ArellanoBoverBlundellBond

ArellanoBoverBlundellBond refers to a family of econometric methods for estimating dynamic panel data models, named after key contributions by Arellano, Bover, and Blundell with Bond. These estimators are designed to handle panels with a short time dimension and a potentially endogenous lagged dependent variable, as well as unobserved individual effects and measurement error.

The core idea stems from two related approaches. Arellano and Bover (1995) introduced difference GMM, which first-differences the model to remove fixed effects and uses lagged levels as instruments for the differenced equation. This helps address endogeneity of the lagged dependent variable but can perform poorly when variables are highly persistent or instruments are weak. Blundell and Bond (1998) extended this with system GMM, adding a second, equation in levels and using lagged differences as instruments for that level equation. The system GMM estimator typically offers improved efficiency and better finite-sample performance, especially when the autoregressive parameter is near unity or when measurement error is present.

Typical dynamic panel model: y_it = α y_{i,t-1} + x_it'β + η_i + ε_it, where η_i is an unobserved unit effect. Estimation relies on valid instruments and moment conditions, with diagnostic tests such as Hansen’s overidentification test and tests for serial correlation in first-differenced residuals (AR(1) and AR(2)) to assess instrument validity and model specifications.

Applications span macroeconomics, finance, and development economics, including studies of investment, productivity, consumption persistence, and policy transmission. While powerful, these methods require careful instrument selection, adequate sample size in N and T, and attention to potential weak instruments and model assumptions.