Annual per-capita cigarette sales for 46 U.S. states over 1963–1992 (a balanced panel, 1,380 state-year observations), along with the price per pack, population, consumer price index, per-capita disposable income, and the minimum price per pack among neighboring states used to instrument for price endogeneity. Baltagi, B. H. and Levin, D. (1992), "Cigarette taxation: raising revenues and reducing consumption," Structural Change and Economic Dynamics, 3(2), 321–335. See also Baltagi, B. H., Griffin, J. M. and Xiong, W. (2000), "To pool or not to pool: homogeneous versus heterogeneous estimators applied to cigarette demand," Review of Economics and Statistics, 82(1), 117–126.
Format
A data frame with 1,380 observations and 9 variables (46 states times 30 years, 1963–1992):
- state
State identifier code (46 distinct U.S. states). Use as
ivar.- year
Year, coded 63–92 (i.e., 1963–1992). The time variable: use as
tvar.- price
Price per pack of cigarettes (nominal).
- pop
Population.
- pop16
Population above the age of 16.
- cpi
Consumer price index (1983 = 100).
- ndi
Per-capita nominal disposable income.
- sales
Cigarette sales, in packs per capita.
- pimin
Minimum price per pack of cigarettes in adjoining states.
Source
Baltagi, B. H. and Levin, D. (1992). Cigarette taxation: raising revenues and reducing consumption. Structural Change and Economic Dynamics, 3(2), 321–335.
Baltagi, B. H., Griffin, J. M. and Xiong, W. (2000). To pool or not to pool: homogeneous versus heterogeneous estimators applied to cigarette demand. Review of Economics and Statistics, 82(1), 117–126.
Distributed via R package plm, data(Cigar).
Redistribution basis: system.file("COPYRIGHTS", package = "ivreg2r").
Examples
data(cigar)
# Price-endogeneity IV specification in real (CPI-deflated) terms, with
# the neighboring-state minimum price as the excluded instrument, as in
# the GFIC empirical example.
cigar_real <- transform(
cigar,
lsales = log(sales),
lrprice = log(price / cpi),
lrndi = log(ndi / cpi),
lrpimin = log(pimin / cpi)
)
fit <- ivreg2(lsales ~ lrndi | lrprice | lrpimin, data = cigar_real)
summary(fit)
#>
#> 2SLS Estimation
#>
#> Call:
#> ivreg2(formula = lsales ~ lrndi | lrprice | lrpimin, data = cigar_real)
#>
#> Observations: 1,380
#> VCV type: Classical (iid)
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 3.58676 0.11626 30.852 <2e-16 ***
#> lrprice -0.75698 0.04233 -17.882 <2e-16 ***
#> lrndi 0.24775 0.02521 9.827 <2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> ---
#> R-squared: 0.3165
#> Adj. R-squared: 0.3155
#> Wald chi2(2): 168.3 (p < 2.2e-16)
#> Root MSE: 0.1856
#>
#> Underidentification test (Anderson canon. corr. LM statistic):
#> Chi-sq(1) = 901.42 (p < 2.2e-16)
#>
#> Weak identification test:
#> Cragg-Donald Wald F: 2593.62
#> Stock-Yogo critical values (IV size):
#> 10% maximal IV size 16.38
#> 15% maximal IV size 8.96
#> 20% maximal IV size 6.66
#> 25% maximal IV size 5.53
#>
#> Overidentification test (Sargan): (equation exactly identified)
#>
#> Weak-instrument-robust inference:
#> H0: B1=0 and orthogonality conditions are valid
#> Anderson-Rubin Wald F(1,1377) = 261.83 (p < 2.2e-16)
#> Anderson-Rubin Wald Chi-sq(1) = 262.40 (p < 2.2e-16)
#> Stock-Wright LM S Chi-sq(1) = 220.48 (p < 2.2e-16)
#>
#> Endogeneity test:
#> Chi-sq(1) = 16.86 (p = 0.0000)
#> Tested: lrprice
#>
#> First-stage diagnostics:
#> Endogenous F-stat p-value Partial R2 Shea PR2 SW F AP F
#> lrprice 2593.62 0.0000 0.6532 0.6532 2593.62 2593.62
#>
#> Instrumented: lrprice
#> Included instruments: lrndi
#> Excluded instruments: lrpimin
#>