trends: Google Trends for Crude Oil Data.

In fDMA: Dynamic Model Averaging and Dynamic Model Selection for Continuous Outcomes

Google Trends for Crude Oil Data.

Description

Google Trends for Crude Oil Data.

Usage

data(trends)

Format

trends is xts object such that

• trends$prod – Google Trends for "oil production"

• trends$cons – Google Trends for "oil consumption"

• trends$econ_act – Google Trends for "economic activity"

• trends$r – Google Trends for "interest rate"

• trends$stocks – Google Trends for "stock markets"

• trends$risk – Google Trends for "market stress"

• trends$ex_rate – Google Trends for "exchange rate"

Details

The data are in monthly frequency. They cover the period between Jan, 2004 and Oct, 2024.

Source

The data are provided by Google.

https://trends.google.com/trends

Examples

data(trends)
gtrends <- trends/100
data(crudeoil)
wti <- crudeoil[-1,1]
drivers <- (lag(crudeoil[,-1],k=1))[-1,]
ld.wti <- (diff(log(wti)))[-1,]
ld.drivers <- drivers[-1,]
ld.drivers[,c(4,6)] <- (diff(drivers[,c(4,6)]))[-1,]
ld.drivers[,c(1:2,5,7)] <- (diff(log(drivers[,c(1:2,5,7)])))[-1,]
ld.drivers[,c(3,6)] <- ld.drivers[,c(3,6)]/100
ld.wti <- ld.wti['2004-01-01-/']
ld.drivers <- ld.drivers['2004-01-01-/']

xx <- ld.drivers
m <- fDMA(y=ld.wti,x=xx,alpha=0.99,lambda=0.99,initvar=1,model="dma",gprob=gtrends,omega=0.5)

fDMA documentation built on April 4, 2025, 5:16 a.m.