The Pretence of Amnesia: Autocorrelation, Systemic Memory, and the Limits of Temporal Isolation

Singh, Rudra Pratap (2026): The Pretence of Amnesia: Autocorrelation, Systemic Memory, and the Limits of Temporal Isolation.

Abstract

Standard econometric pedagogy and practice rest upon a foundational, yet highly fragile, epistemological assumption: that unobservable error terms are independent across time and space, expressed mathematically as �(u_iu_j) =0. By requiring the residuals of a model to exhibit zero covariance, this framework treats the continuous, dynamic flow of human economies as a sequence of isolated, memoryless events. However, because economic variables operate within deeply entangled, complex systems, omitted factors—such as institutional trust, cultural risk appetite, or market sentiment—naturally possess historical inertia. While the discipline frequently acknowledges the presence of systemic memory, its traditional remedies, such as artificially inflated standard errors (HAC) and autoregressive parameters (ARIMA), function primarily as mechanical patches that obscure deeper structural misspecifications. Furthermore, even highly sophisticated modern techniques, including Machine Learning causal forests and Synthetic Controls, fail to resolve the core epistemological crisis, as they remain bound by assumptions of total observability or historical stasis. We argue that true statistical rigor in complex, non-ergodic environments requires a paradigm shift. The discipline must pivot away from the hubristic pursuit of precise causal extraction and instead embrace quantitative methods designed to mathematically bound systemic exposure to path-dependent uncertainty

• Item Type: MPRA Paper
• Language: English
• Keywords: Autocorrelation, Systemic Memory, Non-Ergodicity, Epistemology, Complex Systems, Via Negativa
• Subjects: B - History of Economic Thought, Methodology, and Heterodox Approaches > B4 - Economic Methodology > B41 - Economic Methodology; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C50 - General

References

Abadie, Alberto, Alexis Diamond, and Jens Hainmueller. 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program." Journal of the American Statistical Association 105 (490): 493–505. Box, George E. P., and Gwilym M. Jenkins. 1970. Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day. Hayek, F. A. 1974. "The Pretence of Knowledge." Nobel Memorial Lecture, December 11. Newey, Whitney K., and Kenneth D. West. 1987. "A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix." Econometrica 55 (3): 703–708. Taleb, Nassim Nicholas. 2020. Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications. STEM Academic Press. Wager, Stefan, and Susan Athey. 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests." Journal of the American Statistical Association 113 (523): 1228–1242.