Econometrics and Regression as Standing Preserving Bookkeeping

This paper reclassifies econometrics and regression analysis as forms of standing-preserving bookkeeping rather than as discovery procedures that reveal underlying causal structure. It argues that the apparent power of statistical models arises from their role in organizing admissible observations under fixed reference and scope constraints, not from any ontic access to economic mechanisms. Within a closed admissibility framework, regression does not infer causes, generate laws, or justify intervention. It preserves reference across datasets by enforcing consistency conditions on representation—such as comparability, normalization, and error allocation—while remaining agnostic about what, if anything, the modeled relationships correspond to in reality. When these bookkeeping roles are mistaken for causal or explanatory authority, econometric results are illicitly over-interpreted. The analysis is eliminative rather than corrective. No new statistical methods are proposed, and no procedural guidance is given for model construction, validation, or use. Instead, the paper establishes a boundary condition on interpretation: econometric outputs are admissible only as scope-relative summaries of licensed data, not as evidence of causal structure or policy-relevant necessity. By situating regression within the same standing-preserving category as other non-ontic representational tools, the paper dissolves a class of foundational disputes in economics and social science. It closes, rather than advances, debates about what econometrics can “discover” by showing which claims were never admissible in the first place.