xtset an tsset

[Timea De Wispelaere]

Hi!

For the construction of a certain variable, I have to do following calculation:

generate capitalexpenditure = (c21_28-L.c21_28+c630)/c20_58

It is a dataset which includes various financial items over the years 2009-2018 for the firms in my sample, and I made it long. I am now wondering if I should use the xtset command or the tsset command to calculate this variable.

thank you very much,
Timea De Wispelaere

[Nick Cox]

Wrong way round. You can't even refer to lagged values unless you have xtset or tsset the data successfully. In this case a firm identifier and a time (?year) variable are both required,

xtset and tsset commands can't calculate anything for you. They just -- in this context -- allow you to use a time series operator. and ensure that separate panels are treated separately.

[Timea De Wispelaere]

I expressed myself in the wrong way. I mean, before being able to calculate the capitalexpenditure variable, I should let stata know he is working with a time series. I am now wondering, should I use tsset or xtset? Because I don't understand the difference.

[Carlo Lazzaro]

Timea:
there's no difference for what you're after (panel data regerssion):

. use "https://www.stata-press.com/data/r16/nlswork.dta"
(National Longitudinal Survey.  Young Women 14-26 years of age in 1968)

. tsset idcode year
       panel variable:  idcode (unbalanced)
        time variable:  year, 68 to 88, but with gaps
                delta:  1 unit

. xtreg ln_wage age

Random-effects GLS regression                   Number of obs     =     28,510
Group variable: idcode                          Number of groups  =      4,710

R-sq:                                           Obs per group:
     within  = 0.1026                                         min =          1
     between = 0.0877                                         avg =        6.1
     overall = 0.0774                                         max =         15

                                                Wald chi2(1)      =    3140.35
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

------------------------------------------------------------------------------
     ln_wage |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         age |   .0185667   .0003313    56.04   0.000     .0179174    .0192161
       _cons |   1.120439   .0112038   100.01   0.000      1.09848    1.142398
-------------+----------------------------------------------------------------
     sigma_u |  .36972456
     sigma_e |  .30349389
         rho |  .59743613   (fraction of variance due to u_i)
------------------------------------------------------------------------------

. xtset idcode year
       panel variable:  idcode (unbalanced)
        time variable:  year, 68 to 88, but with gaps
                delta:  1 unit

. xtreg ln_wage age

Random-effects GLS regression                   Number of obs     =     28,510
Group variable: idcode                          Number of groups  =      4,710

R-sq:                                           Obs per group:
     within  = 0.1026                                         min =          1
     between = 0.0877                                         avg =        6.1
     overall = 0.0774                                         max =         15

                                                Wald chi2(1)      =    3140.35
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

------------------------------------------------------------------------------
     ln_wage |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         age |   .0185667   .0003313    56.04   0.000     .0179174    .0192161
       _cons |   1.120439   .0112038   100.01   0.000      1.09848    1.142398
-------------+----------------------------------------------------------------
     sigma_u |  .36972456
     sigma_e |  .30349389
         rho |  .59743613   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.

That said, I would go -xtset-.

[Timea De Wispelaere]

thank you both! This was very helpful.