_ANDR EE_<sup>_ ́_</sup> _AND PAPE_ _SEPTEMBER 5, 2023_ _2_

# **I. Introduction**

Statistical agencies around the world are increasingly interested in the use of machine learning in the production of official statistics. In particular, the area of missing data imputation is one of potentially promising applications. A recent survey on the use of machine learning methods in official statistics commissioned by the United Nations Economic Commission and conducted at selected national and international statistical institutions revealed that missing data imputation was second in a ranking of promising areas (Beck et al., 2022). Real-time imputation of economic data may also hold the key to enabling reliable forecasting and monitoring of risks in humanitarian settings where primary data often cannot be collected (Andr ́ee et al., 2020; Wang et al., 2020, 2022), or in development contexts where large scale data operations are typically carried out on an infrequent basis (Mahler et al., 2021).

Traditionally, surveys have been deployed as self-contained data gathering operations aimed at capturing a snapshot of an evolving population statistic such as the poverty rate, market sentiment, or the consumer price index. Developing such one-time analyses has been the bread and butter task of economists for decades, and the go-to approach for policy makers to inform their next actions. The issue of missing data has traditionally been approached from the angle of correcting for the bias and uncertainty that arise in this analytical context. In particular, the work of Rubin (1976); Campion and Rubin (1989); Rubin (1996); Little and Rubin (2012); van Buuren (2012) on multiple imputation has provided important answers to the question of how to deal with non-response when estimating economic relationships.

Increasingly, however, economists and policy makers are looking for continuous insight, as shown by the surge in literature on “now-casting” and real-time indicators (Khan et al., 2022). The literature has put forward many promising applications, but now-casting composite variables is specifically hard as it involves tracking the evolution of multiple contributing factors in a structured manner.

As an example, economists looking at inflation generally track a price index, comprising the combined prices of a consistent basket of important goods. Nonresponses in the price data gathered for the entire basket are highly problematic. Inflation calculation requires all prices in the basket to be observed without bias. Thus, an accurate assumption for the value of the non-response is necessary. For this reason, price surveys traditionally follow a deliberate sampling and measurement process (Reinsdorf et al., 2009) that minimizes measurement errors, missing price quotes, or biases that stem from the locations or methods of measurement, as all are sources of error (Baker, 1996; Lebow and Rudd, 2003; Greenlees and McClelland, 2010). The deliberate approach makes traditional price data gathering methods robust, but highly inflexible, and typically not suitable for tracking inflation in near-real-time or high-frequency settings, except in few countries with exceptional statistical capacity.

Short-burst rapid surveys are increasingly relied on to complement traditional