# [R] Interpretation of Variance decomposition in VAR model

From: Ron Michael <ron_michael70_at_yahoo.com>
Date: Thu, 20 Mar 2008 11:32:03 -0700 (PDT)

Hi all,

This question is not really R related, rather on Statistics subject itself. Even I did not do those using R. however still I want to post it here, because my hope is I could get help from great statisticians who are the very active member of this group.

My problem is to interpret Variance decomposition of VAR model in layman's language.

Using EViews I got following :

Variance Decomposition of LN_FU:
Period S.E. LN_SPOT LN_FU

``` 1	 0.024422	 93.66959	 6.330413
2	 0.034838	 94.36506	 5.634938
3	 0.042280	 94.60712	 5.392882
4	 0.048540	 94.30725	 5.692747
5	 0.054060	 93.99039	 6.009611
6	 0.059042	 93.67545	 6.324554
7	 0.063621	 93.33405	 6.665951
8	 0.067885	 92.99347	 7.006529
9	 0.071893	 92.65966	 7.340337
10	 0.075687	 92.33266	 7.667341

Variance Decomposition of LN_SPOT:
Period	S.E.	LN_SPOT	LN_FU

1	 0.023745	 100.0000	 0.000000
2	 0.033741	 99.51122	 0.488785
3	 0.041018	 99.25339	 0.746605
4	 0.047204	 98.98354	 1.016462
5	 0.052660	 98.62401	 1.375990
6	 0.057600	 98.24985	 1.750151
7	 0.062155	 97.86303	 2.136970
8	 0.066400	 97.46197	 2.538034
9	 0.070394	 97.05655	 2.943451
10	 0.074176	 96.65102	 3.348978

```

Cholesky Ordering: LN_SPOT LN_FU

Myquestion is How to interpret those result in layman language? If I sayfollowing : "93.66959% of tomorrow's volatility in LN_FU is explainedby LN_SPOT's today volatility and remaining 6.330413% is explained byit's today's Volatility", is this statement correct? Then what will bethe interpretation of remaining numbers like : 94.36506,5.634938,......etc?

And also What could be the interpretation of the SE in layman's term?

I already gone through Eviews help file however, did not get anything. If u people here help me on this regard, I will be very very grateful.

Regards,

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