Instrumental variables estimation tackles in impact evaluation. It uses exogenous variation to estimate causal effects when treatment variables are correlated with error terms. This method requires instruments that are relevant to the treatment but don't directly affect outcomes.
The process involves , first regressing treatment on the instrument, then using predicted values to estimate causal effects. This approach helps address issues like , measurement error, and reverse causality in econometric analysis.
Instrumental Variables Estimation
Concept and Purpose
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Instrumental variables estimation addresses endogeneity issues in causal inference and impact evaluation
Isolates exogenous variation in the treatment variable to estimate causal effects
Used when correlation exists between treatment variable and error term in regression model
Requires instruments satisfying relevance (correlation with treatment) and exclusion restriction (no direct effect on outcome)
Involves two-stage least squares (2SLS) process
First stage regresses treatment on instrument
Second stage uses predicted values to estimate causal effect
Allows consistent estimation of causal effects with omitted variable bias, measurement error, or reverse causality
Key Conditions and Process
Instrument must correlate with treatment variable ()
Instrument must not directly affect outcome variable (exclusion restriction)
Two-stage least squares (2SLS) estimation process
Stage 1: Regress treatment on instrument
Stage 2: Use predicted values to estimate causal effect