PINstimation (version 0.1.2)

mpin_ml: MPIN model estimation via standard ML methods

Description

Estimates the multilayer probability of informed trading (MPIN) using the standard Maximum Likelihood method.

Usage

mpin_ml(data, layers = NULL, xtraclusters = 4, initialsets = NULL,
detectlayers = "EG", ..., verbose = TRUE)

Value

Returns an object of class estimate.mpin

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 argument layers is given, then the maximum likelihood estimation will use the number of layers provided. If layers is omitted, the function mpin_ml() will find the optimal number of layers using the algorithm developed in Ersan2022a;textualPINstimation (as default).

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.

initialsets

A dataframe containing initial parameter sets for the estimation of the MPIN model. The default value is NULL. If initialsets is NULL, the initial parameter sets are determined by the function initials_mpin().

detectlayers

A character string referring to the layer detection algorithm used to determine the number of layer 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.

...

Additional arguments passed on to the function mpin_ml. The recognized argument is is_parallel. is_parallel is a logical variable that specifies whether the computation is performed using parallel processing. The default value is FALSE.

verbose

A binary variable that determines whether detailed information about the steps of the estimation of the MPIN model is displayed. No output is produced 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

# ------------------------------------------------------------------------ #
# Estimate MPIN model using the standard ML method                         #
# ------------------------------------------------------------------------ #

# Estimate the MPIN model using mpin_ml() assuming that there is a single
# information layer in the data. The model is then equivalent to the PIN
# model. The argument 'layers' takes the value '1'.
# We use two extra clusters to generate the initial parameter sets.

estimate <- mpin_ml(xdata, layers = 1, xtraclusters = 2, verbose = FALSE)

# Show the estimation output

show(estimate)

# Estimate the MPIN model using the function mpin_ml(), without specifying
# the number of layers. The number of layers is then detected using Ersan and
# Ghachem (2022a).
# -------------------------------------------------------------
# \donttest{
estimate <- mpin_ml(xdata, xtraclusters = 2, verbose = FALSE)
# }
# Show the estimation output

show(estimate)

# Display the likelihood-maximizing parameters

show(estimate@parameters)

# Display the global multilayer probability of informed trading

show(estimate@mpin)

# Display the multilayer probabilities of informed trading per layer

show(estimate@mpinJ)

# Display the first five initial parameters sets used in the maximum
# likelihood estimation

show(round(head(estimate@initialsets, 5), 4))