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![Questión
Consider the following model:
cigarette, = Bo + Bi price, + e,
with price the price of cigarette in state s in USD and cigarettes is the num
Bi represents
[ Select )
v and,
Bo represents
[ Select )
Question 2
Consider the following model:
cigarette, = Bo + Bi male; + e
with male is a binary variable taking 1 if the individual is a man and 0 ot
Bi represents [Select ]
Bo represents ( Select]](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F388b9ac0-efa3-40fc-b7da-89882719f55a%2Fb33a25be-c996-42dd-9dfb-87862dbed05e%2Fcb4zku_processed.jpeg&w=3840&q=75)
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- Investigate what factors determine the number of times a person logs into Facebook per week. It is argued that these four factors are important: number of friends, age in years, whether the person is employed, and whether the student has a Twitter account. That is: FACEBOOK LOGIN=f(FRIENDS,AGE,EMPLOYED,TWITTER) Do you think other relevant explanatory variables should also be included? Name any two such variables and explain why they should be included in the regression.(Econmetrics) Q.1 How can you test for general misspecification of model if it would have only (any of) two independent variables?look at the influence of mileage on the sale price of each brand ofautomobile. Postulate the following statistical models,?_ℎ????? = ?0 + ?1?????? + ?? and ?_?ℎ??? = ?0 + ?1?????? + ?? with ??~?(0, ?2).For each model (‘model_honda’ and ‘model_chev’) perform the following,Comment on the miles variable after examining it with hist() and summary() commands.What does the assumption of non-random independent variable imply about how thedata must be collected if the same miles variable is used for both brand of automobile?
- We have a random sample of workers from a large firm. In 2017, the firm ran a training program. Some workers did the training program, others did not. The firm now wants to assess the effect of the training on earnings. We use the following model to estimate the effect of a training program on annual earnings in 2018: ln(earn2018)=β0+β1train+β2ln(earn2016)+β3educ+β4exper+u where earn2018 = individual total annual earnings in 2018 in dollars train = a dummy variable that takes the value 1 if the individual worker did the training in 2017 and 0 otherwise earn2016 = individual total annual earnings in 2016 in dollars educ = the individual's years of education exper = the individual's years of experience We find: ln(earn2018)^= 11.672 + 0.041train + 0.821ln(earn2016) + 0.037educ + 0.012exper (5.864) (0.019) (0.258) (0.013) (0.007) n=1278, R2= 0.048 Which of the following is the correct interpretation of the…We have a random sample of workers from a large firm. In 2017, the firm ran a training program. Some workers did the training program, others did not. The firm now wants to assess the effect of the training on earnings. We use the following model to estimate the effect of a training program on annual earnings in 2018: ln(earn2018)=β0+β1train+β2ln(earn2016)+β3educ+β4exper+u where earn2018 = individual total annual earnings in 2018 in dollars train = a dummy variable that takes the value 1 if the individual worker did the training in 2017 and 0 otherwise earn2016 = individual total annual earnings in 2016 in dollars educ = the individual's years of education exper = the individual's years of experience Now, I want to test whether the effect of an additional year of education increases earnings by twice as much as an additional year of experience. My null hypothesis is H0:β3=2β4. To get the standard error I need to conduct this hypothesis test, I rearrange or re-parameterise…What is Heteroscedastic? And is there a way to fix data in case of heteroscedasticity presence ?
- 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.].Without running any additional tests, what can you say about the joint significance of all independent variables in the model? Which statistic(s) helps you to answer this question?Suppose we use the linear probability model to estimate the effect of household income on the willingness to avail a loan product. Given a regression equation: Willingness = 22.34 -0.04HouseholdIncome, with HouseholdIncome measured in hundreds of pesos. How is -0.04 interpreted?
- (Don't accept answers from Chat-GPT)You are estimating the following simple linear regression model: Edui = B0 + B1 MomEdu + ui. Where Edu is the years of schooling of an individual and MomEdu is the years of education of the individual's mother (Note: We might estimate this sort of regression to learn about intergenerational transmission of economic success.) a. Suppose you restrict your sample to individuals with MomEdui = 10 What happens to the OLS estimates? b. Suppose you have two random samples of size 100, both with the same In the first sample, half of the mothers have 12 yearsof education and half have 14 years of education. In the second sample, one quarter of of the mothers have each of 10, 12, 14. and 16 years of education. Does the variance of the OLS estimator differ between the two samples? Explain why or why not. C. Suppose you estimate the above regression using a random sample of 100 observations. Then you find another random sample of 100 with the same as the…The regression table from STATA and the table that I created by looking at the values from STATA are attached. Question: Run a separate regression for each measure of social preferences (risk-taking, patience, trust) and compare whether subjective measures of individual’s math skills, their gender and age are important determinants. Summarize the results of the regressions in a table (each column representing one regression). Interpret the estimated coefficients and provide an intuition of what you have found out.Mr. John operates a medium size business that sells tires. He buys most of his tires from a company that is located in South America. Mr. John believes that he is stocking too much tires so he decided to look into the situation. He wants to use the Economic Order Quantity (EOQ) model to manage his stock of tires. In order to use this model, he must first of all forecast the annual demand for his tires. Using a numerical example, demonstrate to Mr. John how he can use the manual trend projection method of forecasting to forecast demand for the next two years.