In Lingbing/epuR: Collect and Process Economic Policy Uncertainty Data

Economic Policy Uncertainty data

get_epu() function collects the EPU data and outputs an xts object. The required argument is region which is a character indicating the region of EPU data you need. By default, it collects all regions.

The data collection is in real time and normally takes about several seconds to download.

library(epuR)
library(dygraphs)
all_data <- get_EPU()
dygraph(all_data)

As it is a xts object, so dygraph can be directly applied for interactive plots.

dygraph(all_data)

If you need EPU for a specific region, use region = argument:

china_epu <- get_EPU(region = "China")
dygraph(china_epu)

Another way to get country level data is to directly manipulate the all_data object:

china_data <- all_data$China
dygraph(china_data)

The difference is that using region will take care of the NA values so there is no large white area in the plot. However, it is not a problem in xts, just delete the NAs.

dygraph(na.omit(china_data))

Trade Policy Uncertainty (TPU) data

The US TPU data is one of the category-specific EPU indices developed in the original paper by Baker et al. (2016). "It reflects the frequency of articles in American newspapers that discuss policy-related economic uncertainty and also contain one or more references to trade policy". To get the US TPU data:

us_tpu <- get_TPU(region = "US")
dygraph(us_tpu)

China TPU index is constructed by Steven J. Davis, Dingqian Liu and Xuguang S. Sheng using the Renmin Daily and the Guangming Daily.

To get the China TPU:

china_tpu <- get_TPU("China")
dygraph(china_tpu)

Japan TPU is also available, to get Japan TPU:

jap_tpu <- get_TPU("Japan")
dygraph(jap_tpu)

The japan TPU data also contains EPU, FPU, MPUI, and ERPU. If you need TPU, simply:

dygraph(jap_tpu$TPU)

US Equity Market Volatility Index

The default get_emv collects all categories constituting the index and the output can be quite messy. If only the overall EMV is in need, set all = FALSE. Also, it is suggested to keep the default all = TRUE and manipulate the xts output as needed.

emv_data <- get_EMV(all = F)
dygraph(emv_data)

World Unvcertainty Index (WUI) data

The official WUI data set excel file contains several sheets, and the name of which becomes the argument option of the get_wui() function. They are:

• F1: overall uncertainty across the globe.
• F2: trade uncertainty across the globe.
• T1: World Uncertainty Index (WUI) at the global level (simple average and GDP weighted average), income level (advanced, emerging, and low-income economies), and regional level (Africa, Asia and the Pacific, Europe, Middle East and Central Asia, and Western Hemisphere).
• T2: World Uncertainty Index (WUI) for 143 countries.
• T3: total count of the word "uncertainty" (or its variant) in the EIU country reports for 143 countries and from the 1950s.
• T4: count of the total number of words in the EIU reports for 143 countries and from the 1950s.
• T5: count of the total number of pages in the EIU countries reports for 143 countries from the 1950s.
• T6：three-quarter weighted moving average of the World Uncertainty Index (WUI) for 143 countries from the 1950s.
• T8: aggregate World Trade Uncertainty (WTU) index as well as the index by country.

Therefore, to get the overall WUI:

wui_overall <- get_WUI("F1")
dygraph(wui_overall)

To get the World Trade Uncertainty Index, use "T8". The output xts data object contains data for 143 countries, so it might be better to plot only the data you need.

wtui_data <- get_WUI("T8")
dygraph(wtui_data$CHN)

Financial Stree Indicator (FSI) data

Data is available at monthly and quarterly frequencies. get_fsi() outputs monthly data by default.

fsi_mon <- get_FSI()
dygraph(fsi_mon)
fsi_quar <- get_FSI(freq = "quarterly")
dygraph(fsi_quar)

Immigration Related Index (IRI) data

usa_iri <- get_IRI("USA")
dygraph(usa_iri)

Geopolitical Risk Index (GPR) data

There are 4 types: 1. type= 1 for quarterly GRI, 2. type = 2 for GPRH, 3. type = 3 for GPR of countries, 4. type = 4 for GPR words.

gpr <- get_GPR(type = 1)
dygraph(gpr$GPR)

Conclusion

Data from the EPU website have different sources, with different data formats and frequencies. epuR takes cares of the nuisances by providing a consistent way of collecting different index data.

Lingbing/epuR documentation built on May 2, 2020, 1:22 p.m.