Data Structuring for Seasonality and Variable Control

[dachosen1]

I’m trying to understand how to best structure data to account for seasonality or control for other variables using causal impact.

For example, consider a case where I’m conducting an analysis to measure the impact of an economic policy on GDP, but I want to control for inflation.

Is the below the correct way to structure the data? Does the order of the columns matter?

library(CausalImpact)

gdp<- rnorm(100)
inflation <- rnorm(100)

data <- cbind(gdp, inflation)

pre.period <- c(1, 50)
post.period <- c(51, 100)

impact <- CausalImpact(data, pre.period, post.period)