We investigate the impacts of sampling frequency and model specification uncertainty on the outcome of unit root tests, commonly employed as market efficiency tests, using a new, robust Bayesian test on seven commodity futures prices at three different sample frequencies (daily, weekly, and monthly). Using Bayesian model averaging to account for different possible mean and error variance specifications, we show that sample frequency does affect the unit root test results: the higher the frequency, the higher the support for stationarity. We further show that not accounting for model specification uncertainty can produce unit root test results that are not robust.

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