NEWS - PINstimation Package

Version 0.1.2 [20.03.2023]

We introduce a new function called classify_trades() that enables users to classify high-frequency (HF) trades individually, without aggregating them.
For each HF trade, the function assigns a variable that is set to TRUE if the trade is buyer-initiated, or FALSE if it is seller-initiated.
The aggregate_trades() function enables users to aggregate high-frequency (HF) trades at different frequencies. In the previous version, HF trades were automatically aggregated into daily trade data. However, with the updated version, users can now specify the desired frequency, such as every 15 minutes.

We identified and corrected an error in the mpin_ecm() function. Previously, the function would sometimes produce inconsistent results as the posterior distribution allowed for the existence of information layers with a probability of zero. We have now fixed this issue and the function produces correct results.
We have made some updates to the mpin_ml() function to better handle cases where the MPIN estimation fails for all initial parameter sets. Specifically, we have fixed an error in the display of the estimation results when such failure occurs. With these updates, the function should now be able to handle such failures more robustly and provide appropriate feedback.
We have simplified the ECM estimation functions, with a particular focus on the adjpin() function. We have improved the convergence condition of the iterative process used in the ECM estimation. Moreover, we rounded the values of the parameters at each iteration to a relevant number of decimals. This shall result in a faster convergence and prevent issues with decreasing likelihood values.

The functions pin(), pin_*(), mpin_ml(), mpin_ecm(), adjpin(), vpin(), and aggregate_trades() accept now, for their arguments data, datasets of type matrix. In the previous version, it only accepted dataframes, which did not allow users, for instance, to use rollapply() of the package zoo.
Introduction of the function pin_bayes() that estimates the original pin model using a bayesian approach as described in Griffin et al.(2021).

Fixed an error in the function initials_pin_ea() as it used to produce some parameter sets with negative values for trade intensity rates. The negative trade intensity rates are set to zero.
Fixed two errors in the function vpin(): (1) A bug in the calculation steps of vpin (2) The argument verbose does not work properly.
Fixed an issue with resetting the plan for the future (future::plan) used for parallel processing.