In the model Yi = b0 + b1*Xi + ui, Xi can take values 1 or 2. If E(ui | Xi = 2) > E(ui | Xi = 1), the OLS estimate of b1 is consistent True False
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- When estimating the equation: i Acrimerate =B+B Aunemployment + Au, i 0 The estimate of B, is only causal if there is no correlation between Acrimerate; 1 True False and Au; iY = AK1/3L2/3; Y = 30, K = 10, L = 20. The TPF is A. 1.89 B. 0.89 C. 1.27 D. 1.05TRUE OR FALSE. When heterosdasticity is present OLS estimators are still BLUE.
- The Drell Yan process is the production of charged lepton pairs in hadron-hadron collisions, It takes place when a quark from one hadron and an anti-quark from another hadron annihilate to produce a sinigle virtual photon, which subsequently produces a pair of oppositely charged leptons. Estimate the ratio of the Drell-Yan cross-sections o(w*n -u")/o(x*p-+u*). Choose 1 option.Problem 2 In economics we are often faced with causal problems where the endogeneity arises because of simultaneity. A classic example is that if you are interested in estimating a demand curve, the issue is what you observe in the data are equli- birum and prices and quantity which is not only a function of demand but also a function of supply. In this excercise we will generalize the problem of simul- taneity bias. Consider a situation where we are interested in the effect of T; on Y₁, i.e. obtain- ing a consistent estimate of a. But Y; also effects T₁. Let X; be some exogenous 1 covariates affecting both Y; and T₁. Let us imagine you have an exogenous vari- able Z which only effects T; but does not directly effect Y₁. In particular, consider the following structural equations: = Y₁ Ti where E (u; | X₁, Z;) = 0 and E (v; | Xį, Zi) = 0 aTi + Xiß + ui pY; + X₁Y+Z₁8+ Vi (1) (2)If MLR 1-4 hold, then OLS estimators are correct are unbiased and inconsistent equal to the population parameter unbiased and consistent
- As a manager of a small software retailing company, you are concerned with projected profit next year. While profit can be determined as the difference between sales and maintenance cost, or in symbols, P = S - M, where P is profit, S is sales, and M is maintenance cost including technical support. It is argues that when sales goes up so does maintenance cost because the cost of technical support will go up. Further, it is measured that the correlation between S and M is 0.8. Now given the figure that sales next year is expected to be $300 thousand with standard deviation of $4 thousand and maintenance cost is expected to be $150 thousand with standard deviation of $6 thousand, what would be the expected profit and its standard deviation you will include in your report?b. A regression on the original regressors, q₁² and a constant term yields the following statistics: R2 0.296041 F = 1.177507 coeff of q² has a t-statistic of 2.876 (i) With this information, which test can you implement to deal with the problem omitted variables and why? (ii) Implement the test as stated in b(i) and interpret the results. (iii) What is (are) the consequence(s) of the problem alluded to above on the estimators?(20.7) (1.35) Scenario 38-1. Consider the following OLS model applied to the data for workers wages and years of schooling, where e; stands for the residual: WAGE = -10.7+ 3.16 x SCHOOL + e. Refer to Scenario 38-1. Suppose that the residual term has a zero mean and is uncorrelated with the independent variable. Suppose that the standard error is 1.35. This means that we can be 95 percent confident that the true wage benefit of a year of schooling 000 is equal to $8. is equal to $0.46. lies between $0.46 and $5.86. lies between $8 and $13.4.
- CEVAX)= B_0+B 1) X) does not imply w ich of the followin: Select one: O a. 1 unit increas of X changes the expected valu of Y by the amount of B, O b. the averag valu of Y changes withX O c. the equation is a population regression funcicn d. (Y=B0+B.1VX for all units in the population Clear my choiceUse 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 0.05With Panel Data, if we assume that the individual effects vi are not correlated with the regressors Xit (i.e. E(vi|Xit) = 0), which one of the following statements is correct: The Fixed Effects estimator is not consistent. Both the OLS and the Random Effects estimators are not consistent. The OLS estimator is not consistent, but the Random Effects estimator is consistent. The OLS and the Random Effects estimator are consistent. All of the above. None of the above