Econometrics

Question 1

YNAM
RIRO
YOSO!
YANR

Answer 1

You Need A Model
Random In Random Out
You Only Sample Once
You are Never Right

Question 2

Cross Sectional, Time Series, Longitudal/Panel

Answer 2

.

Question 3

Probability Distribution (function)

Answer 3

A list of all possible values of the random variable, and the probability that each value will occur.

-These probabilities sum to 1.

Question 4

Random Variable (RV)

Answer 4

A number with an uncertain value, but it can be estimated with probability.

-its value depends in some clearly-defined way on a set of possible random events(frequency distribution).

Question 5

discrete random variable vs continuous random variable

Answer 5

A Discrete can only take a countable number of values(finite). For example, number of people in a room.

A Continuous can take an infinite number of values. For example, amount of time taken to do something, it could be infinite.

Question 6

Bernoulli distribution

Answer 6

a random variable(dummy variable) which takes the value 1 with success probability of p, and the value 0 with failure probability of q=1-p.

Question 7

Binomial distribution

Answer 7

with parameters n and p is the discrete probability distribution of the number of successes(x) in a sequence of n independent yes/no experiments, the outcomes of the experiment are Bernoulli(0/1).

Question 8

E(Y)

Answer 8

= expected value of Y 
= weighted average of all possible values/mean of Y 
= typical value of Y 
= center of Y's distribution 
= first moment of Y 
= MUy

Question 9

V(Y)

Answer 9

= variance of Y
= Spread/Dispersion of Y's distribution 
= expected of SQUARED distance between Y and Muy
= [ (y-MUy)^2]
=σ^2

Question 10

SD(Y)

Answer 10

=Standard Deviation of Y
= “ typical” distance between Y + M measured in same units as Y -> (y-MUy)
=sqrt{V(Y)}

Question 11

Sample Space

Answer 11

The set of all possible outcomes

Question 12

Marginal Probability Distribution

Answer 12

probability distribution between 2 random variables(X and Y).

Question 13

Normal Probability Distribution

Answer 13

A normal distribution with a mean of 0 and SD/V of 1. N(0,1).

To compute probabilities for a normal distribution it must first be standardized by subtracting the mean, then dividing the result by the SD.

Question 14

Standardized

Answer 14

-Special Type of linear Function

To compute probabilities for a normal distribution it must first be standardized by subtracting the mean, then dividing the result by the SD.

Formula: Z= (y-My)/SD

aka Z= (y-My)/sqr(V/n)

Question 15

“Z-score”

Answer 15

The number of standard deviations that a value x is away from the mean.

*Use tables at end of book

Question 16

Covariance

Answer 16

A measure of the extent to which 2 random variables move together.

Formula:

Question 17

Correlation

Answer 17

An alternative measure of the dependence between X and Y that solves the “units” problem of covariance.

-Specifically, the correlation between X and Y is the covariance(x,y), divided by their standard deviations.

Formula: Corr(X, Y) = cov(X,Y) / SD(x),SD(y)

-# is between -1 and 1. 0 means uncorrelated.

Question 18

Probability Density Function

Answer 18

a probability dist. function for a Continuous RV falling in between the range/area of the distribution.

Question 19

Cumulative Probability Distribution

Answer 19

the probability that a random variable id >= to a particular value,

Question 20

Regression functions tell you about?

Answer 20

AVERAGES(statistical/probability) of a conditional probability of (x | y).

-We summarize these based on conditional distribution functions

Question 21

Law of Large numbers.

Answer 21

states that based on probability and statistics under general conditions, Ybar will be near Mu(convergence) with very high probability when n is large. and vise versa if it’s small.

Question 22

Central Limit Theorem

Answer 22

states that regardless of the distribution of the underlying(parent) population of data based on law of large numbers:

1. the mean of the population of means is always = to the mean of underlying population
2. The SD of the population is always = to the SD of the underlying population divided by the the square root of the sample size(n)
3. The distribution of means will increasingly approximate a normal distribution as the size N of samples increases.

-The distribution of sample means is also called the “sampling distribution”, which is a normal distribution.

Question 23

p-value

Answer 23

Also called the significance probability is the probability of drawing a statistic at least as adverse to the null hypothesis as the one you computed from the sample, assuming the null hypothesis is correct.

Formula:See page 72

Question 24

Standard Error, sample variance, sample SD

Answer 24

See page 73-74 for formulas.

Question 25

Estimator (& Estimate)

Answer 25

an estimator is a function of a sample data draw randomly from a population. An estimate is the numerical value of the estimator when it is actually computed using data from a sample.

• if mean of estimator equals mu it is Unbiased.
• Least squared estimator: minimizes the distance between Y and mu.

Question 26

Coverage probability

Answer 26

the probability that a confidence interval of random samples contains the true population mean.