# Note to compile this file to README.mb, run the following: # rmarkdown::render('README.Rmd',output_format = 'md_document') knitr::opts_chunk$set( echo = FALSE, warning = FALSE, message = FALSE, error = FALSE ) #knitr::opts_knit$set(root.dir = '../')
This package provides functions used in the Project
The package can then be installed using devtools::install_github('santoscs/mecnost')
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Os dados são PIB trimestral dos setores extraídos das contas nacionais do IBGE no período de 1996Q1 a 2016Q3.
As séries temporais são mostradas na figuras seguir:
library(mecnost) g1 <- tsplot(log(pibsetores[,1:7])) g2 <- tsplot(log(pibsetores[,8:14])) multiplot(g1, g2, cols = 2)
Estima os modelos por MCMC
# estima os modelos (lento) # lpib <- as.list(log(pibsetores)) # models <- purrr::map(lpib, btcmodel, # a.theta = 1, b.theta = 1000, n.sample = 2050, # thin = 1, save.states = TRUE) # alarm() # # save(models, file = "data-raw/estimacoes.RData") load(file = "data-raw/estimacoes.RData") library(ggplot2) y <- as.list(names(models)) plots <- purrr::map2(models, y, autoplot, discarding=0.05) tabs <- purrr::map(models, tab.btcmodel, discarding=0.1)
multiplot(plotlist = plots[1:4], cols = 2) multiplot(plotlist = plots[5:8], cols = 2) multiplot(plotlist = plots[9:12], cols = 2) multiplot(plotlist = plots[13:14], cols = 2)
tab <- purrr::invoke_map("cbind", list(tabs)) tab <- t(tab[[1]]) tab <- cbind(rownames(tab), tab) rownames(tab) <- as.vector(rbind(names(models), " r ")) knitr::kable(tab)
# componentes estimados library(purrr) ciclos <- models %>% map(stimated.btcmodel) %>% map_df(function(x) x[,"Ciclo (gap)"]) ciclos <- as.ts(ciclos) tsp(ciclos) <- tsp(pibsetores) # calcula coerencia e fase library(ggplot2) library(astsa) sr=spec.pgram(ciclos[,1:7], kernel("daniell", 6),taper=0,plot=FALSE) autoplot(sr, plot.type = "coh") autoplot(sr, plot.type = "phase") sr=spec.pgram(ciclos[,8:14], kernel("daniell", 6),taper=0,plot=FALSE) autoplot(sr, plot.type = "coh") autoplot(sr, plot.type = "phase") dados1 <- models %>% map(stimated.btcmodel) %>% map_df(function(x) x[,"Tendencia"]) dados2 <- models %>% map(stimated.btcmodel) %>% map_df(function(x) x[,"Ciclo (gap)"]) dados3 <- models %>% map(stimated.btcmodel) %>% map_df(function(x) x[,"Tx. de Cresc."]) dados <- cbind(tren=dados1, cycle=dados2, grow=dados3) write.csv2(dados, file = "data-raw/resultados-estimacoes.csv")
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