A. We would examine the “sensitivity” of the three categories based on their elasticity of demand. The expenditure on clothing is the most sensitive to prices of products since it’s the least basic need of human beings out of the three. The food is the most basic need out of the three, thus it will be the least sensitive to product price change, as we speak of DISPOSABLE INCOME (the food and housing expenditures amount and quality that one consumes are limited relatively to clothing).
Speaking about sensitivity to income, as income rises one is more likely to spend a larger portion of his extra income on the clothing expenditures as the consumption levels of housing and food are not expected to rise drastically (elasticity of demand).
B. The main advantages of using aggregate data (MRM vs. SRM) in estimating a demand function is that we get more realistic picture. E.g when taking into consideration all or several explanatory variables (price, income, trend…), we are likely to achieve more informative and realistic picture\explanation (avoiding biased variables and information).
The drawback are that we might be using variables with perfect or partial correlation (perfect\partial multico-linearity) which can result in distorted results or poor estimates.
Dependent Variable: LGFOOD |
Method: Least Squares |
Date: 02/04/11 Time: 12:03 |
Sample: 1959 2003 |
Included observations: 45 |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
LGDPI | 0.505634 | 0.007007 | 72.15646 | 0.0000 |
C | 1.842743 | 0.057990 | 31.77697 | 0.0000 |
R-squared | 0.991809 | Mean dependent var | 6.021331 |
Adjusted R-squared | 0.991618 | S.D. dependent var | 0.222787 |
S.E. of regression | 0.020396 | Akaike info criterion | -4.903487 |
Sum squared resid | 0.017889 | Schwarz criterion | -4.823191 |
Log likelihood | 112.3285 | F-statistic | 5206.554 |
Durbin-Watson stat |... [continues]
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