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The Bayesian estimation of a dynamic factor model where the factors follow a multivariate autoregressive model is presented. We derive the posterior distributions for the parameters and the factors and use Monte Carlo methods to compute them. The model is applied to study the association between air pollution and mortality in the city of São Paulo, Brazil. Statistical analysis was performed through a Bayesian analysis of a dynamic factor model. The series considered were minimal temperature, relative humidity, air pollutant of PM10 and CO, mortality circulatory disease and mortality respiratory disease. We found a strong association between air pollutant (PM10), Humidity and mortality respiratory disease for the city of São Paulo.