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PyTIVIPNC: Python Module for Transforming an Integrated Variable with and without Deterministic Trend Parts into Positive and Negative Components

Author

Listed:
  • Alan Mustafa

    (Da Vinci Institute)

  • Abdulnasser Hatemi-J

    (UAE University)

Programming Language

Abstract

This is the first module, to our best knowledge, produced in Python for transforming integrated time series variables of the first degree into partial cumulative sums for positive and negative components. It allows for the potential deterministic parts such as a drift and a trend in the data generating process. The transformed data can be used for conducting asymmetric causality tests of Hatemi-J (2012) and for estimating the asymmetric impulse response functions and variance decompositions developed by Hatemi-J (2014). The module is very consumer friendly via a Graphical User Interface (GUI). Furthermore, the time plots of the original data and the transformed data for positive and negative components are produced by the module.

Suggested Citation

  • Alan Mustafa & Abdulnasser Hatemi-J, 2026. "PyTIVIPNC: Python Module for Transforming an Integrated Variable with and without Deterministic Trend Parts into Positive and Negative Components," Statistical Software Components P00005, Boston College Department of Economics.
    • Handle: RePEc:boc:bocode:p00005
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