Authors : Bhopinder Kambo; Gurinder Singh; Cinde Khagan Rao

Volume/Issue : Volume 11 - 2026, Issue 9 - September

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Google Scholar : https://tinyurl.com/45zkcy7u

DOI : https://doi.org/10.38124/ijisrt/26sep083

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : This study evaluates econometric and machine learning frameworks for forecasting the Canadian Consumer Price Index (CPI) from 1995 to 2025. Comparative analysis establishes that a linear SARIMAX (1,1,1) (1,1,1)12 model systematically outperforms complex deep learning architectures, including LSTM, GRU, and automated ensembles. We resolve the empirical 'Price Puzzle' using a 2SLS instrumental variables framework.

Keywords : Consumer Price Index (CPI), SARIMAX, Machine Learning, 2SLS, Price Puzzle, Taylor Principle, Bank of Canada.

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