2. Instruments and Panel Data

Weak Instruments and LATE

Diagnose unstable IV ratios, weak-identification inference and complier-specific interpretation

Weak Instruments and LATE

A ratio becomes unstable when its denominator is small

The Wald estimator is

τ^=RF^FS^.\hat\tau=\frac{\widehat{RF}}{\widehat{FS}}.

If the first stage is 2 percentage points and the reduced form is 1 point, the ratio is 0.50. A one-point sampling change in the first stage moves it to 0.33 or 1.00. Conventional normal approximations can become misleading.

Py

Compare strong and weak IV ratios

Idle

Both designs use a valid simulated instrument. The weak design produces a much wider and more erratic ratio even with the same structural effect.

The first-stage F statistic is a warning, not a certificate

Rules such as “F above 10” are context-dependent. Instrument count, heteroskedasticity, clustering, multiple endogenous variables and the desired inferential guarantee matter.

Andrews, Stock and Sun (2019) review weak-instrument diagnostics and robust inference, including non-homoskedastic settings. Report the first-stage specification, relevant statistic and weak-IV-robust confidence set rather than one threshold alone.

LATE belongs to compliers

For binary ZZ and DD, principal strata are:

TypeD(0)D(0)D(1)D(1)Meaning
never-taker00never receives scholarship
complier01receipt changed by offer
always-taker11receives regardless
defier10does opposite of offer

Under monotonicity, the Wald ratio identifies

E[Y(1)Y(0)D(1)>D(0)],E[Y(1)-Y(0)\mid D(1)>D(0)],

the effect for compliers. Different instruments can move different people and identify different LATEs.

A strong instrument can answer a narrow question

If the offer affects only applicants uncertain about participation, the LATE may not describe always-takers, never-takers or applicants outside the eligibility band. Characterise compliers through first-stage heterogeneity and institutional knowledge, without pretending individual types are observed.

Sensitivity goes beyond strength

Even a strong instrument can violate exclusion or independence. Cinelli and Hazlett (2025) develop an omitted-variable-bias framework for IV sensitivity, separating possible side effects of the instrument and confounding of the instrument.

The teaching lesson is practical: report how strong an omitted pathway would need to be to change the conclusion, benchmarked against observed variables. Sensitivity analysis quantifies a violation; it does not prove the violation absent.

Quick check

Two valid instruments produce estimates of 0.10 and 0.30 with precise first stages. Must one be wrong?

Answer
No. With heterogeneous effects, the instruments may identify LATEs for different complier groups. Compare assignment mechanisms, first-stage populations and treatment margins before pooling or declaring conflict.

Next: Panel Fixed Effects

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