Dynamic Panel Data Estimation in Accounting Research: System GMM as a Benchmark for Financial Statement Analysis

Dynamic panel data methods, and System GMM in particular, have become a methodological reference point in accounting research, yet their diagnostic performance has rarely been validated against the structural conditions characteristic of Southeast European (SEE) panels, namely SME dominance, short time horizons, and crisis-period volatility. This article applies and evaluates the System GMM estimator as a methodological benchmark for analysing dynamic accounting performance in Southeast European (SEE) panels. Firm-level performance indicators, most notably profitability and productivity, alongside export outcomes, exhibit precisely the econometric characteristics the Blundell–Bond estimator is designed to address persistence, bidirectional causality, and unobserved heterogeneity. Treated as performance-measurement constructs for management control and decision support, these measures are analysed in a firm-level panel of 284 Greek companies (ICAP DataPrisma, 2005–2017). Hansen and AR(2) diagnostics are not rejected across all specifications, establishing a reproducible methodological benchmark for accounting research under SEE conditions. The study further documents the estimator’s alignment with the standards of leading accounting journals, positioning it as an appropriate benchmark for analysing the dynamic evolution of accounting variables under the conditions examined.