instrumenttimuuttujamenetelmiä

Instrumenttimuuttujamenetelmiä, often abbreviated as IV methods, are a class of econometric techniques used to address endogeneity in statistical models. Endogeneity occurs when an independent variable in a regression model is correlated with the error term, leading to biased and inconsistent parameter estimates. IV methods overcome this problem by employing instrumental variables, which are variables that are correlated with the endogenous independent variable but not directly with the error term.

The core idea behind IV methods is to use the variation in the endogenous variable that is explained by the instrument to identify the causal effect of that variable on the dependent variable. A common and widely used IV method is two-stage least squares (2SLS). In the first stage of 2SLS, the endogenous independent variable is regressed on the instrumental variable(s) and any exogenous independent variables. The predicted values from this regression are then used as a proxy for the endogenous variable in the second stage regression, where the dependent variable is regressed on these predicted values and the exogenous variables.

For an instrumental variable to be valid, it must satisfy two key conditions: relevance and exogeneity. Relevance means the instrument must be correlated with the endogenous independent variable. Exogeneity means the instrument must be uncorrelated with the error term in the main regression equation. If these conditions are met, IV methods can provide consistent estimates of the causal effects, even in the presence of endogeneity. Other IV estimators include limited information maximum likelihood (LIML) and generalized method of moments (GMM), which offer different properties and are sometimes preferred depending on the specific context and assumptions.