Last data update: 2014.03.03

 R: Cigarette Consumption Data
CigarettesBR Documentation

Cigarette Consumption Data

Description

Cross-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.

Source

The data are from Baltagi (2002).

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.

See Also

Baltagi2002, CigarettesSW

Examples

data("CigarettesB")

## Baltagi (2002)
## Table 3.3
cig_lm <- lm(packs ~ price, data = CigarettesB)
summary(cig_lm)

## Chapter 5: diagnostic tests (p. 111-115)
cig_lm2 <- lm(packs ~ price + income, data = CigarettesB)
summary(cig_lm2)
## Glejser tests (p. 112)
ares <- abs(residuals(cig_lm2))
summary(lm(ares ~ income, data = CigarettesB))
summary(lm(ares ~ I(1/income), data = CigarettesB))
summary(lm(ares ~ I(1/sqrt(income)), data = CigarettesB))
summary(lm(ares ~ sqrt(income), data = CigarettesB))
## Goldfeld-Quandt test (p. 112)
gqtest(cig_lm2, order.by = ~ income, data = CigarettesB, fraction = 12, alternative = "less")
## 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")
## Breusch-Pagan test (p. 113)
bptest(cig_lm2, varformula = ~ income, data = CigarettesB, student = FALSE)
## White test (Table 5.1, p. 113)
bptest(cig_lm2, ~ income * price + I(income^2) + I(price^2), data = CigarettesB)
## White HC standard errors (Table 5.2, p. 114)
coeftest(cig_lm2, vcov = vcovHC(cig_lm2, type = "HC1"))
## 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))

## Tables 8.1 and 8.2
influence.measures(cig_lm2)

## More examples can be found in:
## help("Baltagi2002")

Results


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> library(AER)
Loading required package: car
Loading required package: lmtest
Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Loading required package: sandwich
Loading required package: survival
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/AER/CigarettesB.Rd_%03d_medium.png", width=480, height=480)
> ### Name: CigarettesB
> ### Title: Cigarette Consumption Data
> ### Aliases: CigarettesB
> ### Keywords: datasets
> 
> ### ** 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

> ## 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")
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>


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