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Antithetic time series analysis and the CompanyX data
Author(s) -
Ridley Dennis,
Ngnepieba Pierre
Publication year - 2014
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/j.1467-985x.2012.12001.x
Subject(s) - series (stratigraphy) , counterintuitive , variance (accounting) , time series , statistics , mathematics , forecast error , econometrics , convergence (economics) , constant (computer programming) , reliability (semiconductor) , computer science , economics , paleontology , philosophy , power (physics) , physics , accounting , epistemology , quantum mechanics , biology , programming language , economic growth
Summary. Antithetic time series analysis is the solution to a most perplexing problem in mathematical statistics. When a mathematical model is fitted to serially correlated data, the parameters of the model are unavoidably biased. All forecasts that are obtained from the model are unavoidably biased and therefore diverge. The forecast reliability worsens with the forecast horizon. It is shown that the forecast bias can be dynamically reduced. This is made possible by the entirely counterintuitive discovery of antithetic time series theory that permits unbiased forecast error convergence to a constant, independent of forecast origin. The forecast error variance in each time period is the same.