RStudio:

Long Run vs Short Run Decompositions in R: The HP filter vs the Hamilton filter

• https://rpubs.com/quarcs-lab/long-run-filters

Online Environments

• R Studio cloud project: https://rstudio.cloud/project/25043

• This notebook can also be executed online at GESIS Notebooks. Just copy the URL of this repository and paste it on the BINDER form To open a virtual R Studio session, make sure you change you click on File and change it to URL. Then, write rstudio in the field URL to open (optional). Finally, click on launch.

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Suggested Citation:

Mendez C. (2020). Long Run vs Short Run Decompositions in R: The HP filter vs the Hamilton filter. R Studio/RPubs. Available at https://rpubs.com/quarcs-lab/long-run-filters . DOI: https://zenodo.org/badge/latestdoi/239955444

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Tutorial

Set parameters of the program

• Name of the series

seriesName <- "RGDPNAIDA666NRUG"

Code examples for other series

• Total GDP of Japan: “JPNRGDPEXP”
• GDP per capita of Japan: “RGDPCHJPA625NUPN”
• GPD per capita of Bolivia: “NYGDPPCAPKDBOL”
• Total GDP of Bolivia: “RGDPNABOA666NRUG”
• Total GDP of Indonesia: “RGDPNAIDA666NRUG”

Load libraries

library(mFilter)
library(quantmod)
library(dplyr)
library(ggplot2)
library(dygraphs)
library(xts)
library(neverhpfilter)

# Change the presentation of decimal numbers to 3 and avoid scientific notation
options(digits=3, scipen=999)

Import data

seriesName <- getSymbols(seriesName, src="FRED", auto.assign = FALSE)

periodicity(seriesName)

## Yearly periodicity from 1960-01-01 to 2019-01-01

Transform the data

• Take the log of the series

seriesName <- log(seriesName)

Plot evolution of the variable

dygraph(seriesName) %>%
  dyRangeSelector()

Apply the HP filter

seriesName_filtered_HP <- hpfilter(seriesName,
                                  freq = 6.25      
                                     )

Plot the HP filter

Long-run trend

Create matrix of actual, trend , and cycle values

actual  <- seriesName_filtered_HP[["x"]]
trendHP <- seriesName_filtered_HP[["trend"]]
cycleHP <- actual - trendHP

colnames(actual)   <- c("actual")
colnames(trendHP)  <- c("trendHP")
colnames(cycleHP)  <- c("cycleHP")

actual_and_trend <- cbind(actual, trendHP)

dygraph(actual_and_trend[,1:2]) %>%
  dyRangeSelector()

Short-run fluctuations

dygraph(cycleHP)  %>%
  dyRangeSelector()

Apply the Hamilton filter

seriesName_filtered_Hamilton <- yth_filter(seriesName,
                                           h = 2,
                                           p = 4,
                                           output = c("x", "trend", "cycle"))

Plot the Hamiltion filter

Long-run trend

Rename columns

colnames(seriesName_filtered_Hamilton)  <- c("actual",
                                             "trendHamilton",
                                             "cycleHamilton")

dygraph(seriesName_filtered_Hamilton[,1:2])  %>%
  dyRangeSelector()

Short-run fluctuation

dygraph(seriesName_filtered_Hamilton[,3])  %>%
  dyRangeSelector()

Run it in the cloud

Tip: Copy and paste this link another tab of your browser.

https://rstudio.cloud/project/25043

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