O E. studies the statistical dependence of an outcome variable on an exogenous variable (or variables) with the view of estimating the average response of the regressand for given values of predictor variables.
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- Suppose that a coffee producing firm estimated the following regression of thedemand for its brand of coffee:Qc = 1.5 − 3.0Pc + 0.8Y + 2.0Pb − 0.6PS +1.2 Awhere Qc = sales of coffee brand C, in dollarsper pound Pc = price of coffee brand C,in dollars per poundY = personal disposable income, in millions of dollars per yearPb = price of the competitive brand of coffee, in dollarsper pound Ps = price of sugar, in dollars per poundA = advertising expenditures for coffee brand C, in hundreds of thousands ofdollars per year.Suppose also that this year, Pc = $2, Y = $2.5, Pb = $1.80,Ps = $1 and A =$1.a. Interpret the results of the estimated demand.b. Compute point price elasticity of demand for the firm’s brand of coffeewith respect to its price.c. Compute the cross-price elasticity of demand for coffee with respect to theprice of competitive coffee brand b.d. At the current price level, would it be viable for the firm to increase the pricelevel of its brand of coffee? Support your answer.…2. Which of the following types of regressions will always have a binary outcome variable? (A) Probit (B) Difference-in-differences (C) Regression discontinuity (D) (A) and (B) will both have binary outcome variablesGive typing answer with explanation and conclusion if education is heavily affected by IQ and IQ is closely correlated with both wage and education level, how would a regression of wage on education be biased? What regression violation is this? (For econometrics)
- 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.1. Let kids denote the number of children ever born to a woman, and let educ denote years of education for the woman. A simple model relating fertility to years of education is kids = 0 + 1educ + u, where u is the unobserved error. (a) What kinds of factors are contained in u? Are these likely to be correlated with level of education? (b) Will a simple regression analysis uncover the ceteris paribus eect of education on fertility? Explain.Consider the IV regression model Yi = β0 + β1Xi + β2Wi + ui, where Xi is correlated with ui and Zi is an instrument. Suppose that the first three assumptions in Key Concept (The IV Regression Assumptions) are satisfied. Which IV assumption is not satisfied whena) Zi is independent of (Yi, Xi, Wi)?b) Zi=Wi?c) Wi is1 for all i?d) Zi=Xi?
- does the simple regression of log (price) on log (nox) produce an upward or a downward biased estimator of β1?Subject: Economics Let ei be the ith residual in the ordinary least squares regression of y on X in the classical regression modeland let εi be the corresponding true disturbance. Prove that plim(ei - εi) = 0What are 2 interpretations of the slope of a regression line?
- If you are interested in investigating themarginal effect of the percentage change of lot size on the changeof house price, then in your linear regression analysis you should apply thelogarithm transformation on: Select one: a. The house price variable. b. Both the house price variable and the lot size variable. c. The lot size variable. d. The bedroom variable. e. Both the bedroom variable and the house price variable.We know that discrimination exists. It influences wages, but also many other dimensions over the life cycle which affect wages indirectly. I run OLS regression with variables wage, age, female and degree. The dependent variable is log(wage) and we replace the variables female and degree with the interaction term. However, discrimination is not included among the observed regressors. Given that omitting confounding variables from regression model can bias the coefficient estimates, omitting discrimination would lead to biased results. Could you please help me provide an example of how unobserved gender discrimination can affect my OLS estimates. [Hint: think about ways in which discrimination can invalidate OLS assumptions.].How should I interpret the coefficients on a regression with a naural log of a dependent variable? Ex: Ln(wage)=B1+B2Experience+B3Male...+Ui