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What is the Role of Control Variables in Multiple Regression?
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- Kristin Forbes in her American Economic Review (2000) article investigates the relationship between economic growth and inequality. She uses five yearly data for 45 countries for the time period 1965-1995. In the table below are results of her using four types of panel regression estimation techniques for the same model, where she estimates the relationship between economic growth and inequality (measured by the Gini coefficient) Estimation method Inequality Income Male Education Female Education PPP R² Countries. Observations Period Fixed effects (1) 0.0036 (0.0015) -0.076 (0.020) -0.014 (0.031) 0.070 (0.032) -0.0008 (0.0003) 0.67 45 180 1965-1995* Five-year periods Random effects (2) 0.0013 (0.0006) 0.017 (0.006) 0.047 (0.015) -0.038 (0.016) -0.0009 (0.0002) 0.49 45 180 1965-1995 Chamberlain's 77-matrix (3) 0.0016 (0.0002) -0.027 (0.004) 0.018 (0.010) 0.054 (0.006) -0.0013 (0.0000) 45 135 1970-1995 Arellano and Bond (4) 0.0013 (0.0006) -0.047 (0.008) -0.008 (0.022) 0.074 (0.018)…Suppose you run the following regression: outcome=alpha0 + alpha1*female + alpha2*married + epsilon. You know that female equals 1 for females and 0 otherwise. You know that married equals 1 if the person is married and 0 otherwise. What is the estimated outcome for non-married females?courses / Faculty Of Economics & Administrative Sciences / ECON309 / Finals / ECON 309 Final Exam A 4. The following types of statistical inference are used throughout econometrics, with the exception of O a. calibration. O b. estimation. Oc. confidence intervals. O d. hypothesis testing. Next
- 5.2 42 The following regression shows the impact of the number of rooms (ROOM) on the monthly rent of an apartment (in thousand Rands) (RENT) from a sample of 42 randomly chosen apartments in Parow: Dependent Variable: RENT Variable C ROOM r-square: 0.7180 Parameter -0.7705 2.1331 SE of regression: 2.3397 Std. Error 0.8233 0.2114 sample size = 42 What will be values of the parameters, variance and standard errors of the parameters, estimated variance of error terms, coefficient of determination and correlation coefficient, if RENT is measured in Rands and there is no change to the unit of measurement of the ROOM variable?QUESTION 2 Continue to use the example from Question 1. Suppose each product is randomly assigned to a process by a computer program, but some products get reassigned on the factory floor (for practical reasons). Let Z¡ denote the original assignment and X¡ the actual process used to produce i. In a regression of Y¡ on X¡ and Wj, OLS is: d. Potentially biased because W; should not be included b. Potentially biased, but an IV regression using Z¡ as an instrument can be used to obtain a consistent estimator C. Unbiased because the products were randomly assigned in the beginning d. Unbiased as long as Zj is also included as a control variable(Econmetrics) Q.1 How can you test for general misspecification of model if it would have only (any of) two independent variables?
- Suppose you have run four regression models: A, B, C, and D. You are going to make a decision on which one to use just based on the adjusted r² value. Here are the adjusted r² values for each model: A: 0.71 B: 0.57 C: 0.65 D: 0.76 Which regression model would you choose based on the adjusted r²? OD since it has the highest adjusted r² value B since it has the lowest adjusted r² OC since it has an adjusted r² between the adjusted r² of regressions B and D. Either B or C since they have the lowest adjusted r²Please answer fastList the 5 assumptions of the Classical Linear Regression Model and explain at least three of them
- Children who were breastfed as infants have higher average IQs on average than children who were not. Breastfeeding advocates claim that this is due to the superior nutrition in breastmilk including brain building chemicals such as DHA. You have been hired to investigate this relationship more fully. Your employers are worried about the possibility of omitted variables biasing the results of the study. Name 3 possible omitted variables and explain how they might bias the results. Explain 2 empirical strategies you might use in your investigation.Using data from a random sample of 1000 working adults, we obtain the following estimated regression to study the effect of experience (exper) on log of wage (log(wage)). log(wage) = 5.423 -0.034ezper +0.009ezper² + 0.082educ + 0.157male What other regression do you need to run to test the null hypothesis that, holding other factors fixed, experience has no effect on log(wage)? Explain what test you would perform.Please no written by hand solution a) Suppose in a regression of weekly salaries on years of schooling for males(m) and females(f), the following results are obtained. Wm = 50Sm and Wf = 40Sf. where Wm (Wf) denotes weekly salary and Sm (Sf) denotes years of schooling for males and females respectively. 50 and 40 are the coefficients on schooling in the male and female regression respectively. On average, men have 12 years of schooling and women have 10 years of schooling. What is the average male-female wage differential? Is this a good estimate of discrimination? Explain why/why not. Using the information in the question, what would you propose as a better estimate of discrimination? State any assumptions that you use and explain your answer.