Hi I'm working with cross country time series data, along with binary variables as dependent variables. I'm sure it's something simple, but wondering if anyone would be willing to provide any feedback. I'd very much appreciate it. Here you can see one of the features with missing values and the imputed values using Amelia II for the country AUS from 1870 to 1872 at the beginning of the time series, then again from 1946-1952. , The numbers imputed are nonsensical, in that they appear to be sampled from the broader cross section of countries, and there is no trend to them. For this type of study, it is common to use linear interpolation. Here is the code, data, and screenshots. Also, if anyone wants to me help me build models to forecast crises of various sorts, that'd be great too. I'm a noob at the modeling but have a lot of ideas for good models. . Thanks again, Brendan #data wrangling library(dplyr) library(Amelia) #impute missing values RRcrisis_panel <- read.csv("schularick_taylor_RRcrises_panel.csv") head(RRcrisis_panel) RRcrisis_panel_loans1 <- select(RRcrisis_panel, year, iso, loans1) a.RRcrisis_panel <- amelia(RRcrisis_panel_loans1, m = 3, ts = "year", cs = "iso", polytime = 1) #look at individual impututations a.RRcrisis_panel$imputations[[1]] head(a.RRcrisis_panel$imputations[[1]]) #save the five different imputations write.amelia(obj=a.RRcrisis_panel, file.stem = "amelia_11_16_2015") Before Imputing [image: Inline image 2] After Imputing [image: Inline image 1] Before Imputing [image: Inline image 3] After Imputing: [image: Inline image 4] Before Imputing [image: Inline image 1] after imputing [image: Inline image 2] ᐧ

Bounding the data will definitely help, but I'm wondering if a better way exists. For example, a pretty clear trend exists between row 9 and 15 below. I had thought setting as a time series data would help get numbers closer to the figures surrounding it, and/or preferably apply a linear interpolation? 1. 607 2. 628 3. 651 4. 624 5. 618 6. 547 7. 517 8. 503 *9. X10. X11. X12. X13. X14. X* 15. 1,670 16. 1,961 17. 2,300 1. 607 2. 628 3. 651 4. 624 5. 618 6. 547 7. 517 8. 503 *9. -16,387,91010. 805,97011. -14,144,69112. -9,984,22313. 12,804,68514. 7,229,454* 15. 1,670 16. 1,961 17. 2,300 ᐧ On Thu, Nov 19, 2015 at 4:33 AM, Skip Barbour &lt;russellbarbour(a)gmail.com&gt; wrote: -- Amelia mailing list served by HUIT [Un]Subscribe/View Archive: http://lists.gking.harvard.edu/?info=amelia More info about Amelia: http://gking.harvard.edu/amelia Amelia mailing list Amelia(a)lists.gking.harvard.edu To unsubscribe from this list or get other information: https://lists.gking.harvard.edu/mailman/listinfo/amelia