Cointegration Test Including Multiple Breaks Using GAUSS

In this tutorial on Cointegration Test Including Multiple Breaks Using GAUSS, we will show usng GUASS how to estimate cointegration tests with multiple breaks. Abdulnasser Hatemi-J has written an excellent paper titled: Tests for cointegration with two unknown regime shifts with an application to financial market integration which describes the methodology of Cointegration Test Including Multiple Breaks and we have used his written code available here. The abstract of the paper is quoted here:

It is widely agreed in empirical studies that allowing for potential structural change in economic processes is an important issue. In existing literature, tests for cointegration between time series data allow for one regime shift. This paper extends three residual-based test statistics for cointegration to the cases that take into account two possible regime shifts. The timing of each shift is unknown a priori and it is determined endogenously. The distributions of the tests are non-standard. We generate new critical values via simulation methods. The size and power properties of these test statistics are evaluated through Monte Carlo simulations, which show the tests have small size distortions and very good power properties. The test methods introduced in this paper are applied to determine whether the financial markets in the US and the UK are integrated.

Cointegration Test Including Multiple Breaks Using GAUSS can be replicated using the following video tutorial or simply follow the steps:

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Asymmetric Causality Gauss Video

In this video tutorial, we present a simple tutorial on how to run Asymmetric Causality using Gauss. The Asymmetric Causality Gauss Video has not been recorded so far anywhere so we thank to Scott Hacker and Abdulnasser Hatemi who has published their code on Ideas platform here: https://ideas.repec.org/c/boc/bocode/g00012.html and fmwww.bc.edu here: fmwww.bc.edu/repec/bocode/h/HHcte.prg. The program performs a bootstrap test for causality with endogenous lag order developed by Hacker and Hatemi-J (2010).

Please cite the Asymetric Causality and Bootstrap Procedure as:

Hacker and Hatemi-J (2010) A Bootstrap Test for Causality with Endogenous Lag Length Choice - theory and application in finance,  Working Paper Series in Economics and Institutions of Innovation 223, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.

In this video tutorial on Asymmetric Causality Gauss Video, please following along these steps to complete testing asymmetric causality between two series (positive - positive or positive-negative cummulative sum of squares) that we can create using Eviews before running GAUSS.

genr dlntrade=lntrade-lntrade(-1)
genr dlntrade = lntrade-lntrade(-1)
genr pos = dlntrade >=0
genr dlntrade_p = pos*dlntrade
genr dlntrade_n = (1-pos)*dlntrade
genr lntrade_p = @cumsum(dlntrade_p)
genr lntrade_n = @cumsum(dlntrade_n)

The above code should be run line by line in Eviews to create cusum for the given series. To create the cusum for lngdp, just replace the lntrade with lngdp using any text editor, we used notepad here.

Then open the cusum of all series in Eviews spread sheet, copy it to Excel and copy the pair of positive-positive or positive-negative cusum and paste into the GAUSS new file. Save it to the directory you are working in as a txt file. We named that as data.txt.

Then open the HHcte.prg download from above links in GAUSS and change the file name, initial arguments for bootstrapping the computed statistic.

Asymmetric Causality Gauss Video is the first on internet to help the public to apply Asymmetric Causality using Gauss developed by Hatemi (2012) and subsequently very popular among macroeconomic and financial researchers. The Asymmetric Causality Gauss Video is a gift to the academic community from AnEconomist.com and all our admiration is to Hatemi and his co-authors.

Other interesting work of Hatemi and his co-authors can be read from: https://ideas.repec.org/e/pha24.html

Watch Asymmetric Causality Gauss Video

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