Forecasting Brazilian Inflation by Its Aggregate and Disaggregated Data: A Test of Predictive Power by Forecast Horizon
28 Pages Posted: 10 Feb 2013 Last revised: 25 Feb 2016
Date Written: February 24, 2016
This work aims to compare the forecast efficiency of different types of methodologies applied to Brazilian consumer inflation (IPCA). We will compare forecasting models using disaggregated and aggregated data from IPCA over twelve months ahead. We used IPCA in a monthly basis, over the period between January 1996 to March 2012. Out-of-sample analysis will be made through the period of January 2008 to March 2012. The disaggregated models were estimated by SARIMA and will have different levels of disaggregation from IPCA as groups and items, as well as disaggregation with more economic sense used by Brazilian Central Bank as: I) services, monitored prices, food and industrials and II) durables, non-durables, semi durables, services and monitored prices. Aggregated models will be estimated by time series techniques as SARIMA, state-space structural models and Markov-switching. The forecasting accuracy among models will be made by the selection model procedure known as Model Confidence Set (Hansen et al. (2011)). We were able to find evidence of forecast accuracy gains in models using more disaggregated rather than aggregate data.
Keywords: Inflation, forecasting, ARIMA, space-state model, Markov-switching, Model Confidence Set
JEL Classification: C53, E31, C52
Suggested Citation: Suggested Citation