README.md

In nelson16silva/wavcoreinf: Estimation and Evaluation of Wavelet Core Inflation Measures

wavcoreinf

wavcoreinf provides a flexible estimation and evaluation of core inflation from wavelet-based signal estimation methods. Functionals are developed to facilitate the estimation of several specifications permitting the selection of the best one according to specific criteria established in the core inflation literature.

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("nelson16silva/wavcoreinf", build_vignettes = TRUE)

Example

This is a basic example which shows you how to obtain a good core inflation measure from wavelet. The wavcoreinf’s vignette provides more information:

vignette("wavcoreinf")
browseVignettes("wavcoreinf")

library(wavcoreinf)
library(dplyr)
library(tidyr)
library(ggplot2)
library(tibble)
library(purrr)
library(rlang)
library(lubridate)
library(forecast)
library(ellipsis)

Brazilian Headline Inflation and Core Inflation

n <- c(433, 11427, 16121, 27838, 27839, 11426, 4466, 16122)
x <- c("ipca", "ipcams", "ipcama", "ipcaex0", "ipcaex1", "ipcadp", "ipcaex2", "ipcaex3")
# inf <- getsgs(x, n)

Creating individual time series from 2006-07-01 to 2019-08-01

coreinf_br2 <- filter(coreinf_br, date >= "2006-07-01")
date <- coreinf_br2[["date"]]
ts_start <- c(year(date[1]), month(date[1]))
for(i in seq_along(x)) {
  assign(x[[i]], ts(coreinf_br2[[i + 1]], start = ts_start, freq = 12))
}

Wavelet Core Inflation

pq <- lags(2, 1)
ebthr_xform <- list(wt = c("dwt", "modwt"))

ebthr_wt <- list(
  wf = c("haar", "la8"),
  n.levels = 3:4
)

ebthr_ebwav <- list(
  vscale = c("level", "independet"),
  a = 3.83,
  prior = c("laplace", "cauchy")
)

ebthr_args <- wav_args_ebthr(ebthr_xform, ebthr_wt, ebthr_ebwav)
ebthr_smooth <- wav_smooth(ipca, ebthr_args)

ebthr_table <- wcore_table(ebthr_smooth, ipca)
ebthr_error_mean <- error_wave_summary(3, x = ebthr_args, y = ipca, lags = pq, k = 15, RMSE = TRUE)

ebthr_summary <- wcore_summary_fcast(ipca, ebthr_table, ebthr_error_mean)
ebthr_best <-  ebthr_summary %>% 
  wcore_summary_best(d = c(1, 1, 1, 1, 2))

ebthr_best_median <- wcore_best_median(ebthr_best)

bayes2_ts <- ts(ebthr_best_median, start = ts_start, freq = 12)

Plot

inf <- tibble(
  date = time(ipca), ipca,
  `IPCA-MS` = ipcams,
  `IPCA-EX0` = ipcaex0,
  `IPCA-EX1` = ipcaex1,
  `IPCA-DP` = ipcadp,
  `IPCA-EX2` = ipcaex2,
  `IPCA-EX3` = ipcaex3,
  `IPCA-BAYES2` = bayes2_ts[, 2]
)

inf %>%
  gather(-1:-2, key = "measure", value = "core") %>%
  ggplot(aes(x = date)) +
  geom_line(aes(y = core, linetype = "Core")) +
  facet_wrap(~measure, nrow = 4) +
  geom_line(aes(y = ipca, linetype = "Headline")) +
  labs(x = "", y = "%a.m", linetype = "") +
  theme(legend.position = c(.8, .1)) + # legend.position = "bottom") +
  scale_linetype_manual(values = c(Core = "solid", Headline = "dashed"))   

Headline and Core Inflation Measures

nelson16silva/wavcoreinf documentation built on Feb. 17, 2025, 7:10 p.m.