A New Metric for Spuriousness Detection in Linear Models

This study proposes a rigorous diagnostic approach to identify and measure spuriousness for linear regression models. The study constructs a collection of new metrics—the True Effect Margin (TEM), Spurious Resistance Index (SRI), Normalized SRI (NSRI), and SRI Gain/Loss Ratio (SRIGLR)—to evaluate the stability as well as substantive importance of single regressors across different model specifications. The theoretical foundation of the given metrics lies in classical least squares estimation but broadens its interpretive scope by formally defining a model-consistent measure of explanatory resilience. The diagnostics mooted are analytically derived and validated through a sequence of simulation experiments designed to mimic common econometric problems, including omitted variable bias and multicollinearity. The study also demonstrates their empirical relevance in a well-balanced 28-country panel over 2000-2023, examining determinants of CO₂ emissions in the transport sector. The empirical results show that conventional statistical significance can overestimate the reliability of certain predictors, whereas SRI-based measures provide a stronger view for identifying variables with real structural influence. This new diagnostic measure is a contribution to the ongoing econometric debates regarding identification, model misspecification, and causal inference. To the extent that it measures the quantifiable stability of a regressor to structural shocks, this method supplies theoretical justification as well as empirical worth for applied researchers in carrying out model selection, robustness tests, and policy analysis.