rifrSE {dineq}

Inference of recentered influence function regression (RIF regression)

Description

Inference of a RIF Regression using a bootstrap method.

Usage

rifrSE(formula, data, weights = NULL, method = "quantile", quantile = 0.5,
  kernel = "gaussian", Nboot = 100, confidence = 0.95)

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). Only a single quantile 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".

Nboot

the number of bootstrap replicates. Default is 100.

confidence

significance level for estimation of the confidence interval of the fitted model. Default is 0.95.

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.

The standard errors, confidence intervals and Z- and P-values are calculated by using a standard bootstrap method (from boot package).

Value

A data frame containing the results of the RIF regression.

Coef

estimated coefficients of the original (non bootstrapped) RIF regression

lower

lower bound of confidence interval of estimated coefficient

upper

upper bound of confidence interval of estimated coefficient

SE

standard error

Z Value

Z value

P Value

P value

Signif

Significance codes of P: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

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. 89–106.

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 rifr

Examples

data(mex_inc_2008)

#Recentered influence funtion of 20th quantile
rifr_q <- rifrSE(income~hh_structure+education, data=mex_inc_2008, weights="factor",
method="quantile", quantile=0.2, kernel="gaussian", Nboot=100, confidence=0.95)

#Recentered influence funtion of the gini coefficient
rifr_gini <- rifrSE(income~hh_structure+education, data=mex_inc_2008, weights="factor",
method="gini", Nboot=100, confidence=0.95)