Dependent variables, explanatory variables and the
disturbances can all contain stochastic trends.
Such stochastic trends can undermine the consistency and the
asymptotic normality of all the estimators.
When our usual estimators and test procedures are undermined, we need alternative methodsof
estimation and inference.
To know when to use such alternative methods, we need formal
tests to expose stochastic trends.
This discussion identifies the various cases which
stochastic trends make the Law of Large Numbers (LLN) or Central Limit Theorem
(CLT) inapplicable and undermine our usual procedure.
Lets we focus on data generating process (DGP) with one
explanator and an unknown intercept.
Yt=β0+β1Xt+ut (1)
which the disturbances are contemporaneously
uncorrelated with the explanators.
The explanators X may or may not contain a stochastic trend, and
the disturbances may or may not contain a stochastic trend.
Notice that, if the disturbances contain
a stochastic trend and X does not, Y must
contain a stochastic trend.
We assume that all nontrending variables
that appear in regression are measure as deviations from their means, and
therefore have a mean of zero.
There are four cases must be considered:
Case 1
When neither X or u contains a stochastic trend, ˆβ1 is consisten.
This is a standard result and the t-statistics
based on t-distribution, and for multiple regression, F-statistics
based on F-distribution and are asymptotically valid.
Case 2
When X contains a stochastic trend,
but u does not contain a stochastic trend, ˆβ1 converges
in probability to β1 .
Unfortunately, the asymptotic
distribution of ˆβ1 is not generally the normal
distribution, so our usual t-statistics and F-statistics are
generally not valid.
Then ˆβ1 is super
consistent , converging at faster that root n rate.
Case 3
When X contains no stochastic trend,
but u does contain a stochastic trend (an unlikely case to encounter in
practice), the numerator of
1T∑xtut1T∑x2t
has a term with an unbound variance, u
, and the denominator converges to a nonzero constant (under our usual
large-sample assumptions).
The LLN and CLT both fail in this case.
Instead of converging to β1 , the ˆβ1 converge to a random variable.
OLS is not consistent. Our usual tests
are invalid.
Case 4
When both X and u contain
stochastic trends, there are two terms with unbounded variances in both the
numerator and the denominator of
1T∑xtut1T∑x2t
The LLN and CLT both fail in this case.
Instead of converging to β1 ,the ˆβ1 converge to a random variable.
OLS is not consistent. Our usual tests
are invalid.
We call the regression of a
stochastically trending variable on another, unrelated, stochastically
trending
variable a spurious regression
because in this case, if β1=0, ordinary t-statistic will far to
too often reject a true null of β1=0.
In multiple regression model, if all the
parameter involved in a test fall into Case 1, the usual t-statistic and
F-statistics can be apply.
If one or more parameter does not fall into
Case 1, usual t-statistic and F-statistics are not valid where
the usual t-distribution and F-distribution provide incorrect
critical values.
If one or more parameter fall into Case 3
or 4, conventional test involving those parameters are inconsistent.
Econometrician have used Monte Carlo
methods to obtain valid critical value for the t-statistic and F-statistics.
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