a) Consider a standard log(wage) equation for men under the assumption that all explanatory variables are exogenous. log(wage) = Bo + B1married + Bzeduc + uj i) ii) Explain the problem you would encounter if Cov(educ, u¡) # 0. What are the consequences of applying OLS to estimate the parameters in the model above in the face of the problem identified in (i)? iii) Explain the three main source of this problem. What is an instrumental variable? iv) Suppose Z is identified as an instrument variable for educ in the model above, explain the main conditions that this instrument satisfy for it to be a valid instrument. v) vi) Explain the two-stage least square (2SLS) method that you need to deal with the problem identified in (i). b) Give the following model.

a) Consider a standard log(wage) equation for men under the assumption that all explanatory variables are exogenous. log(wage) = Bo + B1married + Bzeduc + uj i) ii) Explain the problem you would encounter if Cov(educ, u¡) # 0. What are the consequences of applying OLS to estimate the parameters in the model above in the face of the problem identified in (i)? iii) Explain the three main source of this problem. What is an instrumental variable? iv) Suppose Z is identified as an instrument variable for educ in the model above, explain the main conditions that this instrument satisfy for it to be a valid instrument. v) vi) Explain the two-stage least square (2SLS) method that you need to deal with the problem identified in (i). b) Give the following model.

Calculus For The Life Sciences

2nd Edition

ISBN:9780321964038

Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Chapter4: Calculating The Derivative

Section4.EA: Extended Application Managing Renewable Resources

Problem 2EA

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Table 2 shows a recent graduate’s credit card balance each month after graduation. a. Use exponential regression to fit a model to these data. b. If spending continues at this rate, what will the graduate’s credit card debt be one year after graduating?
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Q3 a) A researcher wants to find out what are the factors which determine the number of installs (I) of an application (app) from a famous app store. Size in Mbs (S), Reviews in *000s (Re), Ratings (0 to 5) (Ra), Price in 'Rs (P). She ran the following regressions: log I = 1.329 + 0.2356 S+0.4320 log(Ra) – 0.2678 P + 1.928 log(Re) Se = (0.63) R? = 0.734 (0.242) (1.29) (0.001) (0.156) df = 156 i. Interpret the regression above. ii. Test for statistical significance of Price in the model. Depending on the result do you suggest that price is a significant factor affecting app installation? iii. Suppose the regression is re-estimated where number of installs (I) varies only with respect to price (P). Average I in sample is 5 and average P is Rs 8.9. Following regression was estimated: I = 52.351 + 3.139 - se = (37.39) (0.0187) df = 156, R? = 0.806 How would you interpret this model? Explain the shape of the curve. iv. What would be the slope and elasticity of number of installs with…
Q3 a) A researcher wants to find out what are the factors which determine the number of installs (I) of an application (app) from a famous app store. Size in Mbs (S), Reviews in *000s (Re), Ratings (0 to 5) (Ra), Price in 'Rs (P). She ran the following regressions: log I = 1.329 + 0.2356 S+0.4320 log(Ra) – 0.2678 P + 1.928 log(Re) Se = (0.63) R? = 0.734 (0.242) (1.29) (0.001) (0.156) df = 156 i. Interpret the regression above. ii. Test for statistical significance of Price in the model. Depending on the result do you suggest that price is a significant factor affecting app installation? iii. Suppose the regression is re-estimated where number of installs (I) varies only with respect to price (P). Average I in sample is 5 and average P is Rs 8.9. Following regression was estimated: Î = 52.351 + 3.139 - 1 se = (37.39) (0.0187) df = 156, R? = 0.806 How would you interpret this model? Explain the shape of the curve. iv. What would be the slope and elasticity of number of installs with…
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log SeriousCoalAccidents) = Bo + Bilog (CoalProduction) + B2log(EmployeeCount) + ɛ. The results are shown below. . reg In_ser i ous_coal In_coal _pr oduct i on In_avg_empl oyee_cnt, robust Li near regr essi on Nunber of obs 17, 784 F( 2, 17781) Pr ob > F 4561. 54 0. 0000 0. 5541 . 4085 R squar ed Poot MSE Robust In_seri ous_coal Coef fi ci ent st d. er r. [ 95% conf. i nt er val ] In_coal_pr oduct i on 0562793 . 0015246 0. 000 0. 000 0. 000 36. 91 0532908 0592677 In_avg_empl oyee_cnt 1036132 0030433 34. 05 .097648 1095784 _cons -. 0321483 0014046 - 22. 89 -. 0349014 -. 0293951 What is the interpretation of the results? A. One additional worker is associated with 0.104 more serious accidents. B. One percent additional workers is associated with 0.104 percent more serious accidents. C. One additional worker is associated with 10.36 percent more serious accidents. D. One percent additional workers is associated with 10.36 more serious accidents.
/3 89. sec²0 d0
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