The production of timely and accurate short-term statistics is a key function of many National Statistical Offices. These indicators are used by policy makers, professional organizations, and financial markets to monitor an economy in real time. Consequently, the collection cycle is usually monthly or quarterly, leaving little time for nonresponse follow-up as the measures being output soon after collection. In general, short-term statistics comprise time series, and missing data treatments often rely on assumptions of autoregression and seasonality. Beginning in March 2020, many short-term statistics programs had unprecedented levels of nonresponse, with apparent breaks in long-time stationary series. In other words, the formerly reliable missing data treatment procedures were no longer viable. In this session, we will discuss general issues of missing data treatment during an economic upheaval, then focus on specific issues such as imputation of zero values versus positive values. Other discussion points will include imputation methods used in practice, both successfully and unsuccessfully.