loop or other methods?

[River Huang]

Dear All, I have this dataset.

* Example generated by -dataex-. To install: ssc install dataex
clear
input byte country float year double I float K2007
1 2007 47639371944.1504 1.027757e+12
1 2008 47079741922.4759 1.027757e+12
1 2009 23310776752.8605 1.027757e+12
1 2010 23169892635.7175 1.027757e+12
1 2011 25140632088.3109 1.027757e+12
1 2012 26392333282.9276 1.027757e+12
1 2013 24184560713.7457 1.027757e+12
1 2014 18385377287.1616 1.027757e+12
1 2015 16695019910.2777 1.027757e+12
1 2016  20107616311.306 1.027757e+12
1 2017 23337869852.3111 1.027757e+12
1 2018  26664902464.842 1.027757e+12
2 2007 6067174174.07463 276483178496
2 2008 7784184466.76024 276483178496
2 2009 9714662213.96143 276483178496
2 2010 10608411137.8261 276483178496
2 2011 11446475617.9555 276483178496
2 2012 12774266788.7859 276483178496
2 2013 14026144934.6544 276483178496
2 2014 15551083505.0151 276483178496
2 2015 17012885354.2527 276483178496
2 2016 17710413653.7783 276483178496
2 2017 21146233903.1325 276483178496
2 2018 24973702239.7895 276483178496
end
label values country country
label def country 1 "乌克兰", modify
label def country 2 "乌兹别克斯坦", modify

For each `country', I'd like to generate a variable `K' which is K_t=(1-0.06)*K_{t-1} + I_t. The value of K in year 2007 is K2007. Any suggestions? Thanks.

[William Lisowski]

I hope this either does what you seek, or at least it points you in a useful direction.

// let's measure these things in millions for readability
replace I = I/1000000
replace K2007 = K2007/1000000
// set up to take advantage of time series operators
xtset country year
generate K = K2007 if year==2007
replace K = (1-0.06) * L.K + I if year>2007
format %9.1f I K2007 K
list, noobs sepby(country)

. list, noobs sepby(country)

  +-------------------------------------------------------+
  |      country   year         I       K2007           K |
  |-------------------------------------------------------|
  |       乌克兰   2007   47639.4   1027757.0   1027757.0 |
  |       乌克兰   2008   47079.7   1027757.0   1013171.3 |
  |       乌克兰   2009   23310.8   1027757.0    975691.8 |
  |       乌克兰   2010   23169.9   1027757.0    940320.2 |
  |       乌克兰   2011   25140.6   1027757.0    909041.6 |
  |       乌克兰   2012   26392.3   1027757.0    880891.4 |
  |       乌克兰   2013   24184.6   1027757.0    852222.5 |
  |       乌克兰   2014   18385.4   1027757.0    819474.5 |
  |       乌克兰   2015   16695.0   1027757.0    787001.1 |
  |       乌克兰   2016   20107.6   1027757.0    759888.6 |
  |       乌克兰   2017   23337.9   1027757.0    737633.2 |
  |       乌克兰   2018   26664.9   1027757.0    720040.1 |
  |-------------------------------------------------------|
  | 乌兹别克斯坦   2007    6067.2    276483.2    276483.2 |
  | 乌兹别克斯坦   2008    7784.2    276483.2    267678.4 |
  | 乌兹别克斯坦   2009    9714.7    276483.2    261332.3 |
  | 乌兹别克斯坦   2010   10608.4    276483.2    256260.8 |
  | 乌兹别克斯坦   2011   11446.5    276483.2    252331.6 |
  | 乌兹别克斯坦   2012   12774.3    276483.2    249966.0 |
  | 乌兹别克斯坦   2013   14026.1    276483.2    248994.2 |
  | 乌兹别克斯坦   2014   15551.1    276483.2    249605.6 |
  | 乌兹别克斯坦   2015   17012.9    276483.2    251642.2 |
  | 乌兹别克斯坦   2016   17710.4    276483.2    254254.1 |
  | 乌兹别克斯坦   2017   21146.2    276483.2    260145.0 |
  | 乌兹别克斯坦   2018   24973.7    276483.2    269510.0 |
  +-------------------------------------------------------+

[River Huang]

Dear William, Thanks a lot. It works well.