values of the IPC ratings as predictors.<sup>5</sup> This allows us to generate predictions that can be updated monthly based on available data, independently of whether recent IPC ratings are available. We distinguish four key groups of covariates:

# **Structural factors**

Structural factors capture time-invariant vulnerability to food insecurity. We proxy these at the district level with spatial trends, population counts, land area, terrain ruggedness and land use shares (cropland and pasture). In addition, we include fixed effects to capture basic country-specific seasonal variation. Nonlinear prediction models like the Random Forest we deploy can exploit interactions between any (combination) of structural factors and other time-varying covariates to capture heterogeneity.

# **Environmental factors**

We rely on remote sensing data to track environmental factors relating to food production. Food production depends on soil moisture content, which can be monitored through the inputs (rainfall) and outputs (evapo-transpiration) of the water balance equation. We also proxy for food plant health directly using the normalized difference vegetation index (NDVI), which is a commonly-used satellite-imagery-based measure of vegetation coverage (Ross et al., 2009; Brown, 2010).

# **Violent**

Violent conflict is an important factor that can impact food insecurity, by disrupting social systems and blocking access, or may signal other factors that contribute to food insecurity. For example, riots and protests may be the sign of an ailing economy, while violent attacks on civilians by non-state actors can cause displacement of people and disrupt food production and distribution (Br ̈uck and D’Errico, 2019; Maxwell et al., 2020). We use the count of conflict events and the number of fatalities in order to capture events of differing intensity, ranging from protests and riots to lethal attacks.

# **Food price inflation**

Food price inflation can directly cause food insecurity by raising the cost of living for vulnerable households (Baffes et al., 2008; Concei ̧c ̃ao and Mendoza, 2009). Rising prices can also signal whether fiscal space is adequate or tightened, for example due to episodes of conflict (Adam et al., 2008). Over longer time horizons, price uncertainty may lead businesses to postpone investments, see (Koomen et al., 2015; Andr ́ee et al., 2017) for the specific case of agricultural investments. We use monthly sub-national food price inflation estimates developed by Andr ́ee (2020a) to capture these important dynamics.<sup>6</sup>

> 5The impact on the forecasting performance is insignificant but the choice results in larger models. Note that, under suitable conditions, a finite-order moving average approximation of a lower-order autoregressive process remains reasonably accurate. Since innovations can be substituted by exogenous variables by defining a process for lower-level constituents, stable autoregressive processes may also be approximated using a sufficient number of lags of relevant exogenous variables.

> 6The paper assembles sporadically available food price data collected by the World Food Programme, and uses a stochastic imputation that leverages unobserved cross-correlations between intermittent market-level price time series of individual food items to impute price data for up to six staple foods for all markets and months in a given country. These are aggregated into a simple equally-weighted geometric price index and interpolated geographically using inverse distance weighting.

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