Dear Stata-listers,
Iam currently analysing impact of access to microfinance institution to consumption smoothing during health shock. My data is in panel for year 2007 and 2014. For dependent variable, i use delta log consumption and independent variable is interaction betweet health shock dummy and distance dummy (i divided distance dummy into three independent variables for 0-1km, 1-10km, >10km)

My data
Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float(delta_cons distance_1km distance_10km distance_more10km)
 12.720392 0 0 1
 12.720392 0 0 1
 12.407311 0 0 1
 12.407311 0 0 1
 -12.83119 0 0 1
 -12.83119 0 0 1
 13.577685 0 0 1
 13.577685 0 0 1
-12.599562 0 0 1
-12.599562 0 0 1
 -13.65112 0 0 1
 -13.65112 0 0 1
  13.66916 0 0 1
  13.66916 0 0 1
 12.526948 0 0 1
 12.526948 0 0 1
         0 0 0 1
         0 0 0 1
-12.231578 0 0 1
-12.231578 0 0 1
 12.964393 0 0 1
 12.964393 0 0 1
-12.174324 0 0 1
-12.174324 0 0 1
 -10.23063 0 0 1
 -10.23063 0 0 1
 13.003392 0 0 1
 13.003392 0 0 1
 12.570395 0 0 1
 12.570395 0 0 1
 13.041876 0 0 1
 13.041876 0 0 1
 -12.92658 0 0 1
 -12.92658 0 0 1
 13.706965 0 0 1
 13.706965 0 0 1
 13.080065 0 0 1
 13.080065 0 0 1
 13.092076 0 0 1
 13.092076 0 0 1
 12.846226 0 0 1
 12.846226 0 0 1
 13.610765 0 0 1
 13.610765 0 0 1
  13.02033 0 0 1
  13.02033 0 0 1
-12.239265 0 0 1
-12.239265 0 0 1
  14.36122 0 0 1
  14.36122 0 0 1
 14.187793 0 0 1
 14.187793 0 0 1
 13.305684 0 0 1
 13.305684 0 0 1
 13.646796 0 0 1
 13.646796 0 0 1
 13.288548 0 0 1
 13.288548 0 0 1
-12.888692 0 0 1
-12.888692 0 0 1
         0 0 0 1
         0 0 0 1
  14.01054 0 0 1
  14.01054 0 0 1
 14.759644 0 0 1
 14.759644 0 0 1
 14.191398 0 0 1
 14.191398 0 0 1
 13.070982 0 0 1
 13.070982 0 0 1
 13.008448 0 0 1
 13.008448 0 0 1
 13.489578 0 0 1
 13.489578 0 0 1
         0 0 0 1
         0 0 0 1
-12.601674 0 0 1
-12.601674 0 0 1
-12.182975 0 0 1
-12.182975 0 0 1
 13.390735 0 0 1
 13.390735 0 0 1
 13.939254 0 0 1
 13.939254 0 0 1
 12.820853 0 0 1
 12.820853 0 0 1
 13.465385 0 0 1
 13.465385 0 0 1
  13.58884 0 0 1
  13.58884 0 0 1
  12.75724 0 0 1
  12.75724 0 0 1
 13.091675 0 0 1
 13.091675 0 0 1
  13.73929 0 0 1
  13.73929 0 0 1
 12.679196 0 0 1
 12.679196 0 0 1
 14.264695 0 0 1
 14.264695 0 0 1
end
I run regression using FE and RE, then i got this result
Code:
xtreg delta_cons i.distance_1km##i.adl i.distance_10km##i.adl i.distance_more10km##i.adl, re
HTML Code:
note: 1.distance_more10km omitted because of collinearity
note: 1.distance_more10km#1.adl omitted because of collinearity

Random-effects GLS regression                   Number of obs     =     12,270
Group variable: pidlinks                        Number of groups  =      6,135

R-sq:                                           Obs per group:
     within  = 0.0000                                         min =          2
     between = 0.0000                                         avg =        2.0
     overall = 0.0000                                         max =          2

                                                Wald chi2(0)      =          .
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =          .

---------------------------------------------------------------------------------------
           delta_cons |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
       1.distance_1km |          0  (omitted)
                1.adl |          0  (omitted)
                      |
     distance_1km#adl |
                 1 1  |          0  (omitted)
                      |
      1.distance_10km |          0  (omitted)
                      |
    distance_10km#adl |
                 1 1  |          0  (omitted)
                      |
  1.distance_more10km |          0  (omitted)
                      |
distance_more10km#adl |
                 1 1  |          0  (omitted)
                      |
                _cons |          0  (omitted)
----------------------+----------------------------------------------------------------
              sigma_u |  11.058928
              sigma_e |          0
                  rho |          1   (fraction of variance due to u_i)
---------------------------------------------------------------------------------------

.
The same omitted result happen with FE command.
Then i tried to use pooled least square to analyse my data
Code:
reg delta_cons i.distance_1km##i.adl i.distance_10km##i.adl i.distance_more10km##i.adl
HTML Code:
note: 1.distance_more10km omitted because of collinearity
note: 1.distance_more10km#1.adl omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =    12,270
-------------+----------------------------------   F(5, 12264)     =    347.26
       Model |  212248.045         5   42449.609   Prob > F        =    0.0000
    Residual |  1499151.93    12,264  122.240046   R-squared       =    0.1240
-------------+----------------------------------   Adj R-squared   =    0.1237
       Total |  1711399.97    12,269  139.489769   Root MSE        =    11.056

---------------------------------------------------------------------------------------
           delta_cons |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
       1.distance_1km |   1.188385   .6092312     1.95   0.051    -.0058037    2.382575
                1.adl |    9.74406   .2532389    38.48   0.000     9.247671    10.24045
                      |
     distance_1km#adl |
                 1 1  |  -1.330926   1.071027    -1.24   0.214    -3.430307    .7684549
                      |
      1.distance_10km |   .6568737    .351311     1.87   0.062    -.0317511    1.345499
                      |
    distance_10km#adl |
                 1 1  |  -.8004681   .7137334    -1.12   0.262    -2.199498    .5985616
                      |
  1.distance_more10km |          0  (omitted)
                      |
distance_more10km#adl |
                 1 1  |          0  (omitted)
                      |
                _cons |   3.212224   .1257526    25.54   0.000     2.965729    3.458719
---------------------------------------------------------------------------------------

.
I could not understand why most of my variables are omitted in the regression result. How to fix this problem and run the right regression?