Bayesian Analysis of Dynamic Factor Models: An Application to Air Pollution and Mortality in São Paulo, Brazil Articles uri icon

publication date

  • September 2008

start page

  • 582

end page

  • 601

issue

  • 6

volume

  • 19

international standard serial number (ISSN)

  • 1180-4009

electronic international standard serial number (EISSN)

  • 1099-095X

abstract

  • 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.