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

Alan Mustafa () and Abdulnasser Hatemi-J ()
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Alan Mustafa: Da Vinci Institute
Abdulnasser Hatemi-J: UAE University

Statistical Software Components from Boston College Department of Economics

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.

Language: Python
Requires: Python
Keywords: integrated erries; cumulative sums; asymmetric causality tests (search for similar items in EconPapers)
Date: 2026-06-21
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http://fmwww.bc.edu/repec/bocode/p/PyTIVIPNC.py program code (text/plain)
http://fmwww.bc.edu/repec/bocode/o/Oil_WTI.csv program code (text/plain)

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