np (version 0.40-13)

npqcmstest: Kernel Consistent Quantile Regression Model Specification Test with Mixed Data Types

Description

npqcmstest implements a consistent test for correct specification of parametric quantile regression models (linear or nonlinear) as described in Racine (2006) which extends the work of Zheng (1998).

Usage

npqcmstest(formula,
           data = NULL,
           subset,
           xdat,
           ydat,
           model = stop(paste(sQuote("model")," has not been provided")),
           tau = 0.5,
           distribution = c("bootstrap", "asymptotic"),
           bwydat = c("y","varepsilon"),
           boot.method = c("iid","wild","wild-rademacher"),
           boot.num = 399,
           pivot = TRUE,
           density.weighted = TRUE,
           random.seed = 42,
           ...)

Arguments

formula

a symbolic description of variables on which the test is to be performed. The details of constructing a formula are described below.

data

an optional data frame, list or environment (or object coercible to a data frame by as.data.frame) containing the variables in the model. If not found in data, the variables are taken from

subset

an optional vector specifying a subset of observations to be used.

model

a model object obtained from a call to rq. Important: the call to rq must have the argument model=TRUE or npqcmstest

xdat

a $p$-variate data frame of explanatory data (training data) used to calculate the quantile regression estimators.

ydat

a one (1) dimensional numeric or integer vector of dependent data, each element $i$ corresponding to each observation (row) $i$ of xdat.

tau

a numeric value specifying the $\tau$th quantile is desired

distribution

a character string used to specify the method of estimating the distribution of the statistic to be calculated. bootstrap will conduct bootstrapping. asymptotic will use the normal distribution. Defaults to boot

bwydat

a character string used to specify ydat used in bandwidth selection. varepsilon uses $1-\tau,-\tau$ for ydat while y will use $y$. Defaults to y.

boot.method

a character string used to specify the bootstrap method. iid will generate independent identically distributed draws. wild will use a wild bootstrap. wild-rademacher will use a wild bootstrap with Rademac

boot.num

an integer value specifying the number of bootstrap replications to use. Defaults to 399.

pivot

a logical value specifying whether the statistic should be normalised such that it approaches $N(0,1)$ in distribution. data. Defaults to TRUE.

density.weighted

a logical value specifying whether the statistic should be weighted by the density of xdat. Defaults to TRUE.

random.seed

an integer used to seed R's random number generator. This is to ensure replicability. Defaults to 42.

...

additional arguments supplied to control bandwidth selection on the residuals. One can specify the bandwidth type, kernel types, and so on. To do this, you may specify any of bwscaling, bwtype, ckertype,

Value

• npqcmstest returns an object of type cmstest with the following components, components will contain information related to Jn or In depending on the value of pivot:
• Jn
  the statistic Jn
• In
  the statistic In
• Omega.hat
  as described in Racine, J.S. (2006).
• q.*
  the various quantiles of the statistic Jn (or In if pivot=FALSE) are in components q.90, q.95, q.99 (one-sided 1%, 5%, 10% critical values)
• P
  the P-value of the statistic
• Jn.bootstrap
  if pivot=TRUE contains the bootstrap replications of Jn
• In.bootstrap
  if pivot=FALSE contains the bootstrap replications of In
• summary supports object of type cmstest.

Usage Issues

If you are using data of mixed types, then it is advisable to use the data.frame function to construct your input data and not cbind, since cbind will typically not work as intended on mixed data types and will coerce the data to the same type.

References

Aitchison, J. and C.G.G. Aitken (1976), Multivariate binary discrimination by the kernel method, Biometrika, 63, 413-420.

Koenker, R.W. and G.W. Bassett (1978), Regression quantiles, Econometrica, 46, 33-50.

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

Murphy, K. M. and F. Welch (1990), Empirical age-earnings profiles, Journal of Labor Economics, 8, 202-229.

Pagan, A. and A. Ullah (1999), Nonparametric Econometrics, Cambridge University Press.

Racine, J.S. (2006), Consistent specification testing of heteroskedastic parametric regression quantile models with mixed data, manuscript.

Wang, M.C. and J. van Ryzin (1981), A class of smooth estimators for discrete distributions, Biometrika, 68, 301-309.

Zheng, J. (1998), A consistent nonparametric test of parametric regression models under conditional quantile restrictions, Econometric Theory, 14, 123-138.

Examples

# EXAMPLE 1: For this example, we conduct a consistent quantile regression
# model specification test for a parametric wage quantile regression
# model that is quadratic in age. The work of Murphy and Welch (1990)
# would suggest that this parametric quantile regression model is
# misspecified.

library("quantreg")

data("cps71")
attach(cps71)

model <- rq(logwage~age+I(age^2), tau=0.5, model=TRUE)

plot(age, logwage)
lines(age, fitted(model))

X <- data.frame(age)

# Note - this may take a few minutes depending on the speed of your
# computer...

npqcmstest(model = model, xdat = X, ydat = logwage, tau=0.5)

# Sleep for 5 seconds so that we can examine the output...

Sys.sleep(5)

# Next try Murphy & Welch's (1990) suggested quintic specification.

model <- rq(logwage~age+I(age^2)+I(age^3)+I(age^4)+I(age^5), model=TRUE)

plot(age, logwage)
lines(age, fitted(model))

X <- data.frame(age)

# Note - this may take a few minutes depending on the speed of your
# computer...

npqcmstest(model = model, xdat = X, ydat = logwage, tau=0.5)

detach(cps71)