# Quadratic term and variance inflation factor in OLS estimation

One question I have carried around with me for a while is related to including quadratic terms for model specifications. I wonder why it is considered ok to include linear and quadratic terms into OLS analysis, despite the fact that the Variance Inflation Factor (VIF) gets high. A model specification that I was taught to be fine to use is the following:

$y = c + x + x^2 + \epsilon$

But this model has quite high numerical linear correlation between $x$ and $x^2$ and I do not understand why in this case the VIF does not matter.

An example for what I mean using R is the following:

# Load packages
require(car)
requre(lmtest)

# Create artificial data
dat <- data.frame(x = 1:1000, x2 = (1:1000)^2)
dat$y <- dat$x2 + rnorm(1000, 0, 10)

# Run model with linear term
dat.lm1 <- lm(y ~ x, dat)

# Run model with linear and quadratic term
dat.lm2 <- lm(y ~ x + x2, dat)


In this setup, the RESET test suggests using quadratic terms but the VIF is high.

> reset(dat.lm1)
RESET test
data:  dat.lm1
RESET = 27344126617, df1 = 2, df2 = 996, p-value < 2.2e-16

> reset(dat.lm2)
RESET test
data:  dat.lm2
RESET = 0.4404, df1 = 2, df2 = 995, p-value = 0.6439

> vif(dat.lm2)
x       x2
16.03008 16.03008


So the question is: Is this really a model specification that is valid and if yes, why does the VIF not matter in that case?

# norm x to mean zero
dat$x_n <- dat$x - mean(dat$x) dat$x2_n <- (dat$x_n)^2 dat.lm2_n <- lm(y ~ x_n + x2_n, dat)  This transformation doesn't affect your residuals but you get smaller confidence intervals. > summary(dat.lm2) Call: lm(formula = y ~ x + x2, data = dat) Residuals: Min 1Q Median 3Q Max -37.505 -7.182 -0.146 7.016 36.599 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.967e-01 9.626e-01 0.620 0.535 x -1.185e-03 4.441e-03 -0.267 0.790 x2 1.000e+00 4.296e-06 232752.480 <2e-16 *** --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1   1 Residual standard error: 10.13 on 997 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: 1 F-statistic: 4.342e+11 on 2 and 997 DF, p-value: < 2.2e-16 > summary(dat.lm2_n) Call: lm(formula = y ~ x_n + x2_n, data = dat) Residuals: Min 1Q Median 3Q Max -37.505 -7.182 -0.146 7.016 36.599 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.505e+05 4.804e-01 521493 <2e-16 *** x_n 1.001e+03 1.109e-03 902349 <2e-16 *** x2_n 1.000e+00 4.296e-06 232752 <2e-16 *** --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1   1 Residual standard error: 10.13 on 997 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: 1 F-statistic: 4.342e+11 on 2 and 997 DF, p-value: < 2.2e-16  And the VIF shrinks of course: > vif(dat.lm2) x x2 16.03008 16.03008 > vif(dat.lm2_n) x_n x2_n 1 1  • why is confidence interval smaller after the transformation? – FMZ Commented Feb 24, 2012 at 5:55 • @FMZ: The variance estimate can be expressed so that it includes$R^2\$ of a regression of the covariate using all other covariates. Therefore, if you reduce this factor the estimated variance will shrink and hence the CIs.