Critically/quantitatively evaluate the following statement (true, false or uncertain): • In a linear model, the Generalized Instrumental Variable (GIV) estimator BGIV is iden- tical to the Ordinary Least Squares (OLS) estimator BOLS.
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A: Q=378-2P2P=378-QP=378-Q2P=189-Q2Now,TR=P×QTR=189-Q2QTR=189-Q22Thus,MR=∂TR∂QMR=189-Q
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A: Q=423-2P2P=423-QP=423-Q2P=211.5-Q2Now,TR=P×QTR=(211.5-Q2)QTR=211.5Q-Q22Thus,MR=∂TR∂QMR=211.5-Q
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- Consider the OLS estimator 3;. Under the Gauss-Markov assumptions, O the estimator is the best linear unbiased estimator. O the estimator is asymptotically normally distributed. O the estimator has the properties stated in the other three possible answers. O the estimator is consistent.The regression equation Netincome = 2,049 +.0478 Revenue was estimated from a sample of 100 leading world companies (variables are in millions of dollars). (0-1) If Revenue = 1, then Netincome = (a-2) Choose the correct statement. (b) Choose the right option. (c) O Increasing the revenue raises the net income. Decreasing the revenue raises the net income. O Increasing the revenue lowers the net income. million. (Round your answer to 2 decimal places.) O The intercept is not meaningful because a firm cannot have net income when revenue is zero. The intercept is meaningful because a firm can have net income when revenue is zero. If Revenue = 24,574, then NetIncome = million. (Round your answer to the nearest whole number.)2- regression analysis is concerned with estimating . Please select one; a) the mean of value of the fixed variable b) the mean value of the c) the mean value of the dependent variable d) the mean value of the correlation coefficient
- When the regression error is heteroskedastic, all of the following statements are false, with the exception of: a. the conditional variance of the error term is not constant. b. the OLS estimator is unbiased but not consistent. C. the OLS estimator is still BLUE.Define the Ordinary Least Square estimator is linear and conditionally unbiased?All questions utilize the multivariate demand function for Smooth Sailing sailboats in C6 on text page 83. Compute to three decimal places. Initial values are: PX = $9500 PY = $10000 I = $15000 A = $170000 W = 160 This function is: Qs = 89830 -40PS +20PX +15PY +2I +.001A +10W 1.(a). Use the above to calculate the arc price elasticity of demand between PS = $9000 decreasing to PS = $8000. The arc elasticity formula is: 1.(b). Judging from the computation in (a), do you expect the revenue resulting from the decrease in Ps to $8000 to increase, remain the same, or decrease relative to the revenue at Ps = $9000. (Hint: see the table on page 65 of Truett). Explain your choice. 1.(c). Calculate the point elasticity of demand for Smooth Sailing sailboats at PS = $9000 (which should make Qs = 101600). The formula is: 1.(d). Does this elasticity value indicate that Smooth Sailing demand is relatively responsive to changes in the price of these sailboats? Explain…
- (e) Suppose you have been given the following ordinary least squares (OLS) regression result. Estimated Long Run Coefficients using the ARDL Approach ARDL (1,2,2,2,0,2) selected based on Akaike Information Criterion Dependent variable is LY 33 observations used for estimation from 1987 to 2019 T-Ratio [Prob.] 4.6671[0.000] 4.6678[0.051] 7.9897[0.043] -4.802[0.009] 2.3898[0.028] 1.0498[0.308] Regressor Coefficient Standard Error 0.36068 0.45447 LK 0.077280 LM 0.097363 0.48751 -0.41208 0.19057 0.52521 LE 0.061017 LF 0.085800 LT 0.079744 C 0.500320 where, Y = Economic growth K = Capital M = Employment E = Electricity consumption F = Foreign direct investment T= Technology (i) Write the regression equation. Interpret the estimated coefficients. (ii) Which explanatory variables are significant at the 1%, 5% and 10% level? Which variables are insignificant? Briefly explain.Water is being poured into a large, cone-shaped cistern. The volume of water, measured in cm³, is reported at different time intervals, measured in seconds. A regression analysis was completed and is displayed in the computer output. Regression Analysis: cuberoot (Volume) versus Time Predictor Coef SE Coef Constant -0.006 0.00017 -35.294 0.000 Time 0.640 0.000018 35512.6 0.000 s=0.030 R-Sq=1.000 R-sq (adj)=1.000 What is the equation of the least-squares regression line? Volume = 0.640 - 0.006(Time) Volume = 0.640 - 0.006(Time) Volume = -0.006 + 0.640(Time) Volume = - 0.006 + 0.640(Time?)A researcher estimates a regression using two different software packages.The first uses the homoskedasticity-only formula for standard errors. Thesecond uses the heteroskedasticity-robust formula. The standard errors arevery different. Which should the researcher use? Why?
- Which one of the following statements is true for a linear regression model with non-spherical disturbances (i.e. E(uu') = Ω): The GLS estimator covariance matrix is unreasonable. The OLS estimator is not consistent. The standard formula for the OLS estimator covariance matrix is incorrect. The GLS estimator is not consistent. All of the above. None of the above.Regression analysis was applied between $ sales (y) and $ advertising (r) across all the branches of a major international corporation. The following regression function was obtained. ŷ = 5000 + 7.25r (a) Predict the amount for sales where the advertising amount is $ 1,000,000.00. (b) If the advertising budgets of two branches of the corporation differ by $30,000, then what will be the predicted difference in their sales?As an auto insurance risk analyst, it is your job to research risk profiles for various types of drivers. One common area of concern for auto insurance companies is the risk involved when offering policies to younger, less experienced drivers. The U.S. Department of Transportation recently conducted a study in which it analyzed the relationship between 1) the number of fatal accidents per 1000 licenses, and 2) the percentage of licensed drivers under the age of 21 in a sample of 42 cities. Your first step in the analysis is to construct a scatterplot of the data. FIGURE. SCATTERPLOT FOR U.S. DEPARTMENT OF TRANSPORATION PROBLEM U.S. Department of Transportation The Relationship Between Fatal Accident Frequency and Driver Age 4.5 3.5 3 2.5 1.5 1 0.5 6. 10 12 14 16 18 Percentage of drivers under age 21 Upon visual inspection, you determine that the variables do have a linear relationship. After a linear pattern has been established visually, you now proceed with performing linear…