# Re: st: Missing values-Times series

 From Philipp Rehm To statalist@hsphsun2.harvard.edu Subject Re: st: Missing values-Times series Date Tue, 29 Aug 2006 21:35:33 +0200

```You may want to use -ipolate-.

Example:

clear
input	Day	Y_observed	Y_Inferred
1	10	10
2	.	25
3	50	50
4	.	50
5	50	50
6	.	40
7	.	30
8	20	20
9	40	40
end

ipolate  Y_observed  Day , generate(Y_ipolate)

list
+--------------------------------------+
| Day   Y_obse~d   Y_Infe~d   Y_ipol~e |
|--------------------------------------|
1. |   1         10         10         10 |
2. |   2          .         25         30 |
3. |   3         50         50         50 |
4. |   4          .         50         50 |
5. |   5         50         50         50 |
|--------------------------------------|
6. |   6          .         40         40 |
7. |   7          .         30         30 |
8. |   8         20         20         20 |
9. |   9         40         40         40 |
+--------------------------------------+

(Note that your value for Y_Inferred for Day 2 is probably wrong).

HTH,
Philipp

Inamo, Jocelyn F. M.D., Ph.D. wrote:
```
Dear STATA Users

I would like to fill the missing values in my time series data shown in the first table, assuming that there is a linear progression between the observed proxy values. Accordingly, I would like to obtain inferred values of Y as shown in the second table.

X Y 1 10 2 3 50 4 5 50 6 7 8 20 9 40

Day Y observed Y Inferred 1 10 10 2 25 3 50 50 4 50 5 50 50 6 40 7 30 8 20 20 9 40 40

The most accurate way to do this would be to go to each missing value and to compute a linear equation within the proxy values of Y. I realized that it was more easy to say than to do (for me at least).

The second way would be to use the smoothing commands of stata. I realized that ksm-lowess work pretty well when set with the smallest bandwidth possible, but I have been unable to recover the inferred values of Y.

Any suggestion?

Thanks
Jocelyn

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