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Puerto Rico (PR) is among the US jurisdictions with the lowest passing rates on the Certified Public Accountant (CPA) exam (National Association of State Boards of Accountancy (NASBA), 2016, 2017). No prior studies have addressed this issue. To fill this gap, the authors conducted a survey among candidates to examine the factors perceived as challenges on the exam. The authors also studied the relationship between respondents’ individual, educational, and CPA exam-related characteristics and the likelihood of success on the exam. Results reveal there are three factors perceived by respondents as their main challenges: level of technical difficulty, cost of the exam, and not enough time to study for the exam. Additional analysis indicates that age, major Grade Point Average, weekly study hours, and the time interval between graduation and the first attempt at the exam are significant factors that affect the passing rates on the exam. These findings suggest that a combination of individual, educational, and exam-related factors affect Puerto Rican candidates’ results on the exam. The socioeconomic background of PR may heighten the impact of such factors. Considering that the accounting profession is always adapting to the evolutionary demands of the business environment, these results may contribute to the development of adequate measures in support of Puerto Rican and other minorities with diverse backgrounds.

This study examines the tracking error (TE) of a sample of sector exchange traded funds (ETFs) using spectral techniques.

TE is examined by computing its power spectrum using the wavelet transform. The wavelet transform maps the TE time series from the time domain to the time–frequency domain. Albeit the wavelet transform is a more complicated mathematical tool compared with the Fourier transform, it also has important advantages such as that it allows to analyze non-stationary data and to detect transient behavior.

Results show that changes in the TE of a sample of sector ETFs are captured by the wavelet transform. Moreover, the authors also find that the wavelet coherence function can be used as a measure of TE in the time–frequency domain.

The study shows that the wavelet coherence function can be used as a reliable measure of TE.