Forecasting the National Passing Rate of the Certified Public Accountant Licensure Examination (CPALE)

Accounting

In this research, Autoregressive Integrated Moving Average (ARIMA) was used in forecasting the national passing rate of the Certified Public Accountant Licensure Examination (CPALE) for 2024 to 2027 and selecting the best fitted model are the main objectives of this study. The data were gathered from the Professional Regulation Commission (PRC) which employs semi-annual data passing rates from 2010 through 2023. This work shows how the historical passing rate data can be used to forecast future rates and how these forecasts may be used in the Accounting Industry. Using the Box-Jenkins time series process, historical demand data was utilized to create various ARIMA models, and the best models were chosen based on four performance criteria: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC)/ Schwartz Information Criterion (SIC), Hannan-Quinn Information Criterion (HQC) and Coefficient of Determination (R squared). Based on the criteria the best ARIMA models were ARIMA (2,1,2), (2,1,1) for May and October first-timer examinees and ARIMA (2,1,3), (1,2,1) for May and October repeater examinees. The results obtained prove that the models could be utilized to forecast the passing rate. The results will assist reviewers/ instructors and PRC in forecasting or setting the passing rate. The researcher recommends that the Commission on Higher Education (CHED), the Board of Accountancy (BOA), the Philippine Institute of Certified Public Accountants (PICPA), and the Professional Regulation Commission (PRC) should look into the matter, revisit and improve the course/review program, teaching techniques, and board examination questions.

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