1.What is the causal relationship in the journal or working paper? Please state the cause and effect variables.

The cause variable is the oil prices. The effect variable is the supply of corn. In United States, corn is one of the sources for the production of ethanol fuel which is mainly used as a motor fuel/fuel additive. The rise and fall in the oil price will affect the corn supply.

2.What is the correlation between the cause and effect variables?

There is a positive correlation between the oil prices and the supply of corn. This is because, the rise in the oil prices will result in the increase of the supply of corn.

There is a positive correlation between sugar and the supply of corn. Ethanol also is produced from the biomass through the fermentation process using glucose derived from sugar. So, when supply of sugar is increase, corn supply also has to show an increment in order to keep units the same with the sugar.

There is a positive correlation between soybeans and the supply of corn. Soybeans are being used to produce ethanol too. In fact, based on research, soybeans can produce more usable energy and reduces greenhouse gases compared to corn-based ethanol. So, when soybeans increase, supply of corn also will increase in order to keep units the same.

There is a negative correlation between chicken price and the supply of corn. This is because, the rise in the chicken price will result in the decrease of the supply of corn.

There is a positive correlation between pork price and the supply of corn. This is because, when the pork price increase, the supply of corn also will increase.

3.Please state and explain on the type of data used in the journal or working paper.

The data in this working paper is a time series data. This is because, the observation of the oil prices and the corn supply is made over time. All the data is being observed and recorded annually for the United States from 1988 to 2010 since the explosion of the ethanol production occurred in the 1970’s and the 21st century.

4.Discuss whether or not the zero conditional mean assumption (x|u=0) applies to the econometrics model? What are other factors that you can think of that may influence the ‘effect variable’?

No. Because there are still other factors that may affect the dependent variable. The other factors are;

?Weather: When the weather is favourable, the production of corn will increase. However, the opposite things happened when the weather is unfavourable like the drought that occured in 2012 that caused a reduction of corn supply.

?Nitrogen supply: Soil that holds a high amount of nitrogen can support a large corn supply.

?Hybrid selection: Biotechnology or genetics upgraded the potential of the corn seeds. Hybrid selection is one of the important factors because it will affect the overall production.

?Chinese effect: Since 2010, China is the greatest consumer and producer of energy. It means that when the country encouraged the use of cleaner energy, the demand for biofuels would increase. So, the corn supply also will show an increment.

Here, it seems like the Chinese effect variable could be positively correlated with oil price. Because we would like to hold these other factors fixed,they are part of the error term. However, if u is correlated with oil price then E(u|oil price)?0, so SLR.4 fails.

5.Comment on the sign and magnitude of the estimated coefficient of the explanatory variables.

The sign for ?1(Oil Price) is ?1;0. An increment of 1 unit in oil price will cause the corn supply to increase by 34.86.

The sign for ?2(Sugar) is ?2;0. An increment of 1 unit in sugar will cause the corn supply to increase by 0.044.

The sign for ?3(Soybeans) is ?3;0. An increment of 1 unit in soybeans will cause the corn supply to increase by 1.75.

The sign for ?4(Chicken Price) is ?40. An increment of 1 unit in pork price will cause the corn supply to increase by 9.975.

6.Are these explanatory variables statistically significant?

To investigate the significance of the explanatory variables, hypothesis test will be carried out.

Simple linear regression:

Supply = 7364.797 + 71.694Price

(437.96) (11.89)

?Oil Price:

?1=0

?1?0

T-stat:

D.f: 25-1-1= 23

– t-distribution

– two sided test

– 0.05 significance level

– critical value: 2.069

T-stat value is in rejection region. So,we reject the null hypothesis. Thus, oil price is a significant variable.

Multiple linear regression:

Supply = 2413.567 +34.86Price + .044Sugar + 1.75Soybeans – 10.58Chicken + 9.975Pork

(3116.307) (15.61) (.43) (.771) (9.96) (13.71)

?Oil Price:

?1=0

?1?0

T-stat :

D.f: 25-5-1=19

– t-distribution

– two sided test

– 0.05 significance level

– critical value: 2.093

T-stat value is in the rejection region. So, we reject the null hypothesis. Thus, oil price is a significant variable.

?Sugar:

?2=0

?2?0

T-stat :

D.f: 25-5-1=19

– t-distribution

– two sided test

– 0.05 significance level

– critical value: 2.093

T-stat value is not in the rejection region. So, we fail to reject the null hypothesis. Thus, sugar is not a significant variable.

?Soybeans:

?3=0

?3?0

T-stat :

D.f: 25-5-1=19

– t-distribution

– two sided test

– 0.05 significance level

– critical value: 2.093

T-stat value is in the rejection region. So, we reject the null hypothesis. Thus, soybeans is a significant variable.

?Chicken Price:

?4=0

?4?0

T-stat :

D.f: 25-5-1=19

– t-distribution

– two sided test

– 0.05 significance level

– critical value: 2.093

T-stat value is not in the rejection region. So, we fail to reject the null hypothesis. Thus, chicken price is not a significant variable.

?Pork Price:

?5=0

?5?0

T-stat :

D.f: 25-5-1=19

– t-distribution

– two sided test

– 0.05 significance level

– critical value: 2.093

T-stat value is not in the rejection region. So, we fail to reject the null hypothesis. Thus, pork price is not a significant variable.

Since the variables sugar, chicken price and pork do not seem significant, F-test will be carried out to investigate whether there is joint significance in them.

?2=0,?4=0,?5=0

Ho is not true

Test statistic:

T-statistic value is not in the rejection region. So, we fail to reject the null hypothesis. Thus, the three variables are not jointly significant.

7.Would you say the explanatory variables explain much of the variation in dependent variable?

The R-squared value is 0.8317. It means that the five explanatory variables explain about 83.17% of the variation in supply of corns which is considered a large variation because it is more than 70%. It also suggests a much more correlation and the variables included help eliminated any error from the omitted variables in the simple regression model.