_ANDR EE_<sup>_ ́_</sup> _DECEMBER - 2021_ _20_ within very short time periods in FCS countries. Figure A1 presents estimated monthly year-on-year inflation on a line chart for the Republic of Yemen together with intra-month price ranges on a candle chart. The intra-month volatility algorithm is detailed in section B.B4 and uses a conditional autoregressive heteroskedasticity model to estimate the time-varying properties of the monthly price variance process. The estimates are used to calculate Expected Shortfall in the price returns, which is used to construct the wicks and bodies of the candles. The open values are defined as the estimated conditional expectation of monthly prices, the highs are the average intra-month price levels in the worst 50% of estimated conditional price increase events, the lows are equal to the average intra-month price levels in the worst 50% of estimated conditional price decrease events. The colored parts of the candles are the estimated price ranges within which the majority of monthly prices are estimated to be, with red candles indicating months in which the end-of-month prices closed below the start-of-month price estimates. Strong inflationary events are thus visualized as consecutive green candles. Periods of high volatility are visualized by candles with large wicks. Results for all other countries are available in figures A2 to A4. The charts in figures A2 to A4 highlight that there have been several notable high inflation events. The World Food Price Crisis of 2007-08 is visible as a price up-tick in most countries, most notably in Afghanistan, Burkina Faso, Chad, Haiti, Niger and Somalia. In some of the charts, the price action seems modest as it is suppressed by recent price action. In these instances, it may be more useful to look at prices on a logarithmic chart. Prices have also visibly surged in many countries in the year following the pandemic.

# **IV. Discussion and concluding remarks**

Food price inflation is an important metric to inform economic policy and is closely watched by both economists and humanitarians, yet official figures are often missing, lacking in spatial detail, or published with delay. More important, crisis situations, or vulnerable populations in general, are often characterized by high geographic specificity while traditional price indicators do not provide insights beyond the major (urban) markets where prices are formally measured. Traditional CPI data in these situations, if available, may be insufficiently unbundled into distinct price estimates for it to be put into a relevant context. Recognizing this, international institutions have invested substantially in subnational price surveys. However, the capacity to monitor inflation has continued to be severely limited by challenges related to missing data. To overcome some of these shortcomings, this paper proposed an approach for real-time imputation of survey data drawing on multiple imputation and ensemble learning ideas.

The paper highlighted the new price monitoring capabilities using survey data gathered in 25 fragile and conflict-affected countries. The final estimates of food price inflation documented in this paper were shown to accurately capture important inflation events including the World Food Price Crisis of 2007-08 and the