generate {PriceIndices}

Generating an artificial scanner dataset

Description

This function provides artificial scanner datasets where prices and quantities are lognormally distributed.

Usage

generate(
  pmi = c(),
  psigma = c(),
  qmi = c(),
  qsigma = c(),
  prec = c(2, 0),
  n = 100,
  n0 = 1,
  r = 1,
  r0 = 1,
  start,
  days = FALSE
)

Arguments

pmi

A numeric vector indicating mi parameters for lognormally distributed prices from the subsequent months.

psigma

A numeric vector indicating sigma parameters for lognormally distributed prices from the subsequent months.

qmi

A numeric vector indicating mi parameters for lognormally distributed quantities from the subsequent months.

qsigma

A numeric vector indicating sigma parameters for lognormally distributed quantities from the subsequent months.

prec

A two-dimensional numeric vector indicating precision, i.e. the number of decimal places, for presenting prices and quantities.

n

An integer parameter indicating the number of products which are to be generated.

n0

An integer parameter indicating the first (the smallest) prodID.

r

An integer parameter indicating the number of outlets (retailer sale points) for which prices and quantities are to be generated.

r0

n0 An integer parameter indicating the first (the smallest) retID.

start

The first period in the generated data frame (as character) limited to the year and month, e.g. '2019-12'.

days

A logical parameter indicating whether the trading day in a given month is to be randomised. The default value of days is FALSE, which means that each transaction for a given month takes place on the first day of the month.

Value

This function returns an artificial scanner dataset where prices and quantities are lognormally distributed. The characteristics for these lognormal distributions are set by pmi, psigma, qmi and qsigma parameters. This function works for a fixed number of products and outlets (see n and r parameters). The generated dataset is ready for further price index calculations.

References

Sulewski, P., Białek, J. (2022). Probability Distribution Modelling of Scanner Prices and Relative Prices. Statistika – Statistics and Economy Journal, Vol. 3/2022, 282-298, Czech Statistical Office, Prague.

Examples

generate(pmi=c(1.02,1.03,1.04),psigma=c(0.05,0.09,0.02),qmi=c(3,4,4),
qsigma=c(0.1,0.1,0.15),start="2020-01",days=TRUE)
generate(pmi=c(1.02,1.03,1.04),psigma=c(0.05,0.09,0.02),qmi=c(6,6,7),
qsigma=c(0.1,0.1,0.15),start="2020-01",n=1000,n0=132578,r=10)