Econometrics

What is an example of a research question in Labour Ecoomics

Does Obtaining a Master’s Degree increase future wages

What is the difference between causation and correlation

Correlation is co-movement, causation means one variable impacts another

What is an example of a “counterfactual”

The birthweight of a baby if the mother had not smoked

What is “centris paribus” is econometrics

Holding all other factors constant while changing one

Why are randomized controlled trials (RCTs) often considered the gold standard to detect causal effects

They change one random variable while holding the others constant

What is the difference between a sample and a population distribution

A population sample includes all distributions while a sample is based on the subset

Which is an example of a discrete variable

Number of Children

Which data set consists solely of observations taken at a single point in time?

Cross-Sectional Data

Which of these best describes a histogram

A method to display the contribution of continuous variables

What is the purpose of summarizing a distribution?

To describe key features like central tendency and dispersion

What does the central limit theorem (CLT) state

The sampling distribution of the mean approaches a normal distribution

What happens to the mean and median if there is a very large outlier

The mean is more effected than the median

If X and Y are independent, it means that

Learning the value of one tells you nothing about the other

Id X and Y are positively related, what does this imply about the conditional expectatio E(Y|X)

As X increases, E(Y|X) increases

2.5

What is the main idea behind binning when calculating conditional means

To group X values into bins and calculate the average of Y within the bin

In linear regression, if we add a control variable Z, what does the coefficient B1 on the X variable represent

The part of the relationship between X and Y that is not explained by Z

What does the simple linear regression model explain

The dependent variable in terms of the explanatory variable

What does the R² value measure in a regression model

Fraction of total variation in outcome (y) explained by regression

Which of the following statements about R² is TRUE

It measures the goodness-of-fit but does not imply causality

What does the assumption of homoskedasticity imply

The variance of the error term doesn’t depend on the explanatory variable

What happens if the zero conditional mean assumption does not hold

OLS estimates are biased

The OLS regression can be estimated even if an explanatory variable has no variation in the sample

False

Given the OLS regression, which of the following best represents the defiiion of a residual for the i-th observation

The difference between yi and the predectied yi(hat) from the OLS regression

Using the following simple OLS regression, we want to analyze the impact of height on wage the proposed model is…

Linear in parameters and nonlinear in variable

What is the main reason behind using multiple regression instead of a simple linear regression model

It allows to account for multiple explanatory variables

The population regression model is given and E(u|x1,x2) = 0, if x2 is omitted, this causes a bias. When is the Bias 0

a. when the omitted variable x2 has no effect on y that is if b2 =0

b. when the omitted variable x2 is uncorrelated with x1 (e.g: cov(x1,x2) = 0)

c. When the omitted variable x2 is included in the regression model again

d. all of the above

All of the above

When does omitting x2 lead to a bias of B1(hat). Assuming that E(u|x1, x2)=0 still holds

a. when B2 > 0 and cov(x1,x2)>0

b. when B2 < 0 and cov(x1,x2) > 0

c. when B2 > 0 and cov(x1, x2) < 0

d. none of the above options

a. When B2 > 0 and Cob (x1,x2) > 0

What happens to the sim of squared residuals (SSR) when more explanatory variables are added to the regression model

SSR decreases as predicted values get closer to actual values

Why is the zero conditional mean assumption important in multiple regression

It allows for a causal interpretation of the estimated regression coefficients

What does the assumption of homoskedasticity imply in a multiple regression model

The error term’s variance is constant across all explanatory values

What does the Gauss-Markov Theorem state about the OLS estimators under assumptions MLR1-MLR5

They are the best linear unbiased estimators

In statistical hypothesis testing, what does the null hypothesis(H0) represent

A claim that the parameter equals a specific hypothesized value, typically zero

In statistical hypothesis testing, it is possible to accept the null hypothesis (H0) or the alternate hypothesis (H1)

No, we do not accept hypotheses— we either reject H0 or fail to reject it

What is the importance of the normality assumptuon in regression analysis

It is necessary for valid hypothesis testing, especially in small samples

True or false: For the purposes of statistical inference, the assumption of normality can be replaced by a large sample size

True

In statistical hypothesis testing, what does the t-statistic measure

How many standard error the estimated coefficient is away from the hypothesis value

What does the F-test evaluate in a regression model

Whether multiple coefficients are jointly significant

What does a low p-value (eg: < 0.05) indicate in hypothesis testing

Strong evidence against the null hypothesis

How are the p-value and t-statistic related

A higher t-statistic generally leads to a lower p-value

What does a confidence interval represent?

A range of values where the population parameter is likely to be found

What value does a dummy variable typically take to indicate the presence of a characteristic

Any number between 0 and 1

In the model shown above, what does B1 represent

The average wage difference between females and males

Given the model above, what is the predicted wage for a man with 12 years of education

6.1

if male = 1 - female, what kind of issue does this cause in a regression if both variables, male and female, are included

Perfect colinearity

Why should you avoid dummy variables for all categories of a qualitative variable

it causes the dummy variable trap

In the interaction model above what is the return to education for females

sig1 + sig_int

In a linear probability model with an outcome y, a slope coefficient of -0.03 for x means. An increase in x…

… by once unit reduces the probability that y occurs by 3 percentage points