PINstimation (version 0.1.2)

initials_mpin: MPIN initial parameter sets of Ersan (2016)

Description

Based on the algorithm in Ersan2016;textualPINstimation, generates initial parameter sets for the maximum likelihood estimation of the MPIN model.

Usage

initials_mpin(data, layers = NULL, detectlayers = "EG",
 xtraclusters = 4, verbose = TRUE)

Value

Returns a dataframe of initial parameter sets each consisting of 3J + 2 variables {\(\alpha\), \(\delta\), \(\mu\), \(\epsilon\)_b, \(\epsilon\)_s}. \(\alpha\), \(\delta\), and \(\mu\) are vectors of length J where J is the number of layers in the MPIN model.

Arguments

data

A dataframe with 2 variables: the first corresponds to buyer-initiated trades (buys), and the second corresponds to seller-initiated trades (sells).

layers

An integer referring to the assumed number of information layers in the data. If the value of layers is NULL, then the number of layers is automatically determined by one of the following functions: detectlayers_e(), detectlayers_eg(), and detectlayers_ecm(). The default value is NULL.

detectlayers

A character string referring to the layer detection algorithm used to determine the number of layers in the data. It takes one of three values: "E", "EG", and "ECM". "E" refers to the algorithm in Ersan2016;textualPINstimation, "EG" refers to the algorithm in Ersan2022a;textualPINstimation; while "ECM" refers to the algorithm in Ghachem2022;textualPINstimation. The default value is "EG". Comparative results between the layer detection algorithms can be found in Ersan2022a;textualPINstimation.

xtraclusters

An integer used to divide trading days into #(1 + layers + xtraclusters) clusters, thereby resulting in #comb(layers + xtraclusters, layers) initial parameter sets in line with ErsanAlici2016;textualPINstimation, and Ersan2016;textualPINstimation. The default value is 4 as chosen in Ersan2016;textualPINstimation.

verbose

a binary variable that determines whether information messages about the initial parameter sets, including the number of the initial parameter sets generated. No message is shown when verbose is set to FALSE. The default value is TRUE.

Details

The argument 'data' should be a numeric dataframe, and contain at least two variables. Only the first two variables will be considered: The first variable is assumed to correspond to the total number of buyer-initiated trades, while the second variable is assumed to correspond to the total number of seller-initiated trades. Each row or observation correspond to a trading day. NA values will be ignored.

References

Examples

# There is a preloaded quarterly dataset called 'dailytrades' with 60
# observations. Each observation corresponds to a day and contains the
# total number of buyer-initiated trades ('B') and seller-initiated
# trades ('S') on that day. To know more, type ?dailytrades

xdata <- dailytrades

# Obtain a dataframe of initial parameter sets for estimation of the MPIN
# model using the algorithm of Ersan (2016) with 3 extra clusters.
# By default, the number of layers in the data is detected using the
# algorithm of Ersan and Ghachem (2022a).

initparams <- initials_mpin(xdata, xtraclusters = 3, verbose = FALSE)

# Show the six first initial parameter sets

print(round(t(head(initparams)), 3))

# Use 10 randomly selected initial parameter sets from initparams to
# estimate the probability of informed trading via mpin_ecm. The number
# of information layers will be detected from the initial parameter sets.

numberofsets <- nrow(initparams)
selectedsets <- initparams[sample(numberofsets, 10),]
# \donttest{
estimate <- mpin_ecm(xdata, initialsets = selectedsets, verbose = FALSE)

# Display the estimated MPIN value

show(estimate@mpin)

# Display the estimated parameters as a numeric vector.

show(unlist(estimate@parameters))

# Store the posterior probabilities in a variable, and show the first 6 rows.

modelposteriors <- get_posteriors(estimate)
show(round(head(modelposteriors), 3))
# }