Midterm

Question 1

Right vs left skewed distribution

Answer 1

Right = mode, median, mean
Left = mean, median, mode

Question 2

What is the variance

Answer 2

The arithmetic average of the squared differences of the data values of the mean

Question 3

How to calculate standard error

Answer 3

Standard deviation divided by the square root of the sample size

Question 4

What is standard deviation

Answer 4

Describes the spread o f values in a continuous distribution - a sample or population
It is used as a descriptive statistic

Question 5

What is a standard error

Answer 5

Is used to measure the accuracy of a sample distribution in representing a population

Question 6

How do you calculate confidence bounds

Answer 6

Mean plus minus (t value * standard error)

Question 7

How to calculate standard error of the difference

Answer 7

Root of (se1 squared plus se2 squared)

Question 8

How to calculate a chi square

Answer 8

Sum of ((observed frequency minus expected frequency)squared) divided by expected frequency

Question 9

What does the chi square tell us about the null hypothesis

Answer 9

If the chi square is larger than the critical value of the degrees of freedom, the null hypothesis can be rejected

Question 10

Explain type I vs type II error

Answer 10

Type II error is one where you fail to reject the null hypothesis when you should
Type I error is one where you reject a null hypothesis when you shouldn’t

Question 11

What is the central limit theorem

Answer 11

Establishes that means of repeated large samples are normally distributed even when underlying distribution of the data is not normal

Question 12

What is a confidence interval?

Answer 12

Best guess of finding the mean of a data set and the confidence that it lies somewhere in the desired interval (generally use 95)

Question 13

Pearson’s correlation coefficient

Answer 13

Measure the association between two continuous variables
R is scaled between -1 and 1
R= covariation of x and y

Question 14

Correlation vs regressions

Answer 14

Correlation tells us how strongly associated two variables are
Regression can tell us on average how much a one unit increase in the independent variable changes the predicated value of the dependent variable

Question 15

What does the line of best fit do

Answer 15

Minimizes the Y distance from each observation to the line

Question 16

Why do we use y hat and how does it differ

Answer 16

Y hat means we are producing estimated y values
In actual values of y we need the error term so y=a+bXi+ei

Question 17

Standard error of the slope

Answer 17

Given by the root mean square error over standard deviation

Question 18

T ratio

Answer 18

t= (b - ßH0)/s.e.

Question 19

What are the five OLS assumptions

Answer 19

Linearity
Mean independence
Homscedasticity
Uncorrelated disturbances
Normal disturbance

Question 20

Explain linearity

Answer 20

Linearity - the dependent variable is a linear function of the x’s plus a population error term ex. yea+ß1x1+ß2x2+e

Pertains to linearity in the parameters

Question 21

Explain mean independence

Answer 21

Zero conditional mean

The mean value of error does not depend on any of the x’s
Assume that e(€)=0

Most important because violations 1. Can generate large bias in the estimates and 2. Cannot be tested for without additional data

Omitted variable bias
Endogenous bias
Measurement error

Question 22

Explain homoscedasticity

Answer 22

The variance of the error cannot depend on the x’s
standard deviation squared is constant
You want homoskedacity
P value has to be >0.05

Non constant variance
Biases the standard errors

Question 23

Explain uncorrelated disturbances

Answer 23

Teh value of the error for any observation is uncorrelated with the value of the error for any other observation

Correlated errors can arise from connected observations, causal effects, or serial correlation

They shrink standard errors, observations are assumed to be more independent that they are, type 1 error danger

Question 24

Explain normal disturbance

Answer 24

The disturbances ,e, are distributed normally

Only the disturbances not the variables must be normally distributed
Normality is the least important assumption

Question 25

How are the OLS assumptions related

Answer 25

1+2 are unbiased estimators 3+4 are BLUE (best linear unbiased estimator) and standard errors are at least as small are those produced by any other method 5 implies that a t table of z table can be used to calculate p values

Question 26

What does the dummy variable do

Answer 26

Helps with comparison of the means of y for different categories of x

Question 27

Collider bias

Answer 27

Occurs when a treatment (independent) variable and outcome (dependent) variable or factors causing these each influence a common third variable and that variable (the collider) is controlled for by design or analysis More general form of selection bias

Question 28

Post treatment bias

Answer 28

While omitting relevant covariates can lead to omitted variable bias, including covariates that control for you causal mechanism can result in post treatment bias

Question 29

What is multicollinearity and what are the consequences

Answer 29

A situation where more than two explanatory variables in a multiple regression are highly linearly related Does not bias the estimation of your coefficient estimates Inflate standard errors of highly colinear variables Induce unstable estimates

Question 30

which OLS assumptions do time series tend to violate

Answer 30

Mean independence and the independence of errors

Question 31

What is stationarity

Answer 31

A time series is weakly stationary if it’s mean and variance remain constant over time

Question 32

What is a dummy variable

Answer 32

A variable that is coded 1 or 0

Question 33

Regression outlier

Answer 33

An observation where the dependant value y is unusually extreme given its independent value x

Question 34

In which direction is the ß1 biased

Answer 34

ß2>0 and corr(x1,x2)>0 positive ß2>0 and corr(x1,x2)<0 negative ß2<0 and corr(x1,x2)>0 negative ß2<0 and corr(x1,x2)<0 positive

Question 35

Interpret the different long linear relationship

Answer 35

Level level : y=a+ßx one unit change in x leads to a ß unit change in y Log linear: log(y) = a+ßx one unit change in x leads to a 100*ß change in y Linear log : y=a+ßlog(x) one percent change in x leads to a ß/100 unit change in y Log log : log(y)=a+ßlog(x) one percent change in x leads to a ß percent change in y

Question 36

How do we interpret shared terms in non linear regression

Answer 36

If b2 is negative and b3^2 is positive then y is convex (smiley) If b2 is positive and b3^2 is negative then y is concave (frowny)

Question 37

What does a time counter do

Answer 37

It draws out the trends in a time series data

Question 38

What is a unit root

Answer 38

How much of y is explained by the previous y The y is almost exactly the same as it’s previous value Also known as a random walk

Question 39

Random walk

Answer 39

Same value today as yesterday with just a bit of randomness

Question 40

Weakly dependent time series

Answer 40

Covariance stationary time series is weakly dependent if the correlation between x1 and x1+h goes to zero sufficiently quickly as h increases

Question 41

What are the two types of panel data

Answer 41

True panels - longitudinal data measuring the same units repeatedly over time pooled cross sections - random surveys in multiple years with a new random sample each time

Question 42

pooled cross sections

Answer 42

Advantages Are amenable to OLS with only minor complications Increased sample size increases accuracy of estimators and adds statistical power Pitfalls Distributions may change in different years Panel heteroskedaciity

Question 43

Fixed effects/within model

Answer 43

Subtract off the mean value of each group from each observation in a group Equivalent to adding a dummy variable for each group Super power- yields within estimation in which only the variation within groups is used for coefficients

Question 44

What is persistence

Answer 44

Persistence in time series Evers to the continuity of an effect after the cause is removed Often related to the notion of memory properties of time series Has an effect on standard errors and can lead to false positives and negatives If the effect of infinitesimally small shock will be influencing in future predictions of time series for a very long time you will have a persistent time series process

Question 45

How do you deal with persistence

Answer 45

Use lag data Make sure to model your data

Question 46

How do you interpret a marginal effects plot

Answer 46

The y axis is the marginal effect of x on y dy/ dx And the x axis is now the value of the conditioning variable

Question 47

How do you calculate vif

Answer 47

1/1-r^2 1/tolerance Tolerance is 1-r^2