Econometrica · 2001 · 381 citations · 21 references
MusicEconometric ModelEconomicsParameter EstimationEngineeringMonte CarloInstrumental Variable EstimatorsStatistical FoundationBusinessEconometricsValid InstrumentsMusical AnalysisStatistical InferenceEstimation TheoryStatisticsInstrumental Variables
Properties of instrumental variable estimators are sensitive to the choice of valid instruments, even in large cross‑section applications. The paper derives simple mean‑square error criteria to optimally select instrument sets for IV estimation. The authors develop mean‑square error criteria for 2SLS, LIML, and bias‑adjusted 2SLS, deriving the MSE analytically and proving optimality of the selection rule. Monte Carlo experiments and an empirical returns‑to‑education study show that the proposed instrument‑selection rule improves performance and yields comparable large estimates for 2SLS and LIML.
Properties of instrumental variable estimators are sensitive to the choice of valid instruments, even in large cross-section applications. In this paper we address this problem by deriving simple mean-square error criteria that can be minimized to choose the instrument set. We develop these criteria for two-stage least squares (2SLS), limited information maximum likelihood (LIML), and a bias adjusted version of 2SLS (B2SLS). We give a theoretical derivation of the mean-square error and show optimality. In Monte Carlo experiments we find that the instrument choice generally yields an improvement in performance. Also, in the Angrist and Krueger (1991) returns to education application, when the instrument set is chosen in the way we consider, it turns out that both 2SLS and LIML give similar (large) returns to education.
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American Economic Review · 2012 · 6.8K citations
John Bound, David A. Jaeger, Regina Baker · Journal of the American Statistical Association · 1995 · 3.7K citations
Applied Economics, Endogenous Explanatory Variable, Education +19