Broken-Trends in City Prices: Detecting Breaks and Forecasting Aggregated Inflation

Music City Center 

The importance of forecasting inflation as well as possible cannot be overstated, especially in a country that follows the inflation targeting strategy. With that in mind, this work takes the idea of aggregate regional price indices in 23 Colombian cities to forecast national inflation. This paper employs individual price index time series models to identify price level changes based on city series. Using trend models that incorporate these breaks, we forecast monthly and annual total inflation. Our results show that including trend breaks and disaggregated information improves the accuracy of annual inflation prediction across many time horizons and competes with the item-by-item aggregated forecast exercises. We obtained gains in RMSFE of around 13% and 45%, for one and two months ahead, relative to an aggregated ARIMA model. The forecasts for the end of 2025 are close to 4.5%.

Consumer Price Indexes

Linear Trend Models

Structural Breaks

Forecasting

Regional Forecast Aggregation 

International Statistical Institute