULL, method = "quantile", quantile = 0.5,
  kernel = "gaussian")

Arguments

formula

an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted in the RIF regression.

data

a data frame containing the variables and weights of the model.

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. Should be inside selected data frame in the function and between quotation marks.

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). Multiple quantiles can be used.

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 list containing the results of the RIF regression.

coefficients

the coefficient estimates.

SE

the coefficient standard error.

t

the coefficient t-value.

p

the coefficient p-value.

adjusted_r2

the adjusted r-squares.

Details

RIF Regressions can be used to estimate the marginal effects of covariates on distributional statistics (such as quantiles, gini and variance). It is based on the recentered influence function of a statistic. The transformed RIF is used as the dependent variable in an ordinary least squares regression. RIF regressions are mostly used to estimate the marginal effect of covariates on distributional statistics of income or wealth.

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

rif rifrSE

Examples

# NOT RUN {
data(mex_inc_2008)

#Recentered influence funtion of each decile
rifr_q <- rifr(income~hh_structure+education, data=mex_inc_2008, weights="factor",
method="quantile", quantile=seq(0.1,0.9,0.1), kernel="gaussian")

#Recentered influence funtion of the gini coefficient
rifr_gini <- rifr(income~hh_structure+education, data=mex_inc_2008, weights="factor",
method="gini")


# }