Timeline for Simulating a data generating process
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Dec 19, 2014 at 17:42 | vote | accept | mlofton | ||
| Dec 18, 2014 at 16:23 | comment | added | mlofton | great stuff. if this answer is any indication of the quality of this list, then it's an amazing list. thanks again. | |
| Dec 18, 2014 at 16:17 | comment | added | Aksakal | @mlofton, yes, something like that. The main idea is to base the variance of errors on some fraction of the variance of the dependent variable. The rest is details. I wouldn't worry about backing out exact sigma of errors from MA terms. All you need is to get the sensible sigma's range for simulation tests. | |
| Dec 18, 2014 at 16:01 | comment | added | mlofton | just clicked. I think I get what you are saying now. Is it the following: Calculate SSTOT. Then back out SSERR so that SSERR /SSTOT = 0.2 say. Then, calculate SSERR/n-k as the estimate of simgasquared. Then, set the estimate of sigmasquared to var(e_t) + gamma^2* var(e_t) to back out var(e_t) ? I hope that's right. thanks. | |
| Dec 18, 2014 at 15:52 | comment | added | mlofton | interesting: one idea I had which sounds similar is the following: take one of the data sets, estimate it blindly to get the estimated parameters. then back out the estimated variance using rss/n-k and use that as my sigmasquared hat and then set that sigmasquared hat = var(e_t) + gammasquared var(e_t) to back out var(e_t). is that similar to what you are saying ? if so, I don't understand the 0.2 but I'll click on link. thanks. | |
| Dec 18, 2014 at 15:36 | history | answered | Aksakal | CC BY-SA 3.0 |