Hi all,

I want to calculate exponentially weighted moving average of past performance in an unbalanced panel dataset. Panel variable is firm_id and time variable is year. The time period is between 2010 and 2015 (six years). I have been trying to compute the exponentially weighted average of past performance. Thus, I picked tssmooth exponential. However, when I applied the tssmooth exponential, all the firms without all six-year observations generated missing values for the exponential smoothed performance. For example, if Firm 2 has only 2010, 2011, and 2012, the exponentially weighted average of past performance for Firm 2 will be missing. I get stuck on how to deal with these missing values because they are a lot (about 40% in my data). Are there any other ways to compute the exponentially weighted average of past performance? I am looking forward to any suggestions! Thank you in advance!

Here is sample data:
Code:
*To install: 
ssc install dataex 
clear 
input int year int firm_id float performance
2010    1    0.053
2011    1    0.075
2012    1    0.088
2013    1    0.036
2014    1    0.049
2015    1    -0.109
2010    2    0.464
2011    2    -0.014
2012    2    -0.002
2010    3    0.120
2012    3    0.018
2010    4    0.038
2011    4    0.045
2012    4    0.051
2013    4    0.127
2014    4    0.057
2015    4    0.059
2010    5    0.134
2011    5    0.117

end
My codes for computing the weighted moving average of the past performance. I adjust smoothing parameter from 0.1 to 0.9 with an increment of 0.1
Code:
tsset firm_id year
tssmooth e double p1=performance, parms(0.1)
tssmooth e double p2=performance, parms(0.2)
tssmooth e double p3= performance, parms(0.3)
tssmooth e double p4= performance, parms(0.4)
tssmooth e double p5= performance, parms(0.5)
tssmooth e double p6= performance, parms(0.6)
tssmooth e double p7= performance, parms(0.7)
tssmooth e double p8= performance, parms(0.8)
tssmooth e double p9= performance, parms(0.9)
The screenshot shows the missing data. Newly generated variables p1-p9.
Array