Kiinteävaikutusmalli

Kiinteävaikutusmalli, known in English as the Fixed Effects Model, is a statistical approach used primarily in panel data analysis. It is designed to control for unobserved heterogeneity that is constant over time for each individual unit. This unobserved heterogeneity could be any characteristic of the individual that is not explicitly measured in the data but might influence the outcome variable.

The core idea behind the fixed effects model is to estimate the effects of independent variables by comparing individuals to themselves over time. It effectively removes the influence of time-invariant characteristics by differencing the data or by including individual-specific dummy variables. This allows researchers to isolate the causal impact of variables that change within individuals, even if those individuals possess unobserved, stable traits.

A common implementation is the within-group estimator, which essentially demeans each variable by subtracting the individual's average value from each observation. This process eliminates the fixed effects. Another method is to use least squares dummy variable (LSDV) regression, where a dummy variable is included for each individual unit. Both methods achieve the same estimable results for the coefficients of the time-varying independent variables.

The fixed effects model is particularly useful when there's concern that unobserved individual characteristics might be correlated with the independent variables, leading to omitted variable bias in simpler models like ordinary least squares (OLS) with pooled data. However, it cannot estimate the effects of time-invariant independent variables because their influence is differenced away or captured by the individual dummies.