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# MAE Assingment - Assignment Example

## Extract of sampleMAE Assingment

Dev.  0.025827  11240942  65635008  0.019383  Observations  45  45  45  45 Table 1 above presents the descriptive statistics for our variables of interest. The only point of concern that may arise in this situation is that all the variables reflect some degree of skewness which violates the normality assumption. Additionally, the fact that the number of observations is only 45 may also be a point of concern since this can lead to small sample bias. 2. Time plots Figure 1: Time plot of P There are no seasonal patterns evident in the time plot of P. Figure 2: Time plot of Q The time plot of Q exhibits strong seasonal variations. Figure 3: Time plot of G As is evident from figure 3 above, similar to the time plot of P, the time plot of G also does not exhibit seasonal fluctuations. Figure 4: Time plot of X Figure 4 shows that X also follows a seasonally fluctuating pattern 3. Thus, there is strong evidence of seasonal fluctuations among the Q and X series. This is visible in the oscillatory patterns that these series seem to follow. The series P and G exhibit no seasonal patterns. Additionally, all the series reflect a steady upward trend. Therefore inclusion of seasonal dummies is important since our dependent variable Q does exhibit seasonal fluctuations. ...
18.31869 0.0000 P -7530.197 6092.988 -1.235879 0.2235 G -84559.50 9770.479 -8.654591 0.0000 X 1.865016 0.111494 16.72746 0.0000 R-squared 0.915605     Mean dependent var 4442.111 Adjusted R-squared 0.909430     S.D. dependent var 505.4463 S.E. of regression 152.1132     Akaike info criterion 12.97181 Sum squared resid 948675.2     Schwarz criterion 13.13241 Log likelihood -287.8658     Hannan-Quinn criter. 13.03168 F-statistic 148.2710     Durbin-Watson stat 1.390217 Prob(F-statistic) 0.000000 From table 2 above we find that the estimated coefficients for both P and X are significantly different from zero (evident from the t-statistic). G however is not a significant determinant of Q. The coefficients reflect that the demand for drink and tobacco is negatively influenced by the price of the items and positively influenced by the total consumer expenditure. The coefficient on G is also negative but since it is not significantly different from 0 at the 5% level, we conclude that it does not have an influence on drink and tobacco demand. Thus, our results imply that an increase in the prices of drinks and tobacco will lead to a reduction in its demand while an increase in overall consumer expenditure leads to an increase in the demand. 5. Attempting to include all four dummies leads to perfect multicollinearity. Thus we modify the equation and include dummies for the 1st 3 quarters only. Table 3 presents the results. Table 3: OLS estimation with quarterly dummies Dependent Variable: Q Method: Least Squares Date: 09/01/11 Time: 01:01 Sample: 1980Q1 1991Q1 Included observations: 45 Coefficient Std. Error t-Statistic Prob.   C 5127.935 356.9563 14.36572 0.0000 P -8713.964 2700.994 -3.226206 0.0026 X 0.805451 0.096091 8.382187 0.0000 G -23150.70 ...Show more

## Summary

Part 1: The Demand for Drink and Tobacco 1. Descriptive Statistics Table 1: Descriptive Statistics of the variables of interest P Q X G  Mean  0.164142  4442.111  5854.911  0.165827  Median  0.171100  4501.000  5771.000  0.164500  Maximum  0.206900  5598.000  8737.000  0.210700  Minimum  0.127100  3454.000  4000.000  0.137400  Std…
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