{"action":"created","comment":{"url":"https://api.github.com/repos/statsmodels/statsmodels/pulls/comments/76438485","id":76438485,"diff_hunk":"@@ -1287,17 +1461,20 @@ def reorder(self, order):\n #-------------------------------------------------------------------------------\n # VAR Diagnostics: Granger-causality, whiteness of residuals, normality, etc.\n \n-    def test_causality(self, equation, variables, kind='f', signif=0.05,\n-                       verbose=True):\n-        \"\"\"Compute test statistic for null hypothesis of Granger-noncausality,\n-        general function to test joint Granger-causality of multiple variables\n+    def test_causality(self, causing, kind='f', signif=0.05, verbose=True):","path":"statsmodels/tsa/vector_ar/var_model.py","position":309,"original_position":309,"commit_id":"5c125780627705b7c9f2bc041d4898f757ab2c0e","original_commit_id":"5c125780627705b7c9f2bc041d4898f757ab2c0e","user":{"login":"josef-pkt","id":440735,"avatar_url":"https://avatars.githubusercontent.com/u/440735?v=3","gravatar_id":"","url":"https://api.github.com/users/josef-pkt","html_url":"https://github.com/josef-pkt","followers_url":"https://api.github.com/users/josef-pkt/followers","following_url":"https://api.github.com/users/josef-pkt/following{/other_user}","gists_url":"https://api.github.com/users/josef-pkt/gists{/gist_id}","starred_url":"https://api.github.com/users/josef-pkt/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/josef-pkt/subscriptions","organizations_url":"https://api.github.com/users/josef-pkt/orgs","repos_url":"https://api.github.com/users/josef-pkt/repos","events_url":"https://api.github.com/users/josef-pkt/events{/privacy}","received_events_url":"https://api.github.com/users/josef-pkt/received_events","type":"User","site_admin":false},"body":"We cannot change the API, call signature and returns, in a backwards incompatible way without deprecation, in old/established code.","created_at":"2016-08-26T15:31:14Z","updated_at":"2016-08-26T15:31:14Z","html_url":"https://github.com/statsmodels/statsmodels/pull/3070#discussion_r76438485","pull_request_url":"https://api.github.com/repos/statsmodels/statsmodels/pulls/3070","_links":{"self":{"href":"https://api.github.com/repos/statsmodels/statsmodels/pulls/comments/76438485"},"html":{"href":"https://github.com/statsmodels/statsmodels/pull/3070#discussion_r76438485"},"pull_request":{"href":"https://api.github.com/repos/statsmodels/statsmodels/pulls/3070"}}},"pull_request":{"url":"https://api.github.com/repos/statsmodels/statsmodels/pulls/3070","id":74912793,"html_url":"https://github.com/statsmodels/statsmodels/pull/3070","diff_url":"https://github.com/statsmodels/statsmodels/pull/3070.diff","patch_url":"https://github.com/statsmodels/statsmodels/pull/3070.patch","issue_url":"https://api.github.com/repos/statsmodels/statsmodels/issues/3070","number":3070,"state":"open","locked":false,"title":"[WIP] [GSoC 2016] VECM #3013","user":{"login":"yogabonito","id":7026269,"avatar_url":"https://avatars.githubusercontent.com/u/7026269?v=3","gravatar_id":"","url":"https://api.github.com/users/yogabonito","html_url":"https://github.com/yogabonito","followers_url":"https://api.github.com/users/yogabonito/followers","following_url":"https://api.github.com/users/yogabonito/following{/other_user}","gists_url":"https://api.github.com/users/yogabonito/gists{/gist_id}","starred_url":"https://api.github.com/users/yogabonito/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/yogabonito/subscriptions","organizations_url":"https://api.github.com/users/yogabonito/orgs","repos_url":"https://api.github.com/users/yogabonito/repos","events_url":"https://api.github.com/users/yogabonito/events{/privacy}","received_events_url":"https://api.github.com/users/yogabonito/received_events","type":"User","site_admin":false},"body":"The VECM framework developed in this year's GSoC aims to add the support for non-stationary VAR processes to statsmodels.\r\nCurrently, the following topics are implemented (see also this [Jupyter-notebook](https://gist.github.com/yogabonito/5461b26bed335cad6907aa4e613acb99#file-gsoc-vecm-ipynb)):\r\n- Parameter estimation,\r\n- forecasting,\r\n- testing for Granger-causality,\r\n- testing for instantaneous causality (not on my initial todo-list and may be moved to vector_ar.py to extend the VAR framework's functionality),\r\n- impulse-response analysis, and\r\n- lag order selection.\r\n\r\nSome of them work without _and_ with deterministic terms in the model. Some functions, however, make use of the existing VAR framework which lacks support for some deterministic terms, such as seasonal terms. As a consequence much of the VECM-functionality relying on the VAR framework is not working with deterministic terms yet. This limitation will be overcome with the refactoring of the VAR-related code.\r\n\r\nFuture development will also include the implementation of the following VECM-related functions:\r\n- Testing for the rank of cointegration,\r\n- checking for residual autocorrelation,\r\n- testing for non-normality (I have already begun working on this), and\r\n- tests for structural change.\r\n\r\nIt's been a great summer and I am looking forward to working on these open issues!\r\n\r\n-----------------------------------------------------------------------------------------------------------------------------------\r\n_Original comment:_\r\nEstimation of parameters and tests have been added.\r\nA failing test (related to the parameter matrix for the deterministic terms) indicates that deterministic seasonal terms are not implemented correctly yet.\r\n@josef-pkt, @bashtage, can you have a look at 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