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 | 开心 2020-8-31 13:48:05 |
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签到天数: 37 天 连续签到: 1 天 [LV.5]常住居民I
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各位大侠、各位老师:
请问下,如何通过stata命令将以下数据转化为stata能识别的面板数据呢?sum表示全国,bj表示北京,sh表示上海。我想将这三个变量统一放到id变量名下(新生成一个id变量),通过date 以及id 来设定面板数据。GDP相当于Y,HY和LK相当于X。敬请指教。
- * Example generated by -dataex-. To install: ssc install dataex
- clear
- input int date str3 var double(sum bj sh)
- 2002 "HY" 121717.3984375 4315 5741.02978515625
- 2003 "HY" 137422 5007.2099609375 6694.22998046875
- 2004 "HY" 161840.203125 6033.2099609375 8072.830078125
- 2005 "HY" 187318.90625 6969.52001953125 9247.66015625
- 2006 "HY" 219438.5 8117.77978515625 10572.240234375
- 2007 "HY" 270092.3125 9846.8095703125 12494.009765625
- 2008 "HY" 319244.59375 11115 14069.8701171875
- 2009 "HY" 348517.6875 12153.0302734375 15046.4501953125
- 2010 "HY" 412119.3125 14113.580078125 17165.98046875
- 2011 "HY" 487940.1875 16251.9296875 19195.689453125
- 2012 "HY" 538580 17879.400390625 20181.720703125
- 2013 "HY" 592963.1875 19500.599609375 21602.099609375
- 2014 "HY" 641280.625 21330.830078125 23567.69921875
- 2015 "HY" 685992.875 23014.58984375 25123.44921875
- 2016 "HY" 740060.8125 25669.130859375 28178.650390625
- 2017 "HY" 820754.3125 28014.939453125 30632.990234375
- 2018 "HY" 900309.5 30319.98046875 32679.869140625
- 2002 "LK" 4018340.75 629045.1875 1074870.375
- 2003 "LK" 4517440.5 662746.1875 1397827.25
- 2004 "LK" 5525765 668690.3125 1936196.09375
- 2005 "LK" 6330842.5 782172 2216714.25
- 2006 "LK" 7531935 1203957.6999511719 2531653.15625
- 2007 "LK" 8609195 1424208.75 2947910.34375
- 2008 "LK" 8833590 1380953.25 3018753.3125
- 2009 "LK" 9455645 1488184.75 2982465.40625
- 2010 "LK" 11289872 1568947.92578125 3708518.84375
- 2011 "LK" 11577677 1663789.05078125 3539337.15625
- 2012 "LK" 11993971 1829917.75 3367970.90625
- 2013 "LK" 12585175 1880773.0234375 3363642.90625
- 2014 "LK" 13560841 1885501.3984375 3613830.40625
- 2015 "LK" 14094003 1926195.1015625 3708831.09375
- 2016 "LK" 15104057 1969794.25 3869187.25
- 2017 "LK" 16177345 2052788.525390625 4231741.09375
- 2018 "LK" 16740229 2099127.57421875 4175727.09375
- 2002 "GDP" 171373468 27363430 24714789
- 2003 "GDP" 174324727 24283818 24756008
- 2004 "GDP" 241934678 34883190 35910921
- 2005 "GDP" 284351063 41031321 41462332
- 2006 "GDP" 331973261 49049343 46125103
- 2007 "GDP" 387645906 54365162 51553394
- 2008 "GDP" 405762104 57295174 51113095
- 2009 "GDP" 486063435 66982690 56999557
- 2010 "GDP" 564312300 76088588 71877433
- 2011 "GDP" 620536534 81319111 74560172
- 2012 "GDP" 679772088 85389239 78708890
- 2013 "GDP" 754308682 88167618 82789492
- 2014 "GDP" 831533051 91057554 89659029
- 2015 "GDP" 914773311 95204250 99188938
- 2016 "GDP" 1016357068 99979842 106462549
- 2017 "GDP" 1147866788 101740179 111885296
- 2018 "GDP" 1264688737 107496030 117634335
- end
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