Hi i am running a tobit regresison for data across 2 years 2007-2008 ( they were 2 individual datasets which i appended to make one overall dataset):
My variables include 10 log price categories for alcohol types on trade and off trade : l_p_wine_on l_p_wine_off etc
I also have a log income variable : log_income
My dependent variables are the expenditure shares of the alcohol type expenditure divided by total expenditure : e.g exp_share_wine_on expshare_wine_off
I am looking at the price elasticities of demand and the cross price elasticities of demand vary across each alcohol type and vary across socio-economic groups, government regions and gender

My prices for alcohols are constant throughout the year (i am using the average year price) however they vary between years

here is a data-ex for some of my variables
Code:
: 
  Example generated by -dataex-. To install: ssc install dataex clear input float(l_p_wine_on l_p_beer_on l_p_spirits_on l_p_wine_off l_p_spirits_off >  l_p_beer_off expshare_wine_on expshare_beer_off logincome) byte(socio_group  > gor) int year byte sexhrp .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  4.433789 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0   .01142119  >  5.898746 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  4.898213 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584    .0550356  .015000853  >  6.399842 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0 .0016348386  >  5.584012 3 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0  .015073973  >  7.020905 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  5.225338 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  4.911331 3 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  5.219934 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  5.533279 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  > 4.2492094 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0   .00609936  >  6.168564 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  4.835587 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  5.940566 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0  .006249688  >  5.331317 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  5.786775 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0  .003858888  >  7.201894 2 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  6.476967 2 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  5.009435 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0  .010382757  >  6.377679 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  5.982862 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >   6.11283 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0 .0023888294  >  6.279646 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0  .001813489  >  6.294915 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0  .005435922  >  6.704463 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  6.747566 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0  .005957043  >   6.11456 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584  .014408222  .016718158  >  6.605068 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584  .018981254           0  >  6.019785 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  6.088818 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  4.779476 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0  .008590408  >  6.514719 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584  .005628793  .018012136  >  6.960443 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0  .005657709  >  6.424075 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  5.920457 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584  .008473212           0  >  6.898255 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0   .02177079  >  5.623837 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  4.812526 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  6.182973 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  4.514611 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  6.109314 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  5.362559 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >   5.30903 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >   5.26414 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  > 4.3593974 3 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584  .017695729           0  >   4.77104 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  5.069847 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0   .01336186  >  6.690271 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >   5.80408 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  6.628306 5 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0  .016276948  >  6.522627 5 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  5.519619 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0  .035966147  >  6.422951 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  6.557673 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  6.602438 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  6.402017 3 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .0029820926           0  >  7.401286 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0  .015186014  >  7.176426 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  5.746554 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  5.474176 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0   .01607261  >  6.874416 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0  .010095213  >  6.662046 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584  .024986824    .0423164  >  6.069906 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584  .001250104           0  >  7.438652 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584   .03693495           0  >  7.021414 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0   .02268917  >    6.5658 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  6.958667 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  6.192117 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .0016070686           0  >  5.815264 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >   5.34921 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >     6.279 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  5.516609 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  5.554516 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  6.347932 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584   .01880577           0  >   5.93925 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0   .07133046  >  6.985651 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584  .005728897  .005415315  >   7.12227 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584   .01053234  .021376746  >    6.8088 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .0019776237           0  >  6.519822 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  6.490757 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  6.787439 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  6.457868 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  6.921752 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  7.098411 2 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  4.400603 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584  .013181653           0  >  6.857086 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584  .003744323           0  >  6.710182 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  6.136498 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584   .02200635           0  >  7.438652 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .0019496685           0  >  6.911319 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0 .0006786454  >  6.854755 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  7.438652 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  6.609726 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584  .004789272           0  >  6.868133 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  5.182907 4 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  5.823194 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  4.812526 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0   .04808098  >  5.530222 4 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0           0  >  5.793585 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584           0   .11235794  >  6.436151 1 2 2007 2 end label values gor gor label def gor 2 "north west", modify label values sexhrp sexhrp label def sexhrp 1 "male", modify label def sexhrp 2 "female", modify
I am then running a tobit regression as follows:

Code:
tobit expshare_wine_on l_p_wine_on l_p_beer_on l_p_cider_on l_p_spirits_on l_p_alcopops_on l_p_wine_off l_p_beer_off l_p_spirits_off l_p_cider_off l_p_alcopops_off logincome i.socio_group i.gor i.year i.sexhrp , ll(0)
I have censored the data at zero since some households report no consumption of alcohol

However my results are as follows:

Code:
tobit expshare_wine_on l_p_wine_on l_p_beer_on l_p_cider_on l_p_spirits_on l_p_alcopops_on l_p_wine_off > l_p_beer_off l_p_spirits_off l_p_cider_off l_p_alcopops_off logincome i.socio_group i.gor i.year i.sex > hrp , ll(0) note: l_p_beer_on omitted because of collinearity note: l_p_cider_on omitted because of collinearity note: l_p_spirits_on omitted because of collinearity note: l_p_alcopops_on omitted because of collinearity note: l_p_wine_off omitted because of collinearity note: l_p_beer_off omitted because of collinearity note: l_p_spirits_off omitted because of collinearity note: l_p_cider_off omitted because of collinearity note: l_p_alcopops_off omitted because of collinearity note: 2008.year omitted because of collinearity Refining starting values: Grid node 0: log likelihood = -5976.9775 Fitting full model: Iteration 0: log likelihood = -5976.9775 Iteration 1: log likelihood = -640.92644 Iteration 2: log likelihood = 1103.185 Iteration 3: log likelihood = 1808.8673 Iteration 4: log likelihood = 1909.2432 Iteration 5: log likelihood = 1910.562 Iteration 6: log likelihood = 1910.5625 Iteration 7: log likelihood = 1910.5625 Tobit regression Number of obs = 11,962 Uncensored = 2,312 Limits: lower = 0 Left-censored = 9,650 upper = +inf Right-censored = 0 LR chi2(14) = 927.97 Prob > chi2 = 0.0000 Log likelihood = 1910.5625 Pseudo R2 = -0.3207 ------------------------------------------------------------------------------------------- expshare_wine_on | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------------------+---------------------------------------------------------------- l_p_wine_on | -.028118 .0162136 -1.73 0.083 -.0598992 .0036632 l_p_beer_on | 0 (omitted) l_p_cider_on | 0 (omitted) l_p_spirits_on | 0 (omitted) l_p_alcopops_on | 0 (omitted) l_p_wine_off | 0 (omitted) l_p_beer_off | 0 (omitted) l_p_spirits_off | 0 (omitted) l_p_cider_off | 0 (omitted) l_p_alcopops_off | 0 (omitted) logincome | .0125922 .0006706 18.78 0.000 .0112778 .0139066 | socio_group | 2 | .0014811 .0010997 1.35 0.178 -.0006745 .0036368 3 | -.0078991 .0012672 -6.23 0.000 -.0103829 -.0054152 4 | -.0098159 .003836 -2.56 0.011 -.0173351 -.0022968 5 | .0065436 .0035439 1.85 0.065 -.0004031 .0134903 6 | -.0027114 .0010429 -2.60 0.009 -.0047556 -.0006672 | gor | north west | -.0004291 .0014892 -0.29 0.773 -.0033481 .00249 merseyside | -.0009579 .0014654 -0.65 0.513 -.0038303 .0019145 yorkshire and the humber | .0017352 .0014609 1.19 0.235 -.0011284 .0045987 east midlands | -.0012567 .0021903 -0.57 0.566 -.00555 .0030366 west midlands | -.0024181 .0018331 -1.32 0.187 -.0060112 .0011751 eastern | -.0016493 .0017609 -0.94 0.349 -.005101 .0018023 | year | 2008 | 0 (omitted) | sexhrp | female | .0011694 .000803 1.46 0.145 -.0004046 .0027435 _cons | -.0853166 .0110156 -7.75 0.000 -.106909 -.0637242 --------------------------+---------------------------------------------------------------- var(e.expshare_wine_on)| .0007865 .0000268 .0007357 .0008408 ------------------------------------------------------------------------------------------
Q1. Why are my price variables other than the price of the same dependent variable omitted (as i am trying to work out cross price elasticity of demand) i understand its due to collinearity but what is causing this and how do i overcome it?
Q2. Why is the year dummy variable omitted?
Q3. Originally i had 2 separate datasets for 2007 and 2008 but they contained results for the same variables so i appended them together to get one overall dataset- was this correct, has this caused a problem?

I am following a model which has done close to the same thing and they didn't have this problem

Thanks so much in advance