Suppose we are interested in the effect of education on salary as expressed in the following model. Salary₁ = Bo + B₁ Education, + €₁ For this problem, we are going to assume that the true model is Salary; = 12,000 + 1,000 Education, + €¡ The model indicates that the salary for each person is $12,000 plus $1,000
Suppose we are interested in the effect of education on salary as expressed in the following model. Salary₁ = Bo + B₁ Education, + €₁ For this problem, we are going to assume that the true model is Salary; = 12,000 + 1,000 Education, + €¡ The model indicates that the salary for each person is $12,000 plus $1,000
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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
Transcribed Image Text:Suppose we are interested in the effect of education on salary as expressed in
the following model.
Salary; = Bo + B₁ Education, + €₁
For this problem, we are going to assume that the true model is
Salary; = 12,000 + 1,000 Education; + €¡
The model indicates that the salary for each person is $12,000 plus $1,000
times the number of years of education plus the error term for the individual.
Our goal is to explore how much our estimate of ₁ varies. The do file
salary_simulation.do will simulate a data set with 100 observations (right click
to save the do file). Values of education for each observation are between 0
and 16 years. The error term will be a normally distributed error term with a
standard deviation of 10,000. What does this simulation show about the least
squares estimators?
Select one:
O a. Unbiasedness
O b. Consistency
O c. Efficiency
O d. Nonlinearity

Transcribed Image Text:/* Overview of simulation code
the "simulate" line runs the code 1000 times (as determined in the "reps (1000)" command)
Stata will save the beta coefficients for each simulation.
- the values of coefficient on Education for each simulation are listed in a variable called "_b_Ed"
- the values of the constant for each simulation are listed in a variable called "_b_cons". */¯¯
clear all
program OLS_Sim
clear
set obs 100
gen Ed=16*runiform()
scalar SD = 10000
gen Salary = 12000 + 1000* Ed + SD*rnormal() /* Generate salary (dep. variable) */
regress Salary Ed
/* Run regression */
/* Set sample size */
/* Generate education (ind. variable) drawn from the uniform distribution between 0 and 16*/
/* Set value of standard deviation of error term */
end
simulate _b, reps (1000): OLS_Sim /* Run simulation 1000 times */
summarize /* Summarize coefficient estimates for each simulation */
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