Timeline for Forecasting with mixed frequency data
Current License: CC BY-SA 3.0
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| when toggle format | what | by | license | comment | |
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| Aug 25, 2020 at 16:18 | vote | accept | youjustreadthis | ||
| Aug 25, 2020 at 8:31 | comment | added | atecon | In case you search for another software tool apart from MATLAB for estimating MIDAS types of models, I also recommend gretl: gretl.sourceforge.net Ch. 19 and 40 in the manual offer a good reading on the topic, examples and how to implement things: sourceforge.net/projects/gretl/files/manual/gretl-guide-a4.pdf/… | |
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| Apr 18, 2020 at 15:05 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Feb 25, 2020 at 5:02 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Oct 27, 2019 at 0:01 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Sep 25, 2019 at 22:16 | answer | added | Jonas Striaukas | timeline score: 4 | |
| Jun 10, 2016 at 15:26 | comment | added | Aksakal | take a look at MATLAB ssm function help's Algorithms section. The first bullet's about missing observations. So, if you sample at monthly, and some data is quarterly, then it should be able to proceed assuming that not provided monthly data is "missing". | |
| Jun 10, 2016 at 15:03 | history | edited | youjustreadthis | CC BY-SA 3.0 |
added 177 characters in body
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| Jun 10, 2016 at 15:02 | comment | added | youjustreadthis | Interesting! got any good resources? @Aksakal | |
| Jun 10, 2016 at 15:01 | comment | added | Aksakal | state space models can deal with this. you go with the highest frequency, and the algorithm deals with missing data elements naturally | |
| Jun 10, 2016 at 14:11 | history | asked | youjustreadthis | CC BY-SA 3.0 |