stata数据处理

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求助，现在想根据fixedasset_origin推算之前的值，其中fixedasset_origin的增长率为rate，比如rate1994表示1993到1994年的增长率。难点在于需要用fixedasset_origin第一次出现的值作为原值倒推，将之前年份的缺失值补充完整。每个fimid第一次出现值的年份有的是2007，有的是2006。目前我将fixedasset_origin第一次出现的值给提取出来了，保存为gd.如何用stata命令推算出之前的值呢，请各位老师赐教。

1. * Example generated by -dataex-. To install: ssc install dataex
   
2. clear
   
3. input double firmid float(year gd) long fixedasset_origin float(rate1994 rate1995 rate1996 rate1997 rate1998 rate1999 rate2000 rate2001 rate2002 rate2003 rate2004 rate2005 rate2006 rate2007)
   
4. 1910155010 1993 11976     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
   
5. 1910155010 1994 11976     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
   
6. 1910155010 1995 11976     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
   
7. 1910155010 1996 11976     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
   
8. 1910155010 1997 11976     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
   
9. 1910155010 1998 11976     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
   
10. 1910155010 1999 11976     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
11. 1910155010 2000 11976     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
12. 1910155010 2001 11976     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
13. 1910155010 2002 11976     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
14. 1910155010 2003 11976     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
15. 1910155010 2004 11976     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
16. 1910155010 2005 11976     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
17. 1910155010 2006 11976     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
18. 1910155010 2007 11976 11976 .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
19. 1979848555 1993  4210     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
20. 1979848555 1994  4210     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
21. 1979848555 1995  4210     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
22. 1979848555 1996  4210     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
23. 1979848555 1997  4210     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
24. 1979848555 1998  4210     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
25. 1979848555 1999  4210     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
26. 1979848555 2000  4210     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
27. 1979848555 2001  4210     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
28. 1979848555 2002  4210     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
29. 1979848555 2003  4210     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
30. 1979848555 2004  4210     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
31. 1979848555 2005  4210     . .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
32. 1979848555 2006  4210  4210 .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
33. 1979848555 2007  4210  5876 .3076078 .3357356 .25134882 .09840947 .01497485 .01475391 -.008327703 .018277558 .0556211 .10878377 .1851565 .15622957 .18784995 .2175858
    
34. end

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关键词：stata数据处理 STATA数据 Stata tata 数据处理

你的第 29, 30 笔资料的关系不对吧？ 4210 到 5876 之增长率不是0.2175858？

黃河泉 发表于 2021-2-18 10:08
你的第 29, 30 笔资料的关系不对吧？ 4210 到 5876 之增长率不是0.2175858？

黄老师好，由于构造的增长率是根据其他方法补充的，并非依据fixedasset本身算的。所以只将缺失的数据按照这个增长率补充，本身存在的不需要按照这个增长率补充。

Txwr 发表于 2021-2-18 12:09
黄老师好，由于构造的增长率是根据其他方法补充的，并非依据fixedasset本身算的。所以只将缺失的数据按照 ...

若有其他情况 (上述已有两个情况)，请分别类出 (不然到时候答案会有错)，我帮你去问问！

黃河泉 发表于 2021-2-18 15:39
若有其他情况 (上述已有两个情况)，请分别类出 (不然到时候答案会有错)，我帮你去问问！

感谢黄老师，目前就这两种情况

Txwr 发表于 2021-2-18 22:40
感谢黄老师，目前就这两种情况

不知什么原因，我最近一直无法上 Stata Forum，暂时无法帮你提问。

Txwr 发表于 2021-2-18 22:40
感谢黄老师，目前就这两种情况

我自己试了一下，看看是不是你要的？

1. preserve
   
2. keep rate*
   
3. gen n = _n
   
4. reshape long rate, i(n) j(year)
   
5. keep if n==1
   
6. drop n
   
7. tempfile tem
   
8. save `tem'
   
9. restore
   
10. 
    
11. drop rate*
    
12. merge m:1 year using `tem'
    
13. sort firmid year
    
14. 
    
15. levelsof firmid, local(levels)
    
16. foreach i of local levels {
    
17.         dis `i'
    
18.         forvalues j=2007(-1)1993 {
    
19.                 dis `j'
    
20.                 replace fixedasset_origin = fixedasset_origin[_n+1]/(1+rate[_n+1]) if firmid == `i' & year == `j' & fixedasset_origin == .
    
21.         }
    
22. }

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黃河泉 发表于 2021-2-19 10:11
不知什么原因，我最近一直无法上 Stata Forum，暂时无法帮你提问。

黄老师，原来您那边也登不上Statalist啊，我还以为只有大陆这边登不上呢

zdlspace 发表于 2021-2-19 10:50
黄老师，原来您那边也登不上Statalist啊，我还以为只有大陆这边登不上呢

是的，好几天了！

1. reshape long rate,i(firmid year) j(time)
   
2. keep if year==time
   
3. 
   
4. gsort +firmid -year
   
5. 
   
6. bysort firmid: replace fixedasset_origin=fixedasset_origin[_n-1]/(1+rate[_n-1]) if  fixedasset_origin==. & _n>=2

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