We explore the impact of the COVID-19 shock on a very high frequency indicator: the Spanish daily sales data compiled by the Tax Agency. Firstly, we present a detailed list of the issues related to its modeling, its decomposition (trend, seasonality, and irregularity) and its final seasonal adjustment, which requires a set of deterministic factors linked mostly to calendar effects. Then, we assess the impact of the COVID-19 shock on these tasks. This assessment provides a timely perspective on the evolution of the shock and the challenges that it posed for modeling and seasonal adjustment, clearly related to its unusual and extreme features. The aim of the paper is eminently empirical and dominated by the need to square the unprecedented shock with the available methods and software in a computing environment centered on the R programming language. The methodology draws heavily on a structural approach and it is currently in use by the Tax Agency to compile and disseminate its daily sales data.
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