Consider the following bivariate linear regression model y = a+3x+u. Suppose that E[u|x] # 0 and that z is a valid instrumet for r. Knowing that Cov(y, z) = 0.5 and Cov(2, x) = 0.5, the IV estimate of 3 is 1. O True O False
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![QUESTION 2
Consider the following bivariate linear regression model y = a+3x+u. Suppose that E[u]x] #0
and that z is a valid instrument for r. Knowing that Cov(y, z) = 0.5 and Cov(z, x) = 0.5, the
IV estimate of 3 is 1.
%3D
O True
O False](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F21c69e4a-662f-4e66-806f-5b90389d8382%2Fc799fe04-9800-48bc-8a55-6b7028196ed6%2F1do8txv4_processed.png&w=3840&q=75)
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- OA linear regression model is Units 3,414-0.839xWeek. For week 45, what is the forecast for the number of units? Round your answer to the nearest whole number. OO unitsThe 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.)Describe the important characteristics of the variance of a conditional distribution of an error term in a linear regression. What are the implicationsfor OLS estimation?
- 1. R-squaredSuppose regression of y on an intercept and x with 50 observations yields total sum of squares 100 andexplained sum of squares 36.(a) What is ?^2?(b) What is the correlation coefficient between y and x?(c) What is the standard error of the residual?You estimated a regression with the following output. Source | SS df MS Number of obs = 289 -------------+---------------------------------- F(1, 287) = 41986.64 Model | 664544048 1 664544048 Prob > F = 0.0000 Residual | 4542496.25 287 15827.5131 R-squared = 0.9932 -------------+---------------------------------- Adj R-squared = 0.9932 Total | 669086544 288 2323217.17 Root MSE = 125.81 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 43.81013 .2138056 204.91 0.000 43.38931 44.23096 _cons | 49.31707 16.96222 2.91 0.004 15.93094 82.70319…4. The estimation of the model with quarterly car sales in the U.S. from 1975 to 1990 gives: Source | df MS Number of obs = 64 F( 2, Prob > F 61) = 12.21 Model .32720224 2 .16360112 0.0000 Residual | .817286587 61 .013398141 R-squared Adj R-squared = 0.2625 Root MSE 0.2859 Total | 1.14448883 63 .018166489 .11575 lqne | cCoef. t P>|t| std. Err. [95% Conf. Interval] 1price lincome -.4604611 3.37186 6.89398 -.8280926 .1838504 -4.50 0.000 -1.195724 2.399991 . 4860261 4.94 0.000 1.428121 _cons 5.92543 .4843662 12.23 0.000 4.95688 Based on the parameter estimates, what is the predicted effect of a 10% increase in price on the number of cars sold? What would be the effect of that price increase on the value of car sales?
- A forecaster used the regression equation Qt= a + bt+q₁D₁ + C2D2 + c3D3 and quarterly sales data for 2004/-2021/V (t = 1, ..., 64) for an appliance manufacturer to obtain the results shown below. Q is quarterly sales, and D1, D₂ and D3 are dummy variables for quarters I, II, and III. DEPENDENT VARIABLE: QT R-SQUARE OBSERVATIONS: 64 0.8768 VARIABLE INTERCEPT T D1 D2 D3 F-RATIO P-VALUE ON F 107.982 0.0001 PARAMETER STANDARD ESTIMATE 30.0 1.5 10.0 25.0 40.0 ERROR T-RATIO P-VALUE 2.34 0.0224 2.14 0.0362 3.33 0.0015 3.47 0.0010 2.53 0.0140 12.80 0.70 3.00 7.20 15.80 In any given year, quarterly sales tend to vary as follows:Please no written by hand The assumption of normally distributed errors means that... A. errors can be ignored when doing regression modelling. B. the OLS estimators can also be assumed to be normally distributed since they are a linear functions of the errors. C. the OLS estimators can also be assumed to be normally distributed since they are BLUE. D. the OLS estimators can also be assumed to be normally distributed since they are minimum variance. E. the regression model will not be subject to specification error.Use the following STATA output to test whether the variable wgt is significant at 5% level: Source | SS df Number of obs = EC 3. Prob > F R-squared MS 392 300.76 0.0000 0.6993 Adj R-squared anba6970 4.2965 388) = Model Juu16656.4443 Residual 162,54916 5552.1481 388 18.4601782 Total Juu23818.9935 391 60.9181419 Root MSE Coef. Std. Err. P>It| [95% Conf. Interval] syl ena wat .2677968 -.012674 -.0057079 44.37096 .4130673 .0082501 .0007139 1.480685 -0.65 -1.54 -8.00 29.97 0.517 0.125 0.000 0.000 -1.079927 -.0288944 -.0071115 41.45979 .5443336 0035465 .0043043 47.28213 _cons The variable is not significant because p-value is less than 0.05. The variable is significant because p-value is less than 0.05. The variable is significant because p-value is less than 0.1. The variable is not significant because p-value is greater than 0.05
- 2. Consider the following estimated regression equation (standard errors in parentheses): Yi-120+ 0.10Ft + 5.33Rt (0.05) (1.00) R² = 0.5 i. ii. iii. where A Yi = the corn yield (bushels/ha) in year t Ft = fertilizer intensity (pounds/ha) in year t Rt = rainfall (inches) in year t Interpret the meaning of the intercept. Suppose you are told that the true value of BF (coefficient on fertilizer intensity) is known to be 0.20. Does this show that the estimate is biased? Why or why not? Suppose you were told that the equation does not meet all the classical assumptions and, therefore, the OLS estimator used is not BLUE. Does this mean that the true BR (coefficient on rainfall) is definitely not equal to 5.33? Why or why not?An estimated regression equation is ŷ = -1 + 2ln (x). Then we can forecast: O a ŷ= -0.386 if x = 2. Ob x= 1 ifŷ=-1. Ocŷ=-1 if x = 1. Od ŷ= 1 if x = 1. Oeŷ=-1 if x = 0.5. The following estimated equation was obtained by OLS regression using quarterly data for 1978 to 1996 inclusive. Yt = 2.20+ 0.104Xt₁ - 3.48 Xt₂ + 0.34Xt3 (3.4) (0.005) (2.2) (0.15) Standard errors are in parentheses, the explained sum of squares was 109.6, and the residual sum of squares 18.48. a. Test at the 5% level for the statistical significance of the parameter estimates. b. Calculate the coefficient of determination.