The large dimension of variables as regressors requires a reduction in the number of variables, which we do in this paper through the factorial model. This method is useful if the variables are collinear, as is our case. We aim to synthesize information from Brazilian Institute of Economics (IBRE)’s public and private databases to evaluate if there is an additional gain from the private database to explain the Brazilian inflation index, the broad consumer price index (IPCA). We analyze the monthly period between 2000 and 2018. After we extract factor, we select which factors are relevant regressors to explain inflation with the Autometrics algorithm. Our result is that the factors extracted from the public database bring gains to explain inflation in relation to an autoregressive model. However, the use of factors tied to the private database still leads to explanatory gains for inflation. We have been able to explain about 75% of the inflation variations with the use of the factors extracted from the private and public databases.