The reasons for more attention attributed to national economies as compared to state or provisional economies by the economist.
Concept Introduction:
National Economy: The economy of a nation as a whole, sum of individual units.
National Economy Indicators: This allows analysis of the performance of the economy

Explanation of Solution
National economic indicators are better indicators of the economic health of a country than the state economies because state economies are concerned with their regions only. The local, political or economic fluctuations only affect those areas or nearby areas and therefore are less important at the central level. They do not provide the perfect picture at the macro level to the economist. If the economist wants to get the information about the whole country, then he has to give importance to indicators like
NO doubt, states economies are also important and are the pillars of any central economy but wider picture is shown by only the national economies.
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Chapter 5 Solutions
MACRO ECON 6
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- Determine whether the following is true or false: In a multiple linear regression, y = ẞ₁ + ẞ₁x, + ẞ₂×₂ + u, 0 if corr(x,, x) = 1, then ẞ, cannot be estimated, because the numerator in the formula for ß, is zero. Explain your answer in two sentences or fewer.arrow_forwardChoose all correct answers regarding assumptions for B₁ in a simple linear regression. (a) If there is no variation in X, we cannot identify the estimate of ẞ₁, so E[ẞ₁] = ẞ, does not hold. (b) It measures the y-intercept of the estimated regression line. (c) As the sample size increases, ẞ₁ is more likely to be closer to ẞ, assuming E[ß₁] = ß₁. (d) If the sample covariance between x and y is positive, then B₁ > 0.arrow_forwardChoose all correct answers regarding the multiple linear regression model: y = Bo+ B₁x₁ + B2X2 + + BkXk + U. (a) If you add a relevant variable XK+1, but corr(XK, Xk+1) is high, then you must not include xk+1 in the regression equation. (b) Adding a variable XK+₁ may lower the R² value. (c) Adding all the variables in the dataset does not imply that E[u | X1, X2, ... Xk] = 0. (d) We can estimate the value of ẞ₁ by minimizing Σ(û¡)².arrow_forward
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- Choose one correct answer: The ---A----- dataset includes 100,000 students from 512 schools observed over 12 months. To control differences between schools and months, we should add a total of What are A and B? (a) A: Time series, B: 524 (b) A: Time series, B: 522 (c) A: Panel, B: 524 (d) A: Panel, B: 522 dummies.arrow_forwardRefer to the Stata output from an analysis of data on Store sales. The data include nominal store-level sales (measured in for the month of June 1996 from 844 stores of a national retail chain. The other variables are: A categorical string variable loctype which indicates the type of store location and takes one of three values: "mall" = 1 if the store is located in a shopping mall (indoor or outdoor) "street" = 1 if the store is located on a street "strip" = 1 if the store is located on a strip or highway ⚫ medhhinc = the median household income of the city where the store is located, measured in $1,000 • empl = the total number of employees working in the store in June 1996 . sqft = the store's size, measured in 1,000 square feet A manager wants to know how average monthly sales varies with the characteristics of a store and its location and uses Stata to perform the regression analysis shown. gen sqftXmedhhinc =sqft*medhhinc . reg sales mall street medhhinc empl sqft sqftXmedhhinc,…arrow_forwardEssay question. In this regression analysis, you are interested in the effect of age on the amount of gasoline used by a car, measured in gallons. Suppose you analyze your dataset and find the following results. Based on the Stata output, how can you improve the analysis? State 3 issues in the figure and table, and possible solution for each problem you can apply based on what you have learned in class. gasoline_use 40 20 100 .reg gasoline_use age Source SS df MS Model Residual 28.1117271 9969.5248 1 28.1117271 23 433.4576 Number of obs F(1, 23) Prob > F R-squared 25 0.06 0.8012 0.0028 a Total 9997.63653 24 416.568189 Adj R-squared Root MSE -0.0405 20.82 gasoline_use Coef. Std. Err. t P>|t| [95% Conf. Interval] age cons -.0688645 .2704114 62.37748 13.86839 -0.25 0.801 4.50 0.000 -.6282532 .4905242 33.68853 91.06643 20 40 60 80 agearrow_forward
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