npiv (version 0.1.3)

Engel95: 1995 British Family Expenditure Survey

Description

This dataset is based on a sample taken from the British Family Expenditure Survey for 1995. It includes households consisting of married or cohabiting couples with an employed head of household, aged between 25 and 55 years, and with at most two children. There are 1655 household-level observations in total.

Usage

data("Engel95")

Format

A data frame with 10 columns, and 1655 rows.

food

expenditure share on food, of type numeric

catering

expenditure share on catering, of type numeric

alcohol

expenditure share on alcohol, of type numeric

fuel

expenditure share on fuel, of type numeric

motor

expenditure share on motor, of type numeric

fares

expenditure share on fares, of type numeric

leisure

expenditure share on leisure, of type numeric

logexp

logarithm of total expenditure, of type numeric

logwages

logarithm of total earnings, of type numeric

nkids

`0` indicates no children, `1` indicates 1-2 children, of type numeric

References

Blundell, R., X. Chen and D. Kristensen (2007). “Semi-Nonparametric IV Estimation of Shape-Invariant Engel Curves.” Econometrica, 75(6), 1613-1669. tools:::Rd_expr_doi("10.1111/j.1468-0262.2007.00808.x")

Chen, X. and T. Christensen (2018). “Optimal Sup-norm Rates and Uniform Inference on Nonlinear Functionals of Nonparametric IV Regression.” Quantitative Economics, 9(1), 39-85. tools:::Rd_expr_doi("10.3982/QE722")

Chen, X., T. Christensen and S. Kankanala (2024). “Adaptive Estimation and Uniform Confidence Bands for Nonparametric Structural Functions and Elasticities.” Review of Economic Studies, forthcoming. tools:::Rd_expr_doi("10.1093/restud/rdae025")

Examples

## Load data
data("Engel95", package = "npiv")

## Sort on logexp (the regressor) for plotting purposes
Engel95 <- Engel95[order(Engel95$logexp),] 
attach(Engel95)
logexp.eval <- seq(4.5,6.5,length=100)

## Estimate the Engel curve for food using logwages as an instrument
food_engel <- npiv(food, logexp, logwages, X.eval = logexp.eval)

## Plot the estimated function and uniform confidence bands
plot(food_engel, showdata = TRUE)