frm.ggoff: GGOFF Tests for Fractional Regression Models

In frm: Regression Analysis of Fractional Responses

Description

frm.ggoff is used to test the specification of fractional regression models.

Usage

frm.ggoff(object, version = "LM", table = T, ...)

Arguments

object	an object containing the results of an frm command.
version	a vector containing the test versions to use. Available options: Wald, LM (the default) and, only for the binary component of two-part models, LR. More than one option may be chosen.
table	a logical value indicating whether a summary table with the test results should be printed.
...	Arguments to pass to glm, which is used to estimate the model under the alternative hypothesis when version is a vector containing "Wald" or "LR".

Details

frm.ggoff applies the GGOFF, GOFF1 and GOOFF2 test statistics to fractional regression models estimated via frm. frm.ggoff may be used to test the link specification of: (i) one-part fractional regression models; (ii) the binary component of two-part fractional regression models; and (iii) the fractional component of two-part fractional regression models. When the Wald version is implemented, it is taken into account the option that was chosen for computing standard errors in the model under evaluation. For the LM version, a robust version is computed in cases (i) and (iii) and a conventional version in case (ii). See Ramalho, Ramalho and Murteira (2014) for details on the application of the GGOFF, GOFF1 and GOOFF2 tests in the fractional regression framework.

Value

frm.ggoff returns a named vector with the test results.

Author(s)

Joaquim J.S. Ramalho <[e-mail]>

References

Ramalho, E.A., J.J.S. Ramalho and J.M.R. Murteira (2014), "A generalized goodness-of-functional form test for binary and fractional regression models", Manchester School, 82(4), 488-507.

See Also

frm, for fitting fractional regression models.
frm.reset, for asymptotically equivalent specification tests.
frm.ptest, for non-nested hypothesis tests.
frm.pe, for computing partial effects.

Examples

N <- 250
u <- rnorm(N)

X <- cbind(rnorm(N),rnorm(N))
dimnames(X)[[2]] <- c("X1","X2")

ym <- exp(X[,1]+X[,2]+u)/(1+exp(X[,1]+X[,2]+u))
y <- rbeta(N,ym*20,20*(1-ym))
y[y > 0.9] <- 1

#Testing the logit specification of a standard fractional regression model
#using LM and Wald versions of the GGOFF test, based on 1 or 2 fitted powers of
#the linear predictor
res <- frm(y,X,linkfrac="logit",table=FALSE)
frm.ggoff(res,c("Wald","LM"))

#Testing the probit specification of the binary component of a two-part fractional
#regression model using a LR-based GGOFF test
res <- frm(y,X,linkbin="probit",type="2Pbin",inf=1,table=FALSE)
frm.ggoff(res,"LR")

## See the website http://evunix.uevora.pt/~jsr/FRM.htm for more examples.

Example output

*** GGOFF test ***

H0:  Fractional logit model

  Test Version Statistic p-value 
 GOFF1      LM     0.005   0.943 
 GOFF1    Wald     0.005   0.943 
 GOFF2      LM     0.070   0.792 
 GOFF2    Wald     0.068   0.794 
 GGOFF      LM     0.626   0.731 
 GGOFF    Wald     0.507   0.776 


*** GGOFF test ***

H0:  Binary probit component of a two-part model

  Test Version Statistic p-value 
 GOFF1      LR     0.219   0.640 
 GOFF2      LR     0.708   0.400 
 GGOFF      LR     4.205   0.122 

frm documentation built on May 2, 2019, 12:40 p.m.