This study examines whether and how important it is to adjust output gap frameworks during the covid-19 pandemic and similar unprecedentedly large-scale episodes. our proposed modelling framework comprises a bayesian structural vector autoregressions with an identification setup based on a permanent-transitory decomposition that exploits the long-run relationship of consumption with output and whose residuals are scaled up around the covid-19 period. our results indicate that (i) a single structural error is usually sufficient to explain the permanent component of the gross domestic product (gdp); (ii) the adjusted method allows for the incorporation of the covid-19 period without assuming sudden changes in the modelling setup after the pandemic; and (iii) the proposed adjustment generates approximation improvements relative to standard filters or similar models with no adjustments or alternative ones, but where the specific rare observations are not known. importantly, abstracting from any adjustment may lead to over or underestimating the gap, to too-quick gap recoveries after downturns, or too-large volatility around the median potential output estimations. **** resumen: esta investigación examina si y cómo es importante ajustar la estimación de la brecha de producto (pib) durante la pandemia de covid-19. para ello, proponemos dentro de un enfoque bayesiano un modelo de vectores autoregresivos estructurales (bsvar) con un esquema de identificación basado en la descomposición de choques permanentes y transitorios que explota la relación de largo plazo entre el consumo y el pib, y cuyos residuales se escalan alrededor del periodo de covid-19. nuestros resultados indican que (i) con un sólo choque estructural es suficiente para explicar la componente permanente del pib; (ii) el método ajustado permite la incorporación del período de covid-19 sin asumir cambios bruscos en la configuración de modelización después de la pandemia; y (iii) el ajuste propuesto genera mejo
