1 Installing Rmarkdown

2 Running Your R code online

3 Data and Regression

Christian Kleiber and Achim Zeileis (2008), Applied Econometrics with R, Springer-Verlag, New York.
http://cran.r-project.org/web/packages/AER/AER.pdf


Example 1: Cigarette Consumption
This example is taken from Baltagi (Section 3.10 Empirical Example)

Let us install the AER package. This can be done using the menu:

or using the following command:

Now let us load the “CigarettesB” data from AER package:

## [1] "packs"  "price"  "income"
##      packs    price  income
## AL 4.96213  0.20487 4.64039
## AZ 4.66312  0.16640 4.68389
## AR 5.10709  0.23406 4.59435
## CA 4.50449  0.36399 4.88147
## CT 4.66983  0.32149 5.09472
## DE 5.04705  0.21929 4.87087
## DC 4.65637  0.28946 5.05960
## FL 4.80081  0.28733 4.81155
## GA 4.97974  0.12826 4.73299
## ID 4.74902  0.17541 4.64307
## IL 4.81445  0.24806 4.90387
## IN 5.11129  0.08992 4.72916
## IA 4.80857  0.24081 4.74211
## KS 4.79263  0.21642 4.79613
## KY 5.37906 -0.03260 4.64937
## LA 4.98602  0.23856 4.61461
## ME 4.98722  0.29106 4.75501
## MD 4.77751  0.12575 4.94692
## MA 4.73877  0.22613 4.99998
## MI 4.94744  0.23067 4.80620
## MN 4.69589  0.34297 4.81207
## MS 4.93990  0.13638 4.52938
## MO 5.06430  0.08731 4.78189
## MT 4.73313  0.15303 4.70417
## NE 4.77558  0.18907 4.79671
## NV 4.96642  0.32304 4.83816
## NH 5.10990  0.15852 5.00319
## NJ 4.70633  0.30901 5.10268
## NM 4.58107  0.16458 4.58202
## NY 4.66496  0.34701 4.96075
## ND 4.58237  0.18197 4.69163
## OH 4.97952  0.12889 4.75875
## OK 4.72720  0.19554 4.62730
## PA 4.80363  0.22784 4.83516
## RI 4.84693  0.30324 4.84670
## SC 5.07801  0.07944 4.62549
## SD 4.81545  0.13139 4.67747
## TN 5.04939  0.15547 4.72525
## TX 4.65398  0.28196 4.73437
## UT 4.40859  0.19260 4.55586
## VT 5.08799  0.18018 4.77578
## VA 4.93065  0.11818 4.85490
## WA 4.66134  0.35053 4.85645
## WV 4.82454  0.12008 4.56859
## WI 4.83026  0.22954 4.75826
## WY 5.00087  0.10029 4.71169

3.1 Regression

We regress consumption on price using OLS:

## 
## 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

3.2 Results in Equation Form

## (Intercept)       price 
##    5.094108   -1.198316

The R-square value for our linear model is \(R^2\)=0.2912836.

The adjusted R-square is \(\bar{R}^2\)=0.2751764.

From this, we can deduce that the estimate of the intercept is \(\widehat \beta_0= 5.0941081\), and the estimate of the slope is \(\widehat \beta_1= -1.1983162\).

Consequently, the line of best fit is \[ \widehat {Y_{t}} = 5.0941081 -1.1983162 X_{t} \] However, this is reporting the estimates to too many decimal places: we can reduce that into 3 decimal places as follows: \[ \widehat {Y_{t}} = 5.094 -1.198 X_{t}. \]

3.4 Confidence Intervals

##                 2.5 %     97.5 %
## (Intercept)  4.967747  5.2204696
## price       -1.766224 -0.6304087

3.5 Histogram of residuals

3.6 Saving Fitted Values