Saving residuals in panel regression

[Jay Jeon]

Hi everyone

I have a question about saving the residuals in panel data.

Features of my dataset are as follows:
1) # of panel: 300
2) time id: 1 to 400

example of the dataset can be shown as

company_id	time_id	weekly_sales
1	1	100
1	2	150
1	3	170
1	4	120
1	5	125

Let's say there is an equation:

weekly_sales (i,t) = a + b*weekly_sales(i,t-1) + resid

What I want to acquire is the residuals of the equation above.

I tried the code below, but I think the result is not what I was looking for.

------
gen resid_weekly_sales=.
levelsof(company_id), local(local_company_id)

foreach id of local local_company_id {

regress weekly_sales L.weekly_sales if company_id==`id'
predict r if company_id==`id', resid
replace resid_weekly_sales = r if company_id==`id'

drop r
}

------
I think the reason why the results are wrong is that I did not account for time_id in the coding.

I would appreciate it if someone can help me figure out the solution.

Thank you.

[Carlo Lazzaro]

Jay:
welcome to this forum.
Can't you simply use -predict-?:

. use "https://www.stata-press.com/data/r17/nlswork.dta"
(National Longitudinal Survey of Young Women, 14-24 years old in 1968)

. xtreg ln_wage c.age##c.age, fe vce(cluster idcode)

Fixed-effects (within) regression               Number of obs     =     28,510
Group variable: idcode                          Number of groups  =      4,710

R-squared:                                      Obs per group:
     Within  = 0.1087                                         min =          1
     Between = 0.1006                                         avg =        6.1
     Overall = 0.0865                                         max =         15

                                                F(2,4709)         =     507.42
corr(u_i, Xb) = 0.0440                          Prob > F          =     0.0000

                             (Std. err. adjusted for 4,710 clusters in idcode)
------------------------------------------------------------------------------
             |               Robust
     ln_wage | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         age |   .0539076    .004307    12.52   0.000     .0454638    .0623515
             |
 c.age#c.age |  -.0005973    .000072    -8.30   0.000    -.0007384   -.0004562
             |
       _cons |    .639913   .0624195    10.25   0.000     .5175415    .7622845
-------------+----------------------------------------------------------------
     sigma_u |   .4039153
     sigma_e |  .30245467
         rho |  .64073314   (fraction of variance due to u_i)
------------------------------------------------------------------------------

. predict Residual_epsilon, e
(24 missing values generated)

. list idcode Residual_epsilon in 1/10

     +--------------------+
     | idcode   Residua~n |
     |--------------------|
  1. |      1   -.3836936 |
  2. |      1   -.8380948 |
  3. |      1   -.3073495 |
  4. |      1   -.1464718 |
  5. |      1   -.2049846 |
     |--------------------|
  6. |      1   -.2537888 |
  7. |      1     .438062 |
  8. |      1    .4524961 |
  9. |      1    .2650439 |
 10. |      1     .427596 |
     +--------------------+

.