Financial Econometrics Beyond Estimation: Identification, Dependence, and Uncertainty
Abstract
Financial econometrics has expanded rapidly in recent decades, giving researchers many new tools for empirical analysis. Yet empirical findings often remain sensitive to modeling choices, sample construction, and specification decisions, suggesting that many disagreements arise not from estimation methods alone but from deeper limitations in what data can reveal. This survey organizes financial econometrics around three issues that determine the credibility of empirical inference: identification, dependence, and uncertainty. Identification concerns whether economic quantities such as causal effects, risk premia, and structural parameters can be credibly recovered from observable data. Dependence recognizes that assets, firms, and markets are interconnected, reducing the amount of independent information contained in financial data. Uncertainty captures not only sampling variation but also model misspecification, measurement error, and competing explanations. We review how these problems arise in asset pricing, corporate finance, ESG, and risk management and discuss implications for empirical design and inference.