Multivariate Markov-Switching Models Regressions Framework
This Directory contains Multivariate Markov-Switching Models Regressions Framework Written in Python
Reference From " Bellone B. Classical Estimationof Multivariate Markov-Switching Models using MSVARlib[J]. Econometrics, 2005. "
You can use This Package to build a Statistical arbitrage Strategy using MS-VECM (Markov-Switching Vector Error correction Models.)
or Do some empirical Study of Asymmetric effects using MS-VAR( Markov-Switching Vector AutoRegression)
Some Results Display Here:
Time variant impact of M2 on Stocks Market:
impluse response :
Time variant impact of M2 on substantial economy:
impluse response :
For Detail Usage can Go To <a href=>This Page
An empirical Study based on China's macroeconomic and Financial Data.
On the topic of 「Asymmetric effects of M2 on Stocks Market and substantial economy」.(Project during 2018)
""" Notice：
This Python Package are Based On the Gauss Language witten by Benoit BELLONE (2004)
[The GAUSS programs available on the website http://bellone.ensae.net6 are written
and copyrighted c 2004 by Benoit BELLONE, all rights reserved. They can be run on
Gauss 3.2 or upper versions and should be OX-Gauss compliant, thanks to the routine
M@ximize developped by Laurent and Urbain (2005)7. The code is licensed gratis to all
third parties under the terms of this paragraph. Copying and distribution of the files
in this archive is unrestricted if and only if the files are not modified. Modification of
the files is encouraged, but the distribution of modifications of the code in this archive
is unrestricted only if you meet the following conditions: modified files must carry a
prominent notice stating
(i) the original author and date,
(ii) the new author and the date of release of the modification,
(iii) that there is no warrantee for the code, and
(iv) that the work is licensed at no charge to all parties.
If you use the code extensively in your research, you are requested to provide appropriate
attribution and thanks to the author of the code. No representation is made or implied
as to the accuracy or completeness of the programs which may indeed contain bugs or
errors unknown to the author. Benoit Bellone takes no responsibility for results produced
by MSVARlib programs which are used entirely at the reader’s risk. This package is by
no means finished yet, a enhancement “to do list” remains open. If you plan to extend
the library, find any problems or have suggestions for improvement, contact the author.]"""