Antes de aplicar o modelo de componentes não observados é necessário saber se a hipótese de raiz unitária I(1) é atendida pelas séries inseridas no modelo.
A tabela a seguir apresenta os teste de
library(nimcno) tsmz <- macro95[, c('ipca', 'selicr')] tab <- tab.stationary(tsmz) knitr::kable(tab)
tsplot(tsmz) tsplot(diff(tsmz))
tab <- tab.cointeg(tsmz) knitr::kable(tab)
bn.ipca <- ucmodel(x = macro95[,"ipca"], lag = 3, init = c(rep(c(1.5, 0.4, 0.2), 1),# ar pars c(-0.5), # var arima c(-1), # var level rep(0.01, 1) # cov arima level )) tab <- pars.ucmodel(bn.ipca)$tab knitr::kable(tab, caption = "BN univariado")
bn2 <- ucmodel(x = macro95[,c('ipca', 'selicr')], l=2, init=c(c(2, 0.2), c(2, 0.2), c(-0.5, -0.5), # var arima c(-1.2, -1.2), # var level 0.001, # cov level 0.01, # cov arima rep(0.001, 4) # cov level arima ), corre = TRUE) tab <- pars.ucmodel(bn2)$tab knitr::kable(tab, caption = "BN multivariado")
ucplot(bn.ipca, state = "level") ucplot(bn2, state = "level")
core.ipca1 <- bn.ipca$out$alphahat[,'level'] core.ipca2 <- bn2$out$alphahat[,'level.ipca'] core <- macro95[,c('ipca.ex2', 'ipca.ex', 'ipca.ma', 'ipca.mas', 'ipca.dp')] core <- cbind(core, core.ipca1, core.ipca2) x <- acum(core) y <- acum(macro95[,'ipca']) tsplot(x, y)
tab <- tab.stationary(core, d = FALSE) knitr::kable(tab)
tab <- tab.stationary(core - macro95[,"ipca"], d = FALSE) knitr::kable(tab)
tab <- tab.marques(y = macro95[,'ipca'], x = core) knitr::kable(tab)
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