Generates random initial parameter sets to be used in the estimation of the
`AdjPIN`

model of Duarte and Young (2009)
.

## Usage

```
initials_adjpin_rnd(data, restricted = list(), num_init = 20,
verbose = TRUE)
```

## Arguments

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

- restricted
A binary list that allows estimating restricted AdjPIN models by specifying which model parameters are assumed to be equal. It contains one or multiple of the following four elements

`{theta, mu, eps, d}`

. For instance, If`theta`

is set to`TRUE`

, then the probability of liquidity shock in no-information days, and in information days is assumed to be the same (\(\theta\)`=`

\(\theta'\)). If any of the remaining rate elements`{mu, eps, d}`

is set to`TRUE`

, (say`mu=TRUE`

), then the rate is assumed to be the same on the buy side, and on the sell side (\(\mu\)_{b}`=`

\(\mu\)_{s}). If more than one element is set to`TRUE`

, then the restrictions are combined. For instance, if the argument`restricted`

is set to`list(theta=TRUE, eps=TRUE, d=TRUE)`

, then the restricted AdjPIN model is estimated, where \(\theta\)`=`

\(\theta'\), \(\epsilon\)_{b}`=`

\(\epsilon\)_{s}, and \(\Delta\)_{b}`=`

\(\Delta\)_{s}. If the value of the argument`restricted`

is the empty list (`list()`

), then all parameters of the model are assumed to be independent, and the unrestricted model is estimated. The default value is the empty list`list()`

.- num_init
An integer corresponds to the number of initial parameter sets to be generated. The default value is

`20`

.- 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`

.

## Value

Returns a dataframe of numerical vectors of ten elements
{\(\alpha\), \(\delta\), \(\theta\), \(\theta'\),
\(\epsilon\)_{b}, \(\epsilon\)_{s}, \(\mu\)_{b}, \(\mu\)_{s}, \(\Delta\)_{b}, \(\Delta\)_{s}}.

## 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.

The buy rate parameters {\(\epsilon\)_{b}, \(\mu\)_{b}, \(\Delta\)_{b}} are randomly generated
from the interval (`minB`

, `maxB`

), where `minB`

(`maxB`

) is the smallest
(largest) value of buys in the dataset, under the condition that
\(\epsilon\)_{b}`+`

\(\mu\)_{b}`+`

\(\Delta\)_{b}< `maxB`

. Analogously, the sell rate parameters
{\(\epsilon\)_{s}, \(\mu\)_{s}, \(\Delta\)_{s}} are randomly generated from the interval (`minS`

, `maxS`

),
where `minS`

(`maxS`

) is the smallest(largest) value of sells in the
dataset, under the condition that \(\epsilon\)_{s}`+`

\(\mu\)_{s}`+`

\(\Delta\)_{s} < `maxS`

.

## References

Duarte J, Young L (2009).
“Why is PIN priced?”
*Journal of Financial Economics*, **91**(2), 119--138.
ISSN 0304405X.

## 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 transactions ('B') and seller-initiated
# transactions ('S') on that day. To know more, type ?dailytrades
xdata <- dailytrades
# Obtain a dataframe of 40 random initial parameters for the MLE of
# the AdjPIN model using the initials_adjpin_rnd().
initial.sets <- initials_adjpin_rnd(xdata, num_init = 40)
#> The function initials_adjpin_rnd(...) has generated 40 initial parameter sets.
#>
To display the initial sets, store them in a variable or call (initials_adjpin_rnd(...)).
#>
To hide these messages, set the argument 'silent' to TRUE (silent = TRUE).
#>
# Use the dataframe to estimate the AdjPIN model using the adjpin()
# function.
estimate <- adjpin(xdata, initialsets = initial.sets, verbose = FALSE)
# Show the value of adjusted PIN
show(estimate@adjpin)
#> [1] 0.2951414
```