Weak Instruments

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					Econometrics: Weak Instruments

One criteria for an instrument to be valid is that it needs to be exogenous – in the
traditional simultaneous equations approach this occurs if they are excluded from the
equation of interest. But there is a second criteria. – instrumental relevance. Recent
work suggests that many applications of instrumental variables (IV) regressions suffer
from “weak instruments” or “weak identification”. That is instruments are only
weakly correlated with the included endogenous variables.

The linear IV regression model with a single endogenous regressor and no included
exogenous variables is:

(1) y = Yβ + u


(2) Y = ZΠ + v

Where y and Y are Tx1 vectors of observations on endogenous variables, Z is a TxK
matrix of instruments and u and v are Tx1 error vectors. The errors are assumed to be
iid (idenpendent and identical districted) N(0, Σ) where the elements of Σ are σ2u, σ2uv
and σ2v . Let ρ = σ2uv/ (σu σv) – the correlation between the error terms. Equation (1) is
the structural equation, β the scalar parameter and (2) relates the endogenous
regressor to the instruments.

The Concentration parameter
The concentration parameter μ2 is a measure of the strength of the instruments:

(3) μ2 = Π′ZZ Π/ σ2v

μ2 can be thought of in terms of the F statistic for testing the hypothesis Π=0 (i.e. the
instruments are of no value). For large values of μ2/K, F-1 can be thought of as an
estimator of μ2/K.

We can also link the bias in the 2SLS estimator to μ2 via the following formula:

A practical approach to detecting weak instruments is to define a set of instruments to
be weak if μ2/K is small enough that inferences based on conventional normal
distributions are misleading. For hypothesis testing one could define instruments to be
strong if μ2/K is large enough that a 5% hypothesis test rejects no more than [say]
15% of the time.

The F statistic is useful for making inferences about μ2/K. However, simply using this
to test the hypothesis of non-identification (Π=0) is an inadequate test for weak
instruments. Instead Stock and Yogo propose using F to test the hypothesis that μ2/K
is less than or equal to the weak instrument threshold. Table 1 reports threshold

Table 1: Test for 5% test>15%
               Threshold μ2/K          Threshold F statistic
1             1.82                    8.96
2             4.62                    11.59
3             6.36                    12.83
5             9.20                    15.09
10            15.55                   20.88
15            21.69                   26.80
Source Stock and Yogo

This can be extended to the case of multiple endogenous variables.


In an influential article Angrist and Krueger !991) proposed using the quarter of birth
as an instrument to circumvent bias in estimating the returns to education. This is
exogenous and should be uncorrelated with ability (the difficulty in measuring the
impact of education is that ability os an omitted variable with which education is
correlated, hence the coefficient on education ina wage equation will overestimate
the impact of education per se). With a large sample of in excess of 300,000 people
the instruments they use are however weak and their results misleading illustrating
that this is not jts a small sample problem.