Using a sample from a population of adults, to estimate the effects of education on health, we run the following regression: hypertensioni = a + Beduc₁ + YX¡ + Ei where hypertension is a dummy variable equals one if a person suffers from hypertension and zero otherwise, educ is years of schooling, and X is a vector of demographic variables such as age, gender, and ethnicity. (a) Show that educ in the regression above is likely to be endogenous and discuss the consequences of this on the OLS estimators. (b) Evaluate whether a government policy that requires children to complete twelve years of schooling is good instru al variable for adu

Using a sample from a population of adults, to estimate the effects of education on health, we run the following regression: hypertensioni = a + Beduc₁ + YX¡ + Ei where hypertension is a dummy variable equals one if a person suffers from hypertension and zero otherwise, educ is years of schooling, and X is a vector of demographic variables such as age, gender, and ethnicity. (a) Show that educ in the regression above is likely to be endogenous and discuss the consequences of this on the OLS estimators. (b) Evaluate whether a government policy that requires children to complete twelve years of schooling is good instru al variable for adu

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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A5
A website that rents movies online recorded the age and the number of movies rented during the past month for some of their customers. The data are shown below for a random sample of 25 of their customers.The regression line for the data, with number of movie rentals as the response variable, provides an intercept = 18.87, and slope = -0.228. The standard error of the slope SE(b1) = 0.0827. Margin of error ME for a 99% Confidence Interval for the slope of the Population regression line is: 0.1161 0.2322 0.4644 0.3483
"In the regression model InY=b0+b1*InX+u, the coefficient b1 is interpreted as" O the intercept O A covariance O A regressor O An elasticity
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The measure of standard error can also be applied to the parameter estimates resulting from linear regressions. For example, consider the following linear regression equation that describes the relationship between education and wage: WAGE: = Bo + B₁ EDUC; + &i where WAGE; is the hourly wage of person i (i.e., any specific person) and EDUC; is the number of years of education for that same person. The residual ₂ encompasses other factors that influence wage, and is assumed to be uncorrelated with education and have a mean of zero. Suppose that after collecting a cross-sectional data set, you run an OLS regression to obtain the following parameter estimates: WAGE;= -10.7+ 3.1 EDUC; If the standard error of the estimate of B₁ is 1.04, then the true value of B₁ lies between grows, you would expect this range to in size. and . As the number of observations in a data set
To determine whether the revenue made trom selling ice creams in a smal shop (in USD) and the temperature (in Celcius degrees) are related, a researcher decides to employ the simple linear regression analysis. Using a random set of days in which the temperature varies between 20 and 35, she comes up with the following regression line: Ice Cream Revenue- 200 90 Temperature Based on this analysis, what can she conclude? O A)At 20 Celcius degrees. the shop owner can xpect to sell 5000 USD O B)AI 30 Celoius degrees, the shop owner can expect to sel 2000 usD O C)As the temperature goes up by one degree Celoius, the revenue increases by approximately 00% OD)On average ce cream revenue is about 200 USD when the temperature is O degrees Celcus O E)As the temperature goes up by one degree Celcius. the reverue inereases by approximately 200 USD.
A researcher wants to study the determinants of crime in Turkey. For this she has data 81 Turkish provinces over 17 years. She estimates by OLS the following regression crmpc = Bo + D; + B1polpc + a;+U where crmpc is the crime rate per head of population, polpc is the number of police officers per 1000 individuals. The parameter D, refers to year fixed effects and is one in yeart and zero for all other years. a, is the unobserved province fixed effect and u, is a random unobservable term (idiosyncratic shock). The First Difference estimator (Check all that apply). Your answer: O allows covariance between u, and polpc does not allow covariance between a, and polpc allows covariance between a, and polpc does not allow covariance between u, and polpc O O
The data for this question is given in the file 1.Q1.xlsx(see image) and it refers to data for some cities X1 = total overall reported crime rate per 1 million residents X3 = annual police funding in $/resident X7 = % of people 25 years+ with at least 4 years of college (a) Estimate a regression with X1 as the dependent variable and X3 and X7 as the independent variables. (b) Will additional education help to reduce total overall crime (lead to a statistically significant reduction in crime)? Please explain. (c) Will an increase in funding for the police departments help reduce total overall crime (lead to a statistically significant reduction in total overall crime)? Please explain. (d) If you were asked to recommend a policy to reduce crime, then, based only on the above regression results, would you choose to invest in education (local schools) or in additional funding for the police? Please explain.
Having successfully completed your first year in university, you began your second year with an evaluation of your past performance. You observed that you performed well in those subjects where you were diligent with class attendance whilst you performed poorly in those courses where you missed a number of classes. Upon learning about OLS regression you realize that you are able to predict your average performance based on the number of classes attended. The table below shows your data set. Number of Lectures (X) Percentage Scored (Y) 1 30 2 45 3 51 4 57 5 60 6 65 7 70 8 71 9 72 10 73 11 66 12 71 13 47 14 81 15 83 16 84 17 89 18 99 19 82 20 86
You extract approximately 5,000 observations from the Current Population Survey (CPS) and estimate the following regression function: AHE= 3.32 -0.45 x Age, R² =0.02, SER= 8.66 (1.00) (0.04) where AHE is average hourly earnings, and Age is the individual's age. Given the specification, your 95% confidence interval for the effect of changing age by 5 years is approximately: OA. [$2.32, $4.32]. B. [$1.86, $2.64]. C. [$1.35, $5.30]. D. Cannot be determined given the information provided.
Suppose you work for North Dakota DNR Grand Forks office. DNR would like to know whether they should set aside some conservation land, previously slated to be logged, for a potential state park. You are helping do a travel-cost analysis to estimate the benefits of the set-aside. You collected data from 500 visitors who came to a state park in a neighboring state. You ran regression analysis and controlled for these visitors' age, income, education, employment status, and other important factors that might affect the number of visits. With all the information, you have developed the following relationship: (a) (b) Cost to Visit $20 $40 $80 # of Visits Per Person Per Year 8 6 2 Graph the demand curve for the number of visits as a function of the "price" -- the travel cost. Based on demographic information about the people living in the vicinity of the proposed park, you have estimated that 10,000 people will take an average of 4 visits per year. For the average person, calculate: (1) The…
In the regression equation, what is B0? Group of answer choices the population slope the sample y-intercept the sample slope the population y-intercept