Engel95 {np} | R Documentation

## 1995 British Family Expenditure Survey

### Description

British cross-section data consisting of a random sample taken from the British Family Expenditure Survey for 1995. The households consist of married couples with an employed head-of-household between the ages of 25 and 55 years. 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

  number of children, of type `numeric`

### Source

Richard Blundell and Dennis Kristensen

### References

Blundell, R. and X. Chen and D. Kristensen (2007), “Semi-Nonparametric IV Estimation of Shape-Invariant Engel Curves,” Econometrica, 75, 1613-1669.

Li, Q. and J.S. Racine (2007), Nonparametric Econometrics: Theory and Practice, Princeton University Press.

### Examples

    ## Not run:
    ## Example - compute nonparametric instrumental regression using
    ## Landweber-Fridman iteration of Fredholm integral equations of the
    ## first kind.

    ## We consider an equation with an endogenous regressor (`z') and an
    ## instrument (`w'). Let y = phi(z) + u where phi(z) is the function of
    ## interest. Here E(u|z) is not zero hence the conditional mean E(y|z)
    ## does not coincide with the function of interest, but if there exists
    ## an instrument w such that E(u|w) = 0, then we can recover the
    ## function of interest by solving an ill-posed inverse problem.

    data(Engel95)

    ## Sort on logexp (the endogenous regressor) for plotting purposes

    Engel95 <- Engel95[order(Engel95$logexp),]

    attach(Engel95)

    model.iv <- npregiv(y=food,z=logexp,w=logwages,method="Landweber-Fridman")
    phihat <- model.iv$phi

    ## Compute the non-IV regression (i.e. regress y on z)

    ghat <- npreg(food~logexp,regtype="ll")

    ## For the plots, restrict focal attention to the bulk of the data
    ## (i.e. for the plotting area trim out 1/4 of one percent from each
    ## tail of y and z)

    trim <- 0.0025

    plot(logexp,food,
         ylab="Food Budget Share",
         xlab="log(Total Expenditure)",
         xlim=quantile(logexp,c(trim,1-trim)),
         ylim=quantile(food,c(trim,1-trim)),
         main="Nonparametric Instrumental Kernel Regression",
         type="p",
         cex=.5,
         col="lightgrey")

    lines(logexp,phihat,col="blue",lwd=2,lty=2)

    lines(logexp,fitted(ghat),col="red",lwd=2,lty=4)

    legend(quantile(logexp,trim),quantile(food,1-trim),
           c(expression(paste("Nonparametric IV: ",hat(varphi)(logexp))),
             "Nonparametric Regression: E(food | logexp)"),
           lty=c(2,4),
           col=c("blue","red"),
           lwd=c(2,2))

    ## End(Not run)

[Package np version 0.60-9 Index]