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.