pmpp_predinterval: Random-Window Block Bootstrap for prediction intervals for...
In pmpp: Posterior Mean Panel Predictor

Description Usage Arguments Value Author(s) References Examples

Produces prediction intervals for Posterior Mean Panel Predictor model by means of resampling with replacement from model's residuals. Block Bootstrap method takes into account heteroskedasticity of the error terms, both across units and over time. Block window is chosen randomly.

1 2	pmpp_predinterval(model, fframe, boot_reps = 1000, block_size = NULL, confidence = 0.95, iter = NULL)

`model`	PMPP model, as returned by `pmpp()`
`fframe`	`data.frame` with the same columns as input data to `model`, but with empty rows added to each cross-sectional unit, as created by `create_fframe()`
`boot_reps`	integer; number of bootstrap replications
`block_size`	integer; width of the re-sampled block of residuals
`confidence`	numeric in (0,1); confidence level of the interval
`iter`	iterating constant, to be used in a loop when extraction from call is needed

A data.frame with panel indices, lower and upper bounds and midpoint.

Michal Oleszak

Oleszak, M. (2018). "Forecasting sales with micro-panels: Empirical Bayes approach. Evidence from consumer goods sector.", Erasmus University Thesis Repository

## Not run: data(EmplUK, package = "plm")
EmplUK <- dplyr::filter(EmplUK, year %in% c(1978, 1979, 1980, 1981, 1982))
pmpp_model <- pmpp(dep_var = "emp", data = EmplUK)
my_fframe <- create_fframe(EmplUK, 1983:1985)
intervals <- pmpp_predinterval(pmpp_model, my_fframe, boot_reps = 10)

## End(Not run)