Seasonality in Covid-19 Times

Abstract

COVID-19 has hit the economy in an unprecedented way, abruptly changing the data generating process of many economic time series. This has triggered one of the highest policy interventions that we have ever seen. Policy assessment relies on real time monitoring of the economy using seasonally adjusted time series. This poses a new problem for analysts: to deseasonalize time series under the effects of strongly changing trends and seasonal patterns in real time. We compare different seasonal adjustment strategies based on parametric as well as non-parametric procedures. Firstly, using basic structural time series models we simulate data where we know their generating processes for the trend, cycle, seasonal and irregular components. In this way, we are able to have the counterfactual time series or what would have happened had COVID-19 not occurred. Afterwards, we contaminate the last 12 observations in different ways to reproduce the heterogeneity in the behaviour of time series during COVID-19 and check the performance of several alternatives for deseasonalizing.