eventstudies package unable to handle integer dates

[mithilaas]

eventstudies package accepts only "POSIXt" or "Date" format for dates and gives the following error when integers are used as index for the zoo object.

Error in phys2eventtime(z = returns$firm.returns, events = event.list[i,  : 
  events$when should be one of 'Date' or 'date-time' classes.

Here is a MWE for the issue:

library(eventstudies)
library(zoo)

### Creating data
time.dt  <- data.frame(name=c("A","B","C","D","E"),
                       when=c(3,3,3,3,3))
gdp.dt  <- data.frame(state=rep(c("A","B","C","D","E"),each=10),
                      year=rep(c(1,2,3,4,5,6,7,8,9,10),each = 1),
                      gdp= rep(c(100,100,100,160,165,50,50,50,80,85),each = 5))

dtlist <- split(gdp.dt, gdp.dt$state)

z_list <- lapply(dtlist, function(x) zoo(x$gdp, order.by = x$year))

exp.zoo  <- do.call(merge, z_list)

### eventstudy
es <- eventstudies::eventstudy(firm.returns = exp.zoo,
                 event.list = time.dt,
                 event.window = 2,
                 is.levels=FALSE,
                 type = "None",
                 to.remap = FALSE,
                 inference = TRUE)

[Toodlepoodle]

Suggested solution:
As per Anshul's PR - [WIP] Allow numeric types in phys2eventtime #14
Using Anshuls branch of eventstudy we see that the package is able to deal with integer dates.

library(zoo)
library(boot)

### Creating data
time.dt  <- data.frame(name=c("A","B","C","D","E"),
                       when=c(3,6,7,4,4))
gdp.dt  <- data.frame(state=rep(c("A","B","C","D","E"),each=10),
                      year=rep(c(1,2,3,4,5,6,7,8,9,10),each = 1),
                      gdp= rep(c(100,100,100,160,165,50,50,50,80,85),each = 5))

files.sources = list.files()
sapply(files.sources, source)

dtlist <- split(gdp.dt, gdp.dt$state)

z_list <- lapply(dtlist, function(x) zoo(x$gdp, order.by = x$year))

exp.zoo  <- do.call(merge, z_list)

### eventstudy
es <- eventstudy(firm.returns = exp.zoo,
                 event.list = time.dt,
                 event.window = 2,
                 is.levels=FALSE,
                 type = "None",
                 to.remap = FALSE,
                 inference = TRUE)

The output is as follows

2.5% Mean   97.5%
  56   76     96.000
  56   88     122.050
  56   88     122.050
  57   89     123.025