Cigarette Consumption Data
CigarettesB.RdCross-section data on cigarette consumption for 46 US States, for the year 1992.
Usage
data("CigarettesB")Format
A data frame containing 46 observations on 3 variables.
- packs
Logarithm of cigarette consumption (in packs) per person of smoking age (> 16 years).
- price
Logarithm of real price of cigarette in each state.
- income
Logarithm of real disposable income (per capita) in each state.
References
Baltagi, B.H. (2002). Econometrics, 3rd ed. Berlin, Springer.
Baltagi, B.H. and Levin, D. (1992). Cigarette Taxation: Raising Revenues and Reducing Consumption. Structural Change and Economic Dynamics, 3, 321–335.
Examples
data("CigarettesB")
## Baltagi (2002)
## Table 3.3
cig_lm <- lm(packs ~ price, data = CigarettesB)
summary(cig_lm)
#>
#> Call:
#> lm(formula = packs ~ price, data = CigarettesB)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.45472 -0.09968 0.00612 0.11553 0.29346
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 5.0941 0.0627 81.247 < 2e-16 ***
#> price -1.1983 0.2818 -4.253 0.000108 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.163 on 44 degrees of freedom
#> Multiple R-squared: 0.2913, Adjusted R-squared: 0.2752
#> F-statistic: 18.08 on 1 and 44 DF, p-value: 0.0001085
#>
## Chapter 5: diagnostic tests (p. 111-115)
cig_lm2 <- lm(packs ~ price + income, data = CigarettesB)
summary(cig_lm2)
#>
#> Call:
#> lm(formula = packs ~ price + income, data = CigarettesB)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.41867 -0.10683 0.00757 0.11738 0.32868
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 4.2997 0.9089 4.730 2.43e-05 ***
#> price -1.3383 0.3246 -4.123 0.000168 ***
#> income 0.1724 0.1968 0.876 0.385818
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.1634 on 43 degrees of freedom
#> Multiple R-squared: 0.3037, Adjusted R-squared: 0.2713
#> F-statistic: 9.378 on 2 and 43 DF, p-value: 0.0004168
#>
## Glejser tests (p. 112)
ares <- abs(residuals(cig_lm2))
summary(lm(ares ~ income, data = CigarettesB))
#>
#> Call:
#> lm(formula = ares ~ income, data = CigarettesB)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.13738 -0.07061 -0.01891 0.07253 0.24508
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 1.16169 0.46267 2.511 0.0158 *
#> income -0.21689 0.09684 -2.240 0.0302 *
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.09242 on 44 degrees of freedom
#> Multiple R-squared: 0.1023, Adjusted R-squared: 0.08193
#> F-statistic: 5.016 on 1 and 44 DF, p-value: 0.03022
#>
summary(lm(ares ~ I(1/income), data = CigarettesB))
#>
#> Call:
#> lm(formula = ares ~ I(1/income), data = CigarettesB)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.14143 -0.07235 -0.01921 0.07227 0.24186
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -0.9489 0.4671 -2.032 0.0483 *
#> I(1/income) 5.1287 2.2277 2.302 0.0261 *
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.09215 on 44 degrees of freedom
#> Multiple R-squared: 0.1075, Adjusted R-squared: 0.08722
#> F-statistic: 5.3 on 1 and 44 DF, p-value: 0.02611
#>
summary(lm(ares ~ I(1/sqrt(income)), data = CigarettesB))
#>
#> Call:
#> lm(formula = ares ~ I(1/sqrt(income)), data = CigarettesB)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.14041 -0.07192 -0.01914 0.07233 0.24267
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -2.0045 0.9317 -2.151 0.0370 *
#> I(1/sqrt(income)) 4.6541 2.0352 2.287 0.0271 *
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.09222 on 44 degrees of freedom
#> Multiple R-squared: 0.1062, Adjusted R-squared: 0.08591
#> F-statistic: 5.229 on 1 and 44 DF, p-value: 0.02708
#>
summary(lm(ares ~ sqrt(income), data = CigarettesB))
#>
#> Call:
#> lm(formula = ares ~ sqrt(income), data = CigarettesB)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.13838 -0.07105 -0.01899 0.07247 0.24428
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 2.2172 0.9273 2.391 0.0211 *
#> sqrt(income) -0.9571 0.4243 -2.255 0.0291 *
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.09235 on 44 degrees of freedom
#> Multiple R-squared: 0.1036, Adjusted R-squared: 0.08326
#> F-statistic: 5.087 on 1 and 44 DF, p-value: 0.02913
#>
## Goldfeld-Quandt test (p. 112)
gqtest(cig_lm2, order.by = ~ income, data = CigarettesB, fraction = 12, alternative = "less")
#>
#> Goldfeld-Quandt test
#>
#> data: cig_lm2
#> GQ = 0.31846, df1 = 14, df2 = 14, p-value = 0.02017
#> alternative hypothesis: variance decreases from segment 1 to 2
#>
## NOTE: Baltagi computes the test statistic as mss1/mss2,
## i.e., tries to find decreasing variances. gqtest() always uses
## mss2/mss1 and has an "alternative" argument.
## Spearman rank correlation test (p. 113)
cor.test(~ ares + income, data = CigarettesB, method = "spearman")
#>
#> Spearman's rank correlation rho
#>
#> data: ares and income
#> S = 20784, p-value = 0.05813
#> alternative hypothesis: true rho is not equal to 0
#> sample estimates:
#> rho
#> -0.2817761
#>
## Breusch-Pagan test (p. 113)
bptest(cig_lm2, varformula = ~ income, data = CigarettesB, student = FALSE)
#>
#> Breusch-Pagan test
#>
#> data: cig_lm2
#> BP = 5.4852, df = 1, p-value = 0.01918
#>
## White test (Table 5.1, p. 113)
bptest(cig_lm2, ~ income * price + I(income^2) + I(price^2), data = CigarettesB)
#>
#> studentized Breusch-Pagan test
#>
#> data: cig_lm2
#> BP = 15.656, df = 5, p-value = 0.007897
#>
## White HC standard errors (Table 5.2, p. 114)
coeftest(cig_lm2, vcov = vcovHC(cig_lm2, type = "HC1"))
#>
#> t test of coefficients:
#>
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 4.29966 1.09523 3.9258 0.0003076 ***
#> price -1.33833 0.34337 -3.8977 0.0003352 ***
#> income 0.17239 0.23661 0.7286 0.4702172
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
## Jarque-Bera test (Figure 5.2, p. 115)
hist(residuals(cig_lm2), breaks = 16, ylim = c(0, 10), col = "lightgray")
library("tseries")
jarque.bera.test(residuals(cig_lm2))
#>
#> Jarque Bera Test
#>
#> data: residuals(cig_lm2)
#> X-squared = 0.28935, df = 2, p-value = 0.8653
#>
## Tables 8.1 and 8.2
influence.measures(cig_lm2)
#> Influence measures of
#> lm(formula = packs ~ price + income, data = CigarettesB) :
#>
#> dfb.1_ dfb.pric dfb.incm dffit cov.r cook.d hat inf
#> AL 0.14503 0.069010 -0.14188 0.19186 1.070 1.23e-02 0.0480
#> AZ -0.11311 0.033072 0.10173 -0.25077 0.968 2.05e-02 0.0315
#> AR 0.56419 0.376064 -0.56381 0.66702 0.847 1.36e-01 0.0847
#> CA -0.01386 -0.255192 0.02769 -0.31637 1.114 3.34e-02 0.0975
#> CT 0.15244 -0.022453 -0.14793 -0.20087 1.219 1.37e-02 0.1354 *
#> DE -0.12654 -0.037389 0.12991 0.23129 0.992 1.76e-02 0.0326
#> DC 0.23239 0.001472 -0.22823 -0.29167 1.149 2.86e-02 0.1104
#> FL 0.01116 0.050233 -0.01301 0.07389 1.112 1.86e-03 0.0431
#> GA -0.00269 -0.028971 0.00551 0.04527 1.114 6.99e-04 0.0402
#> ID -0.10101 -0.013791 0.09591 -0.15005 1.079 7.59e-03 0.0413
#> IL -0.00101 0.000075 0.00101 0.00178 1.118 1.09e-06 0.0399
#> IN -0.03076 -0.153574 0.04353 0.19363 1.105 1.26e-02 0.0650
#> IA 0.00638 0.007696 -0.00639 0.01509 1.107 7.77e-05 0.0310
#> KS 0.00314 -0.002575 -0.00389 -0.04083 1.092 5.68e-04 0.0223
#> KY -0.09222 -0.725107 0.14758 0.80979 1.113 2.10e-01 0.1977 *
#> LA 0.31705 0.226157 -0.31744 0.38745 1.022 4.91e-02 0.0761
#> ME 0.17424 0.309538 -0.18410 0.40000 0.940 5.13e-02 0.0553
#> MD 0.39398 0.378023 -0.41346 -0.50701 1.073 8.40e-02 0.1216
#> MA 0.19840 0.073723 -0.20018 -0.23411 1.126 1.84e-02 0.0856
#> MI -0.00898 0.025355 0.00991 0.12316 1.052 5.10e-03 0.0238
#> MN 0.01342 0.042769 -0.01537 0.05001 1.172 8.53e-04 0.0864
#> MS 0.06675 0.002382 -0.06369 0.08277 1.171 2.33e-03 0.0883
#> MO -0.03986 -0.089643 0.04634 0.10541 1.154 3.78e-03 0.0787
#> MT -0.04820 0.067706 0.03769 -0.19283 1.021 1.24e-02 0.0312
#> NE 0.02185 0.027580 -0.02540 -0.09498 1.072 3.05e-03 0.0243
#> NV 0.05366 0.347879 -0.06990 0.45042 0.937 6.47e-02 0.0646
#> NH -0.34967 -0.257318 0.36079 0.40764 1.142 5.53e-02 0.1308
#> NJ 0.12527 -0.004859 -0.12241 -0.15616 1.234 8.29e-03 0.1394 *
#> NM -0.38923 -0.064661 0.37379 -0.49010 0.901 7.56e-02 0.0639
#> NY 0.01626 -0.028925 -0.01431 -0.05033 1.175 8.64e-04 0.0888
#> ND -0.15387 -0.005358 0.14232 -0.31360 0.885 3.12e-02 0.0295
#> OH -0.00856 -0.028773 0.01108 0.04159 1.117 5.90e-04 0.0423
#> OK -0.12028 -0.047228 0.11708 -0.15599 1.094 8.21e-03 0.0505
#> PA 0.00741 -0.001370 -0.00765 -0.02452 1.100 2.05e-04 0.0257
#> RI 0.00218 0.114469 -0.00738 0.16917 1.088 9.64e-03 0.0504
#> SC 0.04282 -0.092254 -0.03271 0.15382 1.132 8.02e-03 0.0725
#> SD -0.04178 0.064802 0.03307 -0.14581 1.079 7.17e-03 0.0402
#> TN 0.01884 -0.062711 -0.01037 0.15431 1.046 7.98e-03 0.0294
#> TX -0.06472 -0.095510 0.06734 -0.12671 1.113 5.44e-03 0.0546
#> UT -0.77803 -0.317059 0.76368 -0.88760 0.679 2.24e-01 0.0856 *
#> VT -0.02396 -0.065794 0.03278 0.20305 0.979 1.35e-02 0.0243
#> VA 0.05235 0.069110 -0.05673 -0.08713 1.156 2.59e-03 0.0773
#> WA -0.00136 -0.010137 0.00187 -0.01242 1.175 5.27e-05 0.0866
#> WV -0.11903 0.031391 0.11039 -0.17766 1.122 1.07e-02 0.0709
#> WI 0.00494 0.006306 -0.00481 0.01736 1.100 1.03e-04 0.0254
#> WY -0.00156 -0.025435 0.00388 0.03501 1.135 4.18e-04 0.0555
## More examples can be found in:
## help("Baltagi2002")