_ANDR EE_<sup>_ ́_</sup> _AND PAPE_ _SEPTEMBER 5, 2023_ _20_ The graph in figure 2 shows the processed exchange rate data constructed from FAO FMPA parallel market quotes and daily rates from the ECB. The result can be used to dollarize the food price estimates. It is important to note that in many countries with strongly depreciating currencies, the exchange rate may correlate very strongly with food prices. In PNG, the data largely follows a different pattern. From the graph we can infer that the dollar appreciated roughly 40% against the Kina between 2009 and 2022 while food prices more than doubled. Over the long run, food has thus increased not only in local currency but also in dollar terms.

When price monitoring focuses on a single food item, however, it becomes more important to ensure that the individual price series have some degree of data support throughout the entire monitoring period. For instance, the results in figure 3 show an example of cabbage prices in Mt. Hagen, the third largest city and capital of the Western Highlands Province that is located in the large fertile Wahgi Valley in central mainland. The result highlights that in the PNG survey data, there are considerable data gaps during which the trust score dips severely. The chart also shows that there was a major price shock that resulted in triple digit product-specific inflation rates in the area. Such large price shocks are less likely to occur in countries where markets are more integrated, and highlight the importance of local price monitoring in places such as PNG where they are not.

# **IV. Discussion and conclusion**

While many countries globally are currently experiencing some of the highest inflation rates in decades, the Pacific Islands lack good capabilities to track food price developments in near-real-time. However, without such capabilities, it is difficult to monitor the situation and inform policies in a timely manner, which is particularly crucial in such a volatile environment.

To gain near-real-time insights, this paper applied a recently developed machinelearning approach to impute local market prices for fresh produce in local markets in PNG. The aim is to investigate the feasibility of monitoring food price inflation continuously relying only on incomplete and intermittent local market-level survey data that can be gathered in a rapid fashion. The application to fresh produce prices in PNG is made particularly challenging by high intra-month price volatility, low cross-market price correlations, and weakly defined food-specific price trends.

The paper runs cross-validations for imputation strategies under different designs in terms of numbers of markets, food items and time periods covered. The results show that imputations can achieve accuracy that is attractive when compared to costly direct measurement of prices. For instance, the imputations processed 27 food items across 8 markets and explains approximately 71% (20092022) to 77% of (2017-2022) of variation in out-of-sample data. When natural price volatility is taken into account, it is likely that this approach only sacrifices a little over 10% of accuracy when compared to direct measurement. Consider-