Data disclosure control for regressions

1. Creating artificial data

Create artificial data for eleven IDs (1-11), ten years (2001-2010), four countries (BE, CH, US, GB), and the continuous variables x, y, z.

clear
set obs 301
generate hvcountry = int((_n-1)/100)+1
generate hv        = _n-100*(hvcountry-1)
generate byte id   = int((hv-1)/10)+1
generate int year  = 2000+hv-10*(id-1)
drop hv

generate str2 country = "BE" if hvcountry==1
replace       country = "CH" if hvcountry==2
replace       country = "US" if hvcountry==3
replace       country = "GB" if hvcountry==4
drop hvcountry

set seed 4869382
generate y = runiform()
generate x = runiform()
generate z = runiform()

Creating missing values

Often, yearly dummies are used as catch-all dummies. However, a dummy for the year 2002 would identify the IDs 1 and 2. The coefficient for the 2002 dummy thus causes a disclosure problem.

qui replace x = . if year==2002 & id > 2  
qui replace x = . if _n==301

Creating problematic IDs

Create an ID that must be ignored (since x is missing, see section “Creating missing values”).

qui replace id = 11 if _n==301    

Dummy if id==1. Publishing information on specific firms is not permitted.

generate id1 = id==1         

Creating problematic dummies

Dummy id/year/country: 1/2005/BE, 6/2007/BE. This dummy is 1 for just two distinct IDs. Therefore, it may not be published.

generate dum_2ids   = _n==5|_n==57    

Dummy id/year/country: 1/2005/BE, 6/2007/BE, 7/2003/BE, 8/2008/CH, 10/2009/BE. This dummy is 1 for five distinct IDs. It may be published.

generate dum_5ids = _n==5|_n==57|_n==63|_n==99|_n==178 

Since z has non-zero values for just one ID, this ID is identified by z. Thus, no regression coefficient for z may be published. nobsreg5 creates a dichotomous auxiliary variable that is 0 if z = 0 and 1 if z <> 0. If z = 1 for just one or two IDs, a disclosure problem is reported.

replace z = 0 if id!=1

Creating additional identifiers

Later on, there is an example using two different identifiers. This occurs, for example, if the data includes domestic firms as well as their affiliates abroad. Confidentiality must then be assured for both the parent institutions as well as their affiliates.

generate byte idtest = id                   
replace idtest = 5 if year==2010  

Other

cls
* global endmark "$$"
global endmark "$$" puts $$ after each nobsreg5 output. Using nobsclean.do, 
you can erase this output. 

2. Applying nobsreg5

nobsreg5 with and without global idlist

Set global idlist. Do not forget "".

. global idlist "id"

. 
. regress y x z dum_2ids dum_5ids i.year

      Source |       SS           df       MS      Number of obs   =       276
-------------+----------------------------------   F(13, 262)      =      0.75
       Model |  .820130219        13   .06308694   Prob > F        =    0.7106
    Residual |   21.985146       262  .083912771   R-squared       =    0.0360
-------------+----------------------------------   Adj R-squared   =   -0.0119
       Total |  22.8052762       275  .082928277   Root MSE        =    .28968

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           x |   .0435811   .0624809     0.70   0.486    -.0794476    .1666098
           z |   .0865582   .0879055     0.98   0.326    -.0865329    .2596493
    dum_2ids |   .0727812   .2690551     0.27   0.787    -.4570044    .6025669
    dum_5ids |  -.2767424   .1702277    -1.63   0.105    -.6119309    .0584462
             |
        year |
       2002  |  -.0087476    .132396    -0.07   0.947    -.2694432     .251948
       2003  |  -.0005282   .0753529    -0.01   0.994    -.1489026    .1478462
       2004  |  -.0394107   .0748988    -0.53   0.599     -.186891    .1080696
       2005  |  -.0699613   .0754836    -0.93   0.355    -.2185931    .0786705
       2006  |   .0558791   .0748336     0.75   0.456    -.0914727    .2032309
       2007  |  -.0253635   .0751384    -0.34   0.736    -.1733156    .1225886
       2008  |  -.0021728   .0754425    -0.03   0.977    -.1507235     .146378
       2009  |  -.0016266   .0751321    -0.02   0.983    -.1495662    .1463131
       2010  |  -.0507146   .0750204    -0.68   0.500    -.1984342     .097005
             |
       _cons |    .462225   .0625129     7.39   0.000     .3391334    .5853166
------------------------------------------------------------------------------

. nobsreg5

Number of distinct values for variable  id  :   10

In  regress y x z dum_2ids dum_5ids i.year
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  z  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  dum_2ids  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Too few observations in at least one category
 
           year
  +----------+
  |     2002 |
  +----------+


It does not always suffice to investigate confidentiality for one identifier only. For example, a sample may consist of two MFIs (id) with each having three subsidiaries (idsub) abroad. Confidentiality must then be assured for both the parent institutions and their subsidiaries. nobsreg5 loops through the idlist.

global idlist is preferable if you run several regressions using the same identifiers.

. global idlist "id idtest"

. regress y x z dum_2ids dum_5ids i.year

      Source |       SS           df       MS      Number of obs   =       276
-------------+----------------------------------   F(13, 262)      =      0.75
       Model |  .820130219        13   .06308694   Prob > F        =    0.7106
    Residual |   21.985146       262  .083912771   R-squared       =    0.0360
-------------+----------------------------------   Adj R-squared   =   -0.0119
       Total |  22.8052762       275  .082928277   Root MSE        =    .28968

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           x |   .0435811   .0624809     0.70   0.486    -.0794476    .1666098
           z |   .0865582   .0879055     0.98   0.326    -.0865329    .2596493
    dum_2ids |   .0727812   .2690551     0.27   0.787    -.4570044    .6025669
    dum_5ids |  -.2767424   .1702277    -1.63   0.105    -.6119309    .0584462
             |
        year |
       2002  |  -.0087476    .132396    -0.07   0.947    -.2694432     .251948
       2003  |  -.0005282   .0753529    -0.01   0.994    -.1489026    .1478462
       2004  |  -.0394107   .0748988    -0.53   0.599     -.186891    .1080696
       2005  |  -.0699613   .0754836    -0.93   0.355    -.2185931    .0786705
       2006  |   .0558791   .0748336     0.75   0.456    -.0914727    .2032309
       2007  |  -.0253635   .0751384    -0.34   0.736    -.1733156    .1225886
       2008  |  -.0021728   .0754425    -0.03   0.977    -.1507235     .146378
       2009  |  -.0016266   .0751321    -0.02   0.983    -.1495662    .1463131
       2010  |  -.0507146   .0750204    -0.68   0.500    -.1984342     .097005
             |
       _cons |    .462225   .0625129     7.39   0.000     .3391334    .5853166
------------------------------------------------------------------------------

. nobsreg5

Number of distinct values for variable  id  :   10

In  regress y x z dum_2ids dum_5ids i.year
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  z  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  dum_2ids  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Too few observations in at least one category
 
           year
  +----------+
  |     2002 |
  +----------+


Number of distinct values for variable  idtest  :   10

In  regress y x z dum_2ids dum_5ids i.year
Potential D I S C L O S U R E problem: Number of distinct IDs (idtest) for variable  z  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Number of distinct IDs (idtest) for variable  dum_2ids  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Too few observations in at least one category
 
           year
  +----------+
  |     2002 |
  |     2010 |
  +----------+


Now try the following:

. nobsreg5 id

Number of distinct values for variable  id  :   10

In  regress y x z dum_2ids dum_5ids i.year
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  z  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  dum_2ids  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Too few observations in at least one category
 
           year
  +----------+
  |     2002 |
  +----------+


and again:

. nobsreg5

Number of distinct values for variable  id  :   10

In  regress y x z dum_2ids dum_5ids i.year
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  z  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  dum_2ids  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Too few observations in at least one category
 
           year
  +----------+
  |     2002 |
  +----------+


How to use nobsreg5 with “particular” regressions

. global idlist "id"

nobsreg5 can deal with the bootstrap command.

. bootstrap, reps(10): reg y x z dum_2ids dum_5ids i.year
(running regress on estimation sample)

Bootstrap replications (10)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
....x.....

Linear regression                               Number of obs     =        276
                                                Replications      =          9
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
                                                R-squared         =     0.0360
                                                Adj R-squared     =    -0.0119
                                                Root MSE          =     0.2897

------------------------------------------------------------------------------
             |   Observed   Bootstrap                         Normal-based
           y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           x |   .0435811   .0599359     0.73   0.467    -.0738912    .1610534
           z |   .0865582   .1205661     0.72   0.473     -.149747    .3228634
    dum_2ids |   .0727812   .0849548     0.86   0.392    -.0937271    .2392896
    dum_5ids |  -.2767424   .0427112    -6.48   0.000    -.3604547     -.19303
             |
        year |
       2002  |  -.0087476   .1407761    -0.06   0.950    -.2846636    .2671684
       2003  |  -.0005282   .0605891    -0.01   0.993    -.1192807    .1182243
       2004  |  -.0394107   .0936103    -0.42   0.674    -.2228834    .1440621
       2005  |  -.0699613   .1396316    -0.50   0.616    -.3436341    .2037115
       2006  |   .0558791   .0871374     0.64   0.521     -.114907    .2266652
       2007  |  -.0253635   .0790378    -0.32   0.748    -.1802748    .1295478
       2008  |  -.0021728   .0722558    -0.03   0.976    -.1437915     .139446
       2009  |  -.0016266    .085663    -0.02   0.985     -.169523    .1662699
       2010  |  -.0507146   .0993763    -0.51   0.610    -.2454886    .1440593
             |
       _cons |    .462225   .0708962     6.52   0.000     .3232709     .601179
------------------------------------------------------------------------------
Note: One or more parameters could not be estimated in 1 bootstrap replicate;
      standard-error estimates include only complete replications.

. nobsreg5

For bootstrap, it is better to run an artificial OLS regression with the same variables.
reg y x z dum_2ids dum_5ids i.year

Number of distinct values for variable  id  :   10

In  bootstrap , reps(10): reg y x z dum_2ids dum_5ids i.year
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  z  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  dum_2ids  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Too few observations in at least one category
 
           year
  +----------+
  |     2002 |
  +----------+


However, the last draw can be a bad one. Run an artificial OLS regression.

. nobsreg5

For bootstrap, it is better to run an artificial OLS regression with the same variables.
reg y x z dum_2ids dum_5ids i.year

Number of distinct values for variable  id  :   10

In  bootstrap , reps(10): reg y x z dum_2ids dum_5ids i.year
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  z  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  dum_2ids  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Too few observations in at least one category
 
           year
  +----------+
  |     2002 |
  +----------+


How to obtain results

You are not allowed to show or take with you the results for the variables z, dum_2ids and 2002.year. However, you can run a regression quietly, invoke nobsreg5, store the results, and drop problematic results.

. nobsreg5

For bootstrap, it is better to run an artificial OLS regression with the same variables.
reg y x z dum_2ids dum_5ids i.year

Number of distinct values for variable  id  :   10

In  bootstrap , reps(10): reg y x z dum_2ids dum_5ids i.year
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  z  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  dum_2ids  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Too few observations in at least one category
 
           year
  +----------+
  |     2002 |
  +----------+


. esttab, drop(z dum_2ids 2001.year 2002.year)

----------------------------
                      (1)   
                        y   
----------------------------
x                  0.0436   
                   (0.73)   

dum_5ids           -0.277***
                  (-6.48)   

2003.year       -0.000528   
                  (-0.01)   

2004.year         -0.0394   
                  (-0.42)   

2005.year         -0.0700   
                  (-0.50)   

2006.year          0.0559   
                   (0.64)   

2007.year         -0.0254   
                  (-0.32)   

2008.year        -0.00217   
                  (-0.03)   

2009.year        -0.00163   
                  (-0.02)   

2010.year         -0.0507   
                  (-0.51)   

_cons               0.462***
                   (6.52)   
----------------------------
N                     276   
----------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001

In this example, nobsreg5 indicates a problem with the year 2002. However, it does not provide the coefficient, nor does it show whether the problem refers to one or two units.

You may save the admissible output in a separate file

esttab using ${path_log}/AllowedOutput.tex, drop(z dum_2ids 2001.year 2002.year)

Ask for the do-file so that you know your commands producing this output, and provide the RDSC with the “usual” log-file with the nobsreg5 output, so that the RDSC can see which coefficients must not be disclosed (z dum_2ids 2001.year 2002.year).

Variable lists

It is possible to use locals, and interactions, as well as to uncomment variables. Up to three variables can be interacted at a time.

Countries are coded according to ISO 3166-2, i.e. as two letters.

. sort country

. egen int countrynum = group(country)
. local dummies "dum_2ids dum_5ids"

. regress y x z `dummies' /* z */ year##countrynum

      Source |       SS           df       MS      Number of obs   =       276
-------------+----------------------------------   F(33, 242)      =      0.82
       Model |  2.28939038        33  .069375466   Prob > F        =    0.7505
    Residual |  20.5158858       242  .084776388   R-squared       =    0.1004
-------------+----------------------------------   Adj R-squared   =   -0.0223
       Total |  22.8052762       275  .082928277   Root MSE        =    .29116

---------------------------------------------------------------------------------
              y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
              x |   .0507786   .0672605     0.75   0.451     -.081712    .1832693
              z |    .097698   .0890275     1.10   0.274    -.0776697    .2730656
       dum_2ids |   .0162509   .2807243     0.06   0.954    -.5367241    .5692259
       dum_5ids |  -.2968767   .1775161    -1.67   0.096    -.6465505    .0527971
                |
           year |
          2002  |   .2285682   .2286561     1.00   0.318    -.2218421    .6789784
          2003  |   .0910481    .131418     0.69   0.489     -.167821    .3499172
          2004  |     .09204   .1303598     0.71   0.481    -.1647447    .3488246
          2005  |  -.0592216   .1324586    -0.45   0.655    -.3201405    .2016973
          2006  |   .1067748   .1305599     0.82   0.414     -.150404    .3639536
          2007  |   .1957004   .1322868     1.48   0.140    -.0648802     .456281
          2008  |   .1363839   .1306336     1.04   0.298    -.1209402    .3937079
          2009  |   .0520746   .1314706     0.40   0.692    -.2068983    .3110475
          2010  |  -.0088338   .1304338    -0.07   0.946    -.2657642    .2480967
                |
     countrynum |
             2  |   .0612792   .1303543     0.47   0.639    -.1954946     .318053
             4  |   .0547827   .1304202     0.42   0.675    -.2021211    .3116865
                |
year#countrynum |
        2002 2  |  -.4802541    .319415    -1.50   0.134    -1.109443    .1489345
        2002 4  |  -.2445666   .3199523    -0.76   0.445    -.8748134    .3856803
        2003 2  |  -.0740602   .1851157    -0.40   0.689    -.4387039    .2905836
        2003 4  |  -.1953668   .1857616    -1.05   0.294    -.5612829    .1705493
        2004 2  |  -.1874566   .1846322    -1.02   0.311    -.5511478    .1762346
        2004 4  |  -.2069892   .1846242    -1.12   0.263    -.5706649    .1566864
        2005 2  |  -.0178834   .1866129    -0.10   0.924    -.3854762    .3497095
        2005 4  |  -.0029708   .1857356    -0.02   0.987    -.3688357     .362894
        2006 2  |  -.0821793   .1860313    -0.44   0.659    -.4486266    .2842679
        2006 4  |   -.070639   .1844631    -0.38   0.702    -.4339971    .2927191
        2007 2  |  -.3491716   .1867774    -1.87   0.063    -.7170886    .0187453
        2007 4  |   -.306136   .1854464    -1.65   0.100    -.6714312    .0591591
        2008 2  |  -.1630339   .1856563    -0.88   0.381    -.5287426    .2026747
        2008 4  |  -.2484715   .1844287    -1.35   0.179    -.6117618    .1148189
        2009 2  |  -.0331352   .1850504    -0.18   0.858    -.3976503    .3313798
        2009 4  |   -.124288   .1850043    -0.67   0.502    -.4887123    .2401363
        2010 2  |  -.1843454   .1842928    -1.00   0.318    -.5473681    .1786773
        2010 4  |   .0608013   .1842672     0.33   0.742    -.3021711    .4237737
                |
          _cons |   .4190482   .0984384     4.26   0.000     .2251429    .6129536
---------------------------------------------------------------------------------

A problem with year and the interaction between year and country will be reported because of the year 2002. There is no problem with country.

. nobsreg5 

Number of distinct values for variable  id  :   10

In  regress y x z dum_2ids dum_5ids  year##countrynum
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  z  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  dum_2ids  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Too few observations in at least one category
 
           year
  +----------+
  |     2002 |
  +----------+

Potential D I S C L O S U R E problem: Too few observations in at least one category
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
 
year#countrynum
  +----------+
  |   2002.1 |
  |   2002.2 |
  |   2002.4 |
  +----------+


Requirements for regional variables are stricter than for the others. You must use the regiovar option to mark the regional variable. For the sake of simplicity, we assume “countrynum” to be a regional variable in this example.

. nobsreg5 , regio(countrynum)

Number of distinct values for variable  id  :   10

In  regress y x z dum_2ids dum_5ids  year##countrynum
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  z  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  dum_2ids  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Too few observations in at least one category
 
           year
  +----------+
  |     2002 |
  +----------+

Potential D I S C L O S U R E problem: Too few observations in at least one category
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
 
year#countrynum
  +----------+
  |   2002.1 |
  |   2002.2 |
  |   2002.4 |
  +----------+


The following line is not requested for disclosure control. However, if you are concerned, e.g. with regard to data mining, type:

. mata: nobstable
                      1                 2
     +-------------------------------------+
   1 |         Variable      distinct IDs  |
   2 |         --------      ------------  |
   3 |                x                    |
   4 |              <>0                10  |
   5 |                z                    |
   6 |              <>0                 1  |
   7 |         dum_2ids                    |
   8 |              <>0                 2  |
   9 |         dum_5ids                    |
  10 |              <>0                 5  |
  11 |             year                    |
  12 |             2008                10  |
  13 |             2001                10  |
  14 |             2010                10  |
  15 |             2003                10  |
  16 |             2005                10  |
  17 |             2002                 2  |
  18 |             2006                10  |
  19 |             2004                10  |
  20 |             2009                10  |
  21 |             2007                10  |
  22 |  year#countrynum                    |
  23 |           2008.1                10  |
  24 |           2001.1                10  |
  25 |           2010.1                10  |
  26 |           2003.1                10  |
  27 |           2005.1                10  |
  28 |           2002.1                 2  |
  29 |           2006.1                10  |
  30 |           2004.1                10  |
  31 |           2009.1                10  |
  32 |           2007.1                10  |
  33 |           2003.2                10  |
  34 |           2005.2                10  |
  35 |           2006.2                10  |
  36 |           2009.2                10  |
  37 |           2001.2                10  |
  38 |           2004.2                10  |
  39 |           2010.2                10  |
  40 |           2008.2                10  |
  41 |           2007.2                10  |
  42 |           2002.2                 2  |
  43 |           2010.4                10  |
  44 |           2001.4                10  |
  45 |           2008.4                10  |
  46 |           2003.4                10  |
  47 |           2007.4                10  |
  48 |           2009.4                10  |
  49 |           2006.4                10  |
  50 |           2004.4                10  |
  51 |           2002.4                 2  |
  52 |           2005.4                10  |
  53 |       countrynum                    |
  54 |                1                10  |
  55 |                2                10  |
  56 |                4                10  |
  57 |         --------      ------------  |
  58 |               id                10  |
     +-------------------------------------+

Continuous variables can be interacted with a discrete variable, but you must use c.var.

. generate byte CH = country=="CH"

. regress y x year##CH##c.z

      Source |       SS           df       MS      Number of obs   =       276
-------------+----------------------------------   F(40, 235)      =      0.86
       Model |  2.90941112        40  .072735278   Prob > F        =    0.7115
    Residual |  19.8958651       235  .084663256   R-squared       =    0.1276
-------------+----------------------------------   Adj R-squared   =   -0.0209
       Total |  22.8052762       275  .082928277   Root MSE        =    .29097

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           x |   .0521869   .0686928     0.76   0.448    -.0831455    .1875193
             |
        year |
       2002  |    .237055   .2167751     1.09   0.275    -.1900158    .6641258
       2003  |  -.0189031   .0961201    -0.20   0.844    -.2082704    .1704641
       2004  |   -.018586   .0950574    -0.20   0.845    -.2058596    .1686875
       2005  |  -.0370376   .0977597    -0.38   0.705    -.2296349    .1555598
       2006  |   .0695158   .0967331     0.72   0.473     -.121059    .2600906
       2007  |   .0761099    .095881     0.79   0.428    -.1127862    .2650061
       2008  |   .0487268   .0971863     0.50   0.617    -.1427408    .2401945
       2009  |  -.0196643   .0960063    -0.20   0.838    -.2088074    .1694787
       2010  |   .0481126   .0966253     0.50   0.619    -.1422499    .2384751
             |
        1.CH |   .0697337    .118323     0.59   0.556    -.1633757    .3028431
             |
     year#CH |
     2002 1  |  -.5650312    .376322    -1.50   0.135    -1.306427    .1763646
     2003 1  |  -.0003976    .167322    -0.00   0.998    -.3300402    .3292451
     2004 1  |   -.160744   .1669415    -0.96   0.337    -.4896371    .1681491
     2005 1  |  -.0392905    .169555    -0.23   0.817    -.3733325    .2947515
     2006 1  |  -.0237878   .1699461    -0.14   0.889    -.3586003    .3110248
     2007 1  |  -.2476185   .1684258    -1.47   0.143    -.5794358    .0841989
     2008 1  |  -.1720039   .1685706    -1.02   0.309    -.5041065    .1600988
     2009 1  |  -.0022892    .167367    -0.01   0.989    -.3320205    .3274422
     2010 1  |  -.2408809   .1677869    -1.44   0.152    -.5714395    .0896777
             |
           z |   .3806424   .3560712     1.07   0.286     -.320857    1.082142
             |
    year#c.z |
       2002  |  -.5982049   .5311467    -1.13   0.261    -1.644622    .4482127
       2003  |  -.0156568   .5483244    -0.03   0.977    -1.095916    1.064603
       2004  |   .2075465   .4902415     0.42   0.672    -.7582832    1.173376
       2005  |  -2.596194   2.232448    -1.16   0.246    -6.994363    1.801974
       2006  |  -.1045337    .424694    -0.25   0.806    -.9412276    .7321602
       2007  |  -.8325823   .4702719    -1.77   0.078     -1.75907    .0939052
       2008  |  -.5667757   .4476572    -1.27   0.207     -1.44871    .3151583
       2009  |  -.1210393   .4880464    -0.25   0.804    -1.082544    .8404658
       2010  |  -.4811215    .512228    -0.94   0.349    -1.490267     .528024
             |
      CH#c.z |
          1  |  -.5440143   .5214208    -1.04   0.298    -1.571271    .4832421
             |
 year#CH#c.z |
     2002 1  |   1.020941   .8935243     1.14   0.254    -.7394003    2.781282
     2003 1  |   1.199939   1.970706     0.61   0.543    -2.682568    5.082447
     2004 1  |   5.993481   2.970497     2.02   0.045     .1412749    11.84569
     2005 1  |   2.182839   2.448532     0.89   0.374     -2.64104    7.006717
     2006 1  |   -.390181   .7883598    -0.49   0.621    -1.943337    1.162975
     2007 1  |   1.059644   .6848443     1.55   0.123    -.2895751    2.408862
     2008 1  |   1.356198   .6859395     1.98   0.049     .0048216    2.707574
     2009 1  |   .7494472   .8380016     0.89   0.372    -.9015082    2.400403
     2010 1  |   .3799805   .8227994     0.46   0.645    -1.241025    2.000986
             |
       _cons |   .4309471   .0770523     5.59   0.000     .2791455    .5827486
------------------------------------------------------------------------------

. nobsreg5

Number of distinct values for variable  id  :   10

In regress y x year##CH##c.z
Potential D I S C L O S U R E problem: Too few observations in at least one category
 
           year
  +----------+
  |     2002 |
  +----------+

Potential D I S C L O S U R E problem: Too few observations in at least one category
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
 
        year#CH
  +----------+
  |   2002.0 |
  |   2002.1 |
  +----------+

Potential D I S C L O S U R E problem: Too few observations in at least one category
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
 
         year#z
  +----------+
  |   2002.1 |
  |   2004.1 |
  |   2010.1 |
  |   2007.1 |
  |   2009.1 |
  |   2008.1 |
  |   2005.1 |
  |   2002.0 |
  |   2003.1 |
  |   2006.1 |
  |   2001.1 |
  +----------+

Potential D I S C L O S U R E problem: Too few observations in at least one category
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
 
      year#CH#z
  +----------+
  | 2002.0.1 |
  | 2004.0.1 |
  | 2010.0.1 |
  | 2007.0.1 |
  | 2009.0.1 |
  | 2008.0.1 |
  | 2005.0.1 |
  | 2002.0.0 |
  | 2003.0.1 |
  | 2006.0.1 |
  | 2001.0.1 |
  | 2003.1.1 |
  | 2001.1.1 |
  | 2006.1.1 |
  | 2009.1.1 |
  | 2002.1.0 |
  | 2004.1.1 |
  | 2002.1.1 |
  | 2008.1.1 |
  | 2007.1.1 |
  | 2005.1.1 |
  | 2010.1.1 |
  +----------+

Potential D I S C L O S U R E problem: Too few observations in at least one category
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
 
           CH#z
  +----------+
  |      0.1 |
  |      1.1 |
  +----------+

Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  c.z  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)

nobsreg5 can cope with up to three interactions.

. regress y x year##CH#c.z
note: 1.CH#c.z omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =       276
-------------+----------------------------------   F(21, 254)      =      1.07
       Model |  1.85034719        21  .088111771   Prob > F        =    0.3832
    Residual |   20.954929       254  .082499721   R-squared       =    0.0811
-------------+----------------------------------   Adj R-squared   =    0.0052
       Total |  22.8052762       275  .082928277   Root MSE        =    .28723

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           x |   .0464237   .0628626     0.74   0.461    -.0773747    .1702221
             |
    year#c.z |
       2001  |  -.0862063   .3574403    -0.24   0.810    -.7901305    .6177178
       2002  |  -.3629379   .5633246    -0.64   0.520     -1.47232     .746444
       2003  |  -.1415079   .6317465    -0.22   0.823    -1.385636     1.10262
       2004  |   .1022079   .5890145     0.17   0.862    -1.057766    1.262182
       2005  |  -3.109985   2.121679    -1.47   0.144    -7.288309    1.068338
       2006  |  -.1188201   .5381162    -0.22   0.825    -1.178558    .9409177
       2007  |  -.8288565   .5726093    -1.45   0.149    -1.956523    .2988103
       2008  |  -.5957328   .5549056    -1.07   0.284    -1.688535    .4970692
       2009  |  -.2359632   .5844094    -0.40   0.687    -1.386868     .914942
       2010  |  -.4902259   .6014148    -0.82   0.416    -1.674621    .6941688
             |
      CH#c.z |
          0  |   .4585058   .4917791     0.93   0.352    -.5099781     1.42699
          1  |          0  (omitted)
             |
 year#CH#c.z |
     2002 1  |   .2361845   .7045638     0.34   0.738    -1.151346    1.623715
     2003 1  |   1.428381   1.847468     0.77   0.440    -2.209925    5.066688
     2004 1  |   4.837904    2.78986     1.73   0.084    -.6562989    10.33211
     2005 1  |     2.4953   2.291502     1.09   0.277    -2.017464    7.008064
     2006 1  |   -.346038   .7430528    -0.47   0.642    -1.809367    1.117291
     2007 1  |   .7811659   .6476371     1.21   0.229    -.4942566    2.056588
     2008 1  |   1.151652    .646208     1.78   0.076    -.1209564     2.42426
     2009 1  |   .7710375   .7879046     0.98   0.329    -.7806205    2.322695
     2010 1  |   .0016748   .7724563     0.00   0.998     -1.51956     1.52291
             |
       _cons |    .441288   .0346298    12.74   0.000     .3730898    .5094861
------------------------------------------------------------------------------

. nobsreg5

Number of distinct values for variable  id  :   10

In  regress y x year##CH#c.z
Potential D I S C L O S U R E problem: Too few observations in at least one category
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
 
         year#z
  +----------+
  |   2002.1 |
  |   2004.1 |
  |   2010.1 |
  |   2007.1 |
  |   2009.1 |
  |   2008.1 |
  |   2005.1 |
  |   2002.0 |
  |   2003.1 |
  |   2006.1 |
  |   2001.1 |
  +----------+

Potential D I S C L O S U R E problem: Too few observations in at least one category
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
 
      year#CH#z
  +----------+
  | 2002.0.1 |
  | 2004.0.1 |
  | 2010.0.1 |
  | 2007.0.1 |
  | 2009.0.1 |
  | 2008.0.1 |
  | 2005.0.1 |
  | 2002.0.0 |
  | 2003.0.1 |
  | 2006.0.1 |
  | 2001.0.1 |
  | 2003.1.1 |
  | 2001.1.1 |
  | 2006.1.1 |
  | 2009.1.1 |
  | 2002.1.0 |
  | 2004.1.1 |
  | 2002.1.1 |
  | 2008.1.1 |
  | 2007.1.1 |
  | 2005.1.1 |
  | 2010.1.1 |
  +----------+

Potential D I S C L O S U R E problem: Too few observations in at least one category
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
 
           CH#z
  +----------+
  |      0.1 |
  |      1.1 |
  +----------+


. regress y x year#c.z##CH

      Source |       SS           df       MS      Number of obs   =       276
-------------+----------------------------------   F(22, 253)      =      1.05
       Model |  1.91457073        22  .087025942   Prob > F        =    0.3990
    Residual |  20.8907055       253  .082571958   R-squared       =    0.0840
-------------+----------------------------------   Adj R-squared   =    0.0043
       Total |  22.8052762       275  .082928277   Root MSE        =    .28735

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           x |   .0560967   .0638394     0.88   0.380    -.0696277    .1818212
             |
    year#c.z |
       2001  |   .3515254   .3405131     1.03   0.303    -.3190759    1.022127
       2002  |   .0765202   .2783699     0.27   0.784    -.4716972    .6247376
       2003  |   .3005688   .3959011     0.76   0.448    -.4791127     1.08025
       2004  |   .5437138   .3273452     1.66   0.098    -.1009549    1.188383
       2005  |  -2.785805   2.072463    -1.34   0.180    -6.867283    1.295673
       2006  |   .3306063   .2166022     1.53   0.128    -.0959667    .7571794
       2007  |  -.3816066   .2923599    -1.31   0.193    -.9573758    .1941626
       2008  |  -.1500646   .2572244    -0.58   0.560    -.6566384    .3565091
       2009  |   .2029243   .3188269     0.64   0.525    -.4249685    .8308171
       2010  |  -.0511358   .3473643    -0.15   0.883    -.7352298    .6329582
             |
        1.CH |  -.0346894   .0393338    -0.88   0.379    -.1121528     .042774
             |
 year#CH#c.z |
     2001 1  |  -.4089185   .4951968    -0.83   0.410    -1.384151    .5663145
     2002 1  |   -.175489   .5065401    -0.35   0.729    -1.173061    .8220833
     2003 1  |   1.112452   1.786997     0.62   0.534    -2.406833    4.631736
     2004 1  |   4.595943    2.75703     1.67   0.097    -.8337101     10.0256
     2005 1  |   2.232908   2.251136     0.99   0.322    -2.200445    6.666261
     2006 1  |  -.7568339    .557952    -1.36   0.176    -1.855656    .3419883
     2007 1  |   .3542492   .4203542     0.84   0.400      -.47359    1.182088
     2008 1  |   .7321162   .4210308     1.74   0.083    -.0970555    1.561288
     2009 1  |   .3811371   .6234974     0.61   0.542    -.8467693    1.609043
     2010 1  |  -.3933466   .6016989    -0.65   0.514    -1.578323    .7916301
             |
       _cons |   .4481796   .0355153    12.62   0.000     .3782363    .5181229
------------------------------------------------------------------------------

. nobsreg5

Number of distinct values for variable  id  :   10

In  regress y x year#c.z##CH
Potential D I S C L O S U R E problem: Too few observations in at least one category
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
 
         year#z
  +----------+
  |   2002.1 |
  |   2004.1 |
  |   2010.1 |
  |   2007.1 |
  |   2009.1 |
  |   2008.1 |
  |   2005.1 |
  |   2002.0 |
  |   2003.1 |
  |   2006.1 |
  |   2001.1 |
  +----------+

Potential D I S C L O S U R E problem: Too few observations in at least one category
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
 
      year#z#CH
  +----------+
  | 2002.1.0 |
  | 2004.1.0 |
  | 2010.1.0 |
  | 2007.1.0 |
  | 2009.1.0 |
  | 2008.1.0 |
  | 2005.1.0 |
  | 2002.0.0 |
  | 2003.1.0 |
  | 2006.1.0 |
  | 2001.1.0 |
  | 2003.1.1 |
  | 2001.1.1 |
  | 2006.1.1 |
  | 2009.1.1 |
  | 2002.0.1 |
  | 2004.1.1 |
  | 2002.1.1 |
  | 2008.1.1 |
  | 2007.1.1 |
  | 2005.1.1 |
  | 2010.1.1 |
  +----------+


. regress y x year#c.z#CH

      Source |       SS           df       MS      Number of obs   =       276
-------------+----------------------------------   F(21, 254)      =      1.07
       Model |  1.85034719        21  .088111771   Prob > F        =    0.3832
    Residual |   20.954929       254  .082499721   R-squared       =    0.0811
-------------+----------------------------------   Adj R-squared   =    0.0052
       Total |  22.8052762       275  .082928277   Root MSE        =    .28723

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           x |   .0464237   .0628626     0.74   0.461    -.0773747    .1702221
             |
 year#CH#c.z |
     2001 0  |   .3722995   .3395488     1.10   0.274      -.29639    1.040989
     2001 1  |  -.0862063   .3574403    -0.24   0.810    -.7901305    .6177178
     2002 0  |   .0955679   .2774093     0.34   0.731    -.4507474    .6418833
     2002 1  |  -.1267533   .4240352    -0.30   0.765     -.961826    .7083194
     2003 0  |   .3169979   .3952895     0.80   0.423    -.4614645     1.09546
     2003 1  |   1.286873   1.735267     0.74   0.459    -2.130471    4.704218
     2004 0  |   .5607137   .3266342     1.72   0.087    -.0825426     1.20397
     2004 1  |   4.940112    2.72796     1.81   0.071    -.4321891    10.31241
     2005 0  |   -2.65148   2.065955    -1.28   0.201    -6.720062    1.417103
     2005 1  |  -.6146855   .8842505    -0.70   0.488    -2.356082    1.126711
     2006 0  |   .3396858   .2162627     1.57   0.117    -.0862107    .7655822
     2006 1  |   -.464858   .5117652    -0.91   0.365    -1.472702    .5429855
     2007 0  |  -.3703507   .2919534    -1.27   0.206    -.9453084    .2046071
     2007 1  |  -.0476906   .3000205    -0.16   0.874    -.6385353    .5431541
     2008 0  |   -.137227   .2566998    -0.53   0.593    -.6427582    .3683041
     2008 1  |   .5559188   .3318623     1.68   0.095    -.0976333    1.209471
     2009 0  |   .2225426   .3179107     0.70   0.485    -.4035341    .8486193
     2009 1  |   .5350743   .5303786     1.01   0.314    -.5094255    1.579574
     2010 0  |  -.0317201   .3465143    -0.09   0.927    -.7141272     .650687
     2010 1  |   -.488551   .4882405    -1.00   0.318    -1.450066    .4729642
             |
       _cons |    .441288   .0346298    12.74   0.000     .3730898    .5094861
------------------------------------------------------------------------------

. nobsreg5

Number of distinct values for variable  id  :   10

In  regress y x year#c.z#CH
Potential D I S C L O S U R E problem: Too few observations in at least one category
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
 
      year#z#CH
  +----------+
  | 2002.1.0 |
  | 2004.1.0 |
  | 2010.1.0 |
  | 2007.1.0 |
  | 2009.1.0 |
  | 2008.1.0 |
  | 2005.1.0 |
  | 2002.0.0 |
  | 2003.1.0 |
  | 2006.1.0 |
  | 2001.1.0 |
  | 2003.1.1 |
  | 2001.1.1 |
  | 2006.1.1 |
  | 2009.1.1 |
  | 2002.0.1 |
  | 2004.1.1 |
  | 2002.1.1 |
  | 2008.1.1 |
  | 2007.1.1 |
  | 2005.1.1 |
  | 2010.1.1 |
  +----------+


It is possible to use time series operators.

. egen idcountry = group(id country)

. xtset idcountry year, yearly
       panel variable:  idcountry (unbalanced)
        time variable:  year, 2001 to 2010
                delta:  1 year

. 
. regress y x z dum_2ids dum_5ids id1 i.year l.z, nocons

      Source |       SS           df       MS      Number of obs   =       246
-------------+----------------------------------   F(14, 232)      =     42.92
       Model |  53.2316487        14  3.80226062   Prob > F        =    0.0000
    Residual |  20.5543229       232  .088596219   R-squared       =    0.7214
-------------+----------------------------------   Adj R-squared   =    0.7046
       Total |  73.7859715       246  .299942974   Root MSE        =    .29765

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           x |   .0940636   .0648528     1.45   0.148    -.0337121    .2218393
           z |   .0582237    .193973     0.30   0.764    -.3239501    .4403975
    dum_2ids |   .0417821   .2842199     0.15   0.883    -.5181999    .6017641
    dum_5ids |  -.2679945   .1749558    -1.53   0.127    -.6126998    .0767107
         id1 |   .1223625   .1547827     0.79   0.430    -.1825969     .427322
             |
        year |
       2003  |   .4342809   .0609196     7.13   0.000     .3142546    .5543072
       2004  |   .3849496   .0659386     5.84   0.000     .2550346    .5148646
       2005  |   .3621084   .0616972     5.87   0.000       .24055    .4836668
       2006  |   .4840109   .0642474     7.53   0.000     .3574279    .6105939
       2007  |    .407758   .0638083     6.39   0.000     .2820401    .5334758
       2008  |   .4345588   .0607264     7.16   0.000      .314913    .5542045
       2009  |   .4321782   .0621269     6.96   0.000     .3097733    .5545832
       2010  |   .3829958   .0611187     6.27   0.000     .2625771    .5034144
             |
           z |
         L1. |  -.0701679    .183585    -0.38   0.703    -.4318748    .2915389
------------------------------------------------------------------------------

. nobsreg5

Number of distinct values for variable  id  :   10

In  regress y x z dum_2ids dum_5ids id1 i.year l.z, nocons
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  z  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  dum_2ids  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  id1  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Too few observations in at least one category
 
           year
  +----------+
  |     2002 |
  +----------+

Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  l.z  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)

. 
. regress y x z L(1 2/3).z id1 i.year 

      Source |       SS           df       MS      Number of obs   =       210
-------------+----------------------------------   F(12, 197)      =      0.75
       Model |  .781212335        12  .065101028   Prob > F        =    0.6982
    Residual |  17.0343669       197  .086468867   R-squared       =    0.0438
-------------+----------------------------------   Adj R-squared   =   -0.0144
       Total |  17.8155792       209  .085242006   Root MSE        =    .29406

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           x |  -.0118582   .0730294    -0.16   0.871     -.155878    .1321615
             |
           z |
         --. |   .2783556   .2577276     1.08   0.281    -.2299036    .7866148
         L1. |  -.1803794   .1964853    -0.92   0.360    -.5678639    .2071051
         L2. |   .2068039   .1944684     1.06   0.289    -.1767031    .5903109
         L3. |   .3887897   .2456482     1.58   0.115    -.0956479    .8732274
             |
         id1 |  -.3305759   .2616688    -1.26   0.208    -.8466075    .1854556
             |
        year |
       2005  |  -.0399652    .077074    -0.52   0.605    -.1919613    .1120309
       2006  |   .0962567   .0772795     1.25   0.214    -.0561446     .248658
       2007  |    .026274   .0777735     0.34   0.736    -.1271014    .1796495
       2008  |   .0308281   .0778545     0.40   0.693    -.1227071    .1843633
       2009  |   .0172255   .0772904     0.22   0.824    -.1351973    .1696483
       2010  |   -.025778   .0770466    -0.33   0.738    -.1777199     .126164
             |
       _cons |   .4492557    .068442     6.56   0.000     .3142828    .5842287
------------------------------------------------------------------------------

. nobsreg5

Number of distinct values for variable  id  :   10

In  regress y x z L(1 2/3).z id1 i.year
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  z  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  L1.z  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  L2.z  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  L3.z  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  id1  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)

To interact a lagged variable, it must be created. il.year does not work neither does year##l.x.

. regress y z dum_2ids dum_5ids year##c.x

      Source |       SS           df       MS      Number of obs   =       276
-------------+----------------------------------   F(22, 253)      =      0.75
       Model |  1.40076124        22  .063670965   Prob > F        =    0.7817
    Residual |   21.404515       253  .084602826   R-squared       =    0.0614
-------------+----------------------------------   Adj R-squared   =   -0.0202
       Total |  22.8052762       275  .082928277   Root MSE        =    .29087

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           z |   .0699377   .0898376     0.78   0.437    -.1069872    .2468626
    dum_2ids |   .0449369   .2778334     0.16   0.872    -.5022241    .5920978
    dum_5ids |  -.2519091    .175353    -1.44   0.152    -.5972467    .0934284
             |
        year |
       2002  |  -.0926737   .3330749    -0.28   0.781    -.7486264    .5632789
       2003  |   .0726707   .1497713     0.49   0.628    -.2222866    .3676281
       2004  |   .0829607   .1704256     0.49   0.627    -.2526729    .4185942
       2005  |    .093386   .1525804     0.61   0.541    -.2071035    .3938756
       2006  |   .2812814   .1550312     1.81   0.071    -.0240346    .5865974
       2007  |   .1560635   .1507155     1.04   0.301    -.1407533    .4528803
       2008  |   .1467695   .1478611     0.99   0.322    -.1444259    .4379648
       2009  |   .0894658   .1547932     0.58   0.564    -.2153816    .3943131
       2010  |  -.0561554   .1485676    -0.38   0.706     -.348742    .2364313
             |
           x |   .2517252   .1876391     1.34   0.181    -.1178084    .6212589
             |
    year#c.x |
       2002  |   .0916317   .4782613     0.19   0.848    -.8502489    1.033512
       2003  |  -.1224754     .27339    -0.45   0.655    -.6608855    .4159347
       2004  |  -.2303776   .2755673    -0.84   0.404    -.7730758    .3123205
       2005  |  -.3349556   .2817105    -1.19   0.236     -.889752    .2198408
       2006  |  -.4368112   .2622678    -1.67   0.097    -.9533173     .079695
       2007  |  -.3496271   .2507404    -1.39   0.164    -.8434314    .1441771
       2008  |  -.3065189   .2749106    -1.11   0.266    -.8479238    .2348859
       2009  |  -.1687405   .2739293    -0.62   0.538    -.7082128    .3707318
       2010  |   .0569397   .2645757     0.22   0.830    -.4641117    .5779911
             |
       _cons |   .3532284   .1124216     3.14   0.002     .1318271    .5746297
------------------------------------------------------------------------------

. nobsreg5

Number of distinct values for variable  id  :   10

In  regress y z dum_2ids dum_5ids year##c.x
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  z  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Number of distinct IDs (id) for variable  dum_2ids  too small
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
Potential D I S C L O S U R E problem: Too few observations in at least one category
 
           year
  +----------+
  |     2002 |
  +----------+

Potential D I S C L O S U R E problem: Too few observations in at least one category
(in case of a continuous variable x: 1 means x!=0 & x!=.  and  0 means x==0)
 
         year#x
  +----------+
  |   2002.1 |
  +----------+