Dear All, I know that, in general, whether to add prefix such as "xi" does not matter for estimation. However, I was asked a question which adding "xi" or not does alter the results. The data is here:
Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input long id double year long industry double(y x)
 2 2004 63  .756728  .8355138047833333
 2 2005 63  .751015  .6496188354333334
 2 2006 63 1.385951  .7327375125666667
 2 2007 63 1.898914        .8224521987
 2 2008 63  .581806      1.79040523525
 2 2009 63  .842814 1.2762391918083331
 2 2010 63  .419548  .9888817151916668
 2 2011 63  .272164 1.7063635349166666
 2 2012 63  .294346 2.4439829506666664
 2 2013 63  .228051 1.1389735984916667
 2 2014 63   .30241     1.236348807075
 2 2015 63  .442573  1.812866477783333
 2 2016 63  .273578  3.648327967416666
 2 2017 63  .294642  3.638739865583334
 4 2004  .        .                  .
 4 2005  .        .                  .
 4 2006  .        .                  .
 4 2007  .        .                  .
 4 2008  .        .                  .
 4 2009  .        .                  .
 4 2010  .        .                  .
 4 2011  .        .                  .
 4 2012  .        .                  .
 4 2013  .        .                  .
 4 2014  .        .                  .
 4 2015  .        .                  .
 4 2016  .        .                  .
 4 2017  .        .                  .
 5 2004  .        .                  .
 5 2005  .        .                  .
 5 2006  .        .                  .
 5 2007  .        .                  .
 5 2008  .        .                  .
 5 2009  .        .                  .
 5 2010  .        .                  .
 5 2011  .        .                  .
 5 2012  .        .                  .
 5 2013  .        .                  .
 5 2014  .        .                  .
 5 2015  .        .                  .
 5 2016  .        .                  .
 5 2017  .        .                  .
 6 2004 63   .29287  .8355138047833333
 6 2005 63  .505632  .6496188354333334
 6 2006 63 1.798777  .7327375125666667
 6 2007 63 1.198202        .8224521987
 6 2008 63  .422264      1.79040523525
 6 2009 63  .790204 1.2762391918083331
 6 2010 63  .618863  .9888817151916668
 6 2011 63  .488526 1.7063635349166666
 6 2012 63  .682689 2.4439829506666664
 6 2013 63  .669052 1.1389735984916667
 6 2014 63  .809775     1.236348807075
 6 2015 63 1.223162  1.812866477783333
 6 2016 63  .968647  3.648327967416666
 6 2017  .        .  3.638739865583334
 7 2004  .        .                  .
 7 2005  .        .                  .
 7 2006  .        .                  .
 7 2007  .        .                  .
 7 2008  .        .                  .
 7 2009  .        .                  .
 7 2010  .        .                  .
 7 2011  .        .                  .
 7 2012  .        .                  .
 7 2013  .        .                  .
 7 2014  .        .                  .
 7 2015  .        .                  .
 7 2016  .        .                  .
 7 2017  .        .                  .
 8 2004  .        .                  .
 8 2005  .        .                  .
 8 2006  .        .                  .
 8 2007  .        .                  .
 8 2008  .        .                  .
 8 2009  .        .                  .
 8 2010  .        .                  .
 8 2011  .        .                  .
 8 2012  .        .                  .
 8 2013  .        .                  .
 8 2014  .        .                  .
 8 2015  .        .                  .
 8 2016  .        .                  .
 8 2017  .        .                  .
 9 2004 79  .676721  .8355138047833333
 9 2005 79  .467796  .6496188354333334
 9 2006 79   .75409  .7327375125666667
 9 2007 79 2.684242        .8224521987
 9 2008 79  .701192      1.79040523525
 9 2009 79 1.664904 1.2762391918083331
 9 2010 79 1.864633  .9888817151916668
 9 2011 79 1.034766 1.7063635349166666
 9 2012 79   .75898 2.4439829506666664
 9 2013 79  .911308 1.1389735984916667
 9 2014 79 1.398672     1.236348807075
 9 2015 79  1.65417  1.812866477783333
 9 2016 79 1.099003  3.648327967416666
 9 2017 79  .610722  3.638739865583334
10 2004  .        .                  .
10 2005  .        .                  .
10 2006  .        .                  .
10 2007  .        .                  .
10 2008  .        .                  .
10 2009  .        .                  .
10 2010  .        .                  .
10 2011  .        .                  .
10 2012  .        .                  .
10 2013  .        .                  .
10 2014  .        .                  .
10 2015  .        .                  .
10 2016  .        .                  .
10 2017  .        .                  .
11 2004  .        .                  .
11 2005  .        .                  .
11 2006  .        .                  .
11 2007  .        .                  .
11 2008  .        .                  .
11 2009  .        .                  .
11 2010  .        .                  .
11 2011  .        .                  .
11 2012  .        .                  .
11 2013  .        .                  .
11 2014  .        .                  .
11 2015  .        .                  .
11 2016  .        .                  .
11 2017  .        .                  .
12 2004 28 1.032094  .8355138047833333
12 2005 28  .702338  .6496188354333334
12 2006 28 1.422744  .7327375125666667
12 2007 28 2.440828        .8224521987
12 2008 28  .811734      1.79040523525
12 2009 28 1.779759 1.2762391918083331
12 2010 28 2.745457  .9888817151916668
12 2011 28 1.053197 1.7063635349166666
12 2012 28  1.05299 2.4439829506666664
12 2013 28 1.019698 1.1389735984916667
12 2014 28 1.054209     1.236348807075
12 2015 28 1.492859  1.812866477783333
12 2016 28 1.211967  3.648327967416666
12 2017 28  .946029  3.638739865583334
14 2004 63  .430889  .8355138047833333
14 2005 63  .594332  .6496188354333334
14 2006 63  .945943  .7327375125666667
14 2007  .        .        .8224521987
14 2008 63  .890433      1.79040523525
14 2009 63 1.794807 1.2762391918083331
14 2010 63  1.16601  .9888817151916668
14 2011 63  .549908 1.7063635349166666
14 2012 63  .846552 2.4439829506666664
14 2013 63 1.093508 1.1389735984916667
14 2014 63 1.244544     1.236348807075
14 2015 63 2.120411  1.812866477783333
14 2016 63  2.23855  3.648327967416666
14 2017 63 1.423093  3.638739865583334
16 2004 37  .306983  .8355138047833333
16 2005 37  .228051  .6496188354333334
16 2006 37  .232354  .7327375125666667
16 2007 37  .494927        .8224521987
16 2008 37   .29909      1.79040523525
16 2009 37  .559564 1.2762391918083331
16 2010 37  .298449  .9888817151916668
16 2011 37  .228051 1.7063635349166666
16 2012 37  .228051 2.4439829506666664
16 2013 37  .257594 1.1389735984916667
16 2014 37   .35575     1.236348807075
16 2015 37  .983892  1.812866477783333
16 2016 37  .546826  3.648327967416666
16 2017 37  .504983  3.638739865583334
17 2004  .        .                  .
17 2005  .        .                  .
17 2006  .        .                  .
17 2007  .        .                  .
17 2008  .        .                  .
17 2009  .        .                  .
17 2010  .        .                  .
17 2011  .        .                  .
17 2012  .        .                  .
17 2013  .        .                  .
17 2014  .        .                  .
17 2015  .        .                  .
17 2016  .        .                  .
17 2017  .        .                  .
18 2004  .        .                  .
18 2005  .        .                  .
18 2006  .        .                  .
18 2007  .        .                  .
18 2008  .        .                  .
18 2009  .        .                  .
18 2010  .        .                  .
18 2011  .        .                  .
18 2012  .        .                  .
18 2013  .        .                  .
18 2014  .        .                  .
18 2015  .        .                  .
18 2016  .        .                  .
18 2017  .        .                  .
19 2004 14 2.209158  .8355138047833333
19 2005 14 1.927555  .6496188354333334
19 2006 14 2.130961  .7327375125666667
19 2007 14 8.873074        .8224521987
19 2008 14 2.068874      1.79040523525
19 2009 14 3.652264 1.2762391918083331
19 2010 14 3.845202  .9888817151916668
19 2011 14 2.133376 1.7063635349166666
19 2012 14 1.965606 2.4439829506666664
19 2013 14 1.616796 1.1389735984916667
19 2014 14 2.345149     1.236348807075
19 2015 14 5.605507  1.812866477783333
19 2016 14 6.972619  3.648327967416666
19 2017  .        .  3.638739865583334
20 2004  .        .                  .
20 2005  .        .                  .
20 2006  .        .                  .
20 2007  .        .                  .
20 2008  .        .                  .
20 2009  .        .                  .
20 2010  .        .                  .
20 2011  .        .                  .
20 2012  .        .                  .
20 2013  .        .                  .
20 2014  .        .                  .
20 2015  .        .                  .
20 2016  .        .                  .
20 2017  .        .                  .
21 2004 37 1.709002  .8355138047833333
21 2005 37 1.373025  .6496188354333334
21 2006 37 1.841413  .7327375125666667
21 2007 37 2.871234        .8224521987
21 2008 37  .843067      1.79040523525
21 2009 37 2.093308 1.2762391918083331
21 2010 37 2.327557  .9888817151916668
21 2011 37  .658607 1.7063635349166666
21 2012 37  .613905 2.4439829506666664
21 2013 37  .569941 1.1389735984916667
21 2014 37  .738986     1.236348807075
21 2015 37 1.251923  1.812866477783333
21 2016 37 1.200638  3.648327967416666
21 2017 37  .875048  3.638739865583334
22 2004 55 2.963965  .8355138047833333
22 2005 55  2.06641  .6496188354333334
22 2006 55 2.817894  .7327375125666667
22 2007 55 3.534271        .8224521987
22 2008 55 1.347176      1.79040523525
22 2009 55  2.04253 1.2762391918083331
22 2010 55 1.684386  .9888817151916668
22 2011 55 1.012511 1.7063635349166666
22 2012 53 1.085851 2.4439829506666664
22 2013 53 1.478983 1.1389735984916667
22 2014 53 2.123118     1.236348807075
22 2015 53 1.933791  1.812866477783333
end
label values industry industry
label def industry 14 "C15", modify
label def industry 28 "C30", modify
label def industry 37 "C39", modify
label def industry 53 "G55", modify
label def industry 55 "G58", modify
label def industry 63 "K70", modify
label def industry 79 "S90", modify
I estimate the following pairs of regressions:
Code:
xtset id year
tab year, gen(dyear)

// L.x (not OK)
xtreg y L.x i.year i.industry, fe cluster(id)
xi: xtreg y L.x i.year i.industry, fe cluster(id)
You can find their outcomes are different. I believe it is caused by the inclusion of different year dummies. But why is this happening? The following is OK, though.
Code:
// L.x (OK)
xtreg y L.x dyear* i.industry, fe cluster(id)
xi: xtreg y L.x dyear* i.industry, fe cluster(id)