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## Y = Rate of Change, Stock Prices, Pe… Show more TABLE 66 S

Paper help Economics Y = Rate of Change, Stock Prices, Pe… Show more TABLE 66 S

# Y = Rate of Change, Stock Prices, Pe… Show more TABLE 66 S

Y = Rate of Change, Stock Prices, Pe… Show more TABLE 66 Stock Prices and Consumer Prices TABLE 66 Stock Prices and Consumer Prices TABLE 66 Stock Prices and Consumer Prices CITY Y = Rate of Change, Stock Prices, Percent Per Year Y = Rate of Change, Stock Prices, Percent Per Year Y = Rate of Change, Stock Prices, Percent Per Year Y = Rate of Change, Stock Prices, Percent Per Year X = Rate of Change, Consumer Prices, Percent Per Year X = Rate of Change, Consumer Prices, Percent Per Year X = Rate of Change, Consumer Prices, Percent Per Year X = Rate of Change, Consumer Prices, Percent Per Year CITY Y X A 5 4.3 B 11.1 4.6 C 3.2 2.4 D 7.9 2.4 E 25.5 26.4 F 3.8 4.2 G 11.1 5.5 H 9.9 4.7 I 3.3 2.2 J 1.5 4 K 6.4 4 L 8.9 8.4 M 8.1 3.3 N 13.5 4.7 O 4.7 5.2 P 7.5 3.6 Q 4.73. 6 R 8 4 S 7.5 3.9 T 9 2.1 Table 66 gives data on percent change per year stock prices (Y) and consumer prices (X) for a cross section of 20 cities. ******************* answer in “SAS format” please********************* (if possible) 1) Plot the data in scattergram 2) Regress Y on X and examine the residuals from this regression. What do you observe? 3) Since the data for city(E) is unusual, repeat the regression in (2) dropping the data on city(E). Now examine the residuals from this regression. What do you observe? 4) If on the basis of the results in (2) you conclude that there was heteroscedasticity in the error variance but on the basis of the results in (3) you reverse your conclusion, what general conclusions do you draw? State whether the following statements are true or false. Breifly justify your answer: 5) When autocorrelation is present, OLS estimators are biased as well as inefficient; 6) The R squared values of two models, one involving regression in the first-difference form and another in the level form, are not directly comparable. 7) In the presence of heterscedasticity the usual OLS method always overestimates the standard errors of estimators. 8) If a regression model is mis-specified (e.g., an important variable is ommitted), the OLS residuals will show a distinct pattern. • Show less

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