The PANEL Procedure

In this example, a dynamic panel demand model for cigarette sales is estimated. It illustrates the application of the method described in the section Dynamic Panel Estimator. The data are a panel from 46 American states over the period 1963–92. For data description see: Baltagi and Levin (1992); Baltagi (1995). All variables were transformed by taking the natural logarithm. The data set CIGAR is shown in the following statements.

data cigar;
   input state year price pop pop_16 cpi ndi sales pimin;
   label
   state  = 'State abbreviation'
   year   = 'YEAR'
   price  = 'Price per pack of cigarettes'
   pop    = 'Population'
   pop_16 = 'Population above the age of 16'
   cpi    = 'Consumer price index with (1983=100)'
   ndi    = 'Per capita disposable income'
   sales  = 'Cigarette sales in packs per capita'
   pimin  = 'Minimum price in adjoining states per pack of cigarettes';
datalines;
1 63 28.6 3383 2236.5 30.6 1558.3045298 93.9 26.1
1 64 29.8 3431 2276.7 31.0 1684.0732025 95.4 27.5
1 65 29.8 3486 2327.5 31.5 1809.8418752 98.5 28.9
1 66 31.5 3524 2369.7 32.4 1915.1603572 96.4 29.5
1 67 31.6 3533 2393.7 33.4 2023.5463678 95.5 29.6
1 68 35.6 3522 2405.2 34.8 2202.4855362 88.4 32
1 69 36.6 3531 2411.9 36.7 2377.3346665 90.1 32.8
1 70 39.6 3444 2394.6 38.8 2591.0391591 89.8 34.3
1 71 42.7 3481 2443.5 40.5 2785.3159706 95.4 35.8
1 72 42.3 3511 2484.7 41.8 3034.8082969 101.1 37.4

   ... more lines ...   

The following statements sort the data by STATE and YEAR variables.

proc sort data=cigar;
   by state year;
run;

Next, logarithms of the variables required for regression estimation are calculated, as shown in the following statements:

data cigar;
   set cigar;
   lsales = log(sales);
   lprice = log(price);
   lndi = log(ndi);
   lpimin = log(pimin);
   label lprice = 'Log price per pack of cigarettes';
   label lndi = 'Log per capita disposable income';
   label lsales = 'Log cigarette sales in packs per capita';
   label lpimin = 'Log minimum price in adjoining states
                     per pack of cigarettes';
run;

The following statements create the CIGAR_LAG data set with lagged variable for each cross section.

proc panel data=cigar;
   id state year;
   clag lsales(1) / out=cigar_lag;
run;

data cigar_lag;
   set cigar_lag;
   label lsales_1 = 'Lagged log cigarette sales in packs per capita';
run;

Finally, the model is estimated by a two step GMM method. Five lags (MAXBAND=5) of the dependent variable are used as instruments. NOLEVELS options is specified to avoid use of level equations, as shown in the following statements:

proc panel data=cigar_lag;
   inst depvar;
   model lsales = lsales_1 lprice lndi lpimin
         / gmm2 nolevels maxband=5 noint;
   id state year;
run;

If the theory suggests that there are other valid instruments, PREDETERMINED, EXOGENOUS and CORRELATED options can also be used.