×

### Let's log you in.

or

Don't have a StudySoup account? Create one here!

×

### Create a StudySoup account

#### Be part of our community, it's free to join!

or

##### By creating an account you agree to StudySoup's terms and conditions and privacy policy

Already have a StudySoup account? Login here

## ECON 300 Week 2

1 review
by: Patricia Soto

69

2

5

# ECON 300 Week 2 ECON 300

Patricia Soto
UIC
GPA 3.89

### Preview These Notes for FREE

Get a free preview of these Notes, just enter your email below.

×
Unlock Preview

### Preview these materials now for free

Why put in your email? Get access to more of this material and other relevant free materials for your school

Week 2 of Notes
COURSE
Econometrics
PROF.
Irina Horoi
TYPE
Class Notes
PAGES
5
WORDS
KARMA
25 ?

## 2

1 review
"I had to miss class because of a doctors appointment and these notes were a LIFESAVER"
Brian Skiles

## Popular in Economcs

This 5 page Class Notes was uploaded by Patricia Soto on Saturday January 23, 2016. The Class Notes belongs to ECON 300 at University of Illinois at Chicago taught by Irina Horoi in Winter 2016. Since its upload, it has received 69 views. For similar materials see Econometrics in Economcs at University of Illinois at Chicago.

×

## Reviews for ECON 300 Week 2

I had to miss class because of a doctors appointment and these notes were a LIFESAVER

-Brian Skiles

×

×

### What is Karma?

#### You can buy or earn more Karma at anytime and redeem it for class notes, study guides, flashcards, and more!

Date Created: 01/23/16
Week 2 Random Sample: If Y 1 ,Y2… Ynare independent random variables with common pdf (y, θ) determined by one parameter θ then {Y1, Y2,….Yn} is said to be a random sample from that pdf. • Y ∼ (µ, σ^2) can take finite sample from population and then estimate µ, σ^2 • Use sample average to find mean! o Sample average y-bar = (ΣYi)/n is an estimator for µ! Estimator is unbiased if the probability distribution of the estimator has an expected value equal to the parameter of interest. Y-bar 1 ≠ Y-bar 2 E[Y-bar] = µ E[(ΣY-sub2)/n] =1/n*E[ΣYi] =1/n*Σ*e[Yi] =n/n*E[Y] =µ θ W = h(Y-sub1,….Y-subn) E[W] = θ upward bias E[W] > θ s^2 = (Σ(Yi-Y-bar)^2/n—1 Central Limit Theorem X-bar ∼ (µ, σ^2/n) Always start hypothesis tests the same way Do college students drink a lot? H(null) µ = 6 Ha µ > 6 Ha µ not equal to 6 Ha µ < 6 0 = Fail to Reject Type Errors Type 1: Reject the null hypothesis when it is in fact true • Ex. Someone who is found guilty when he/she did not commit the crime -> Consequence: Innocent sent to jail o Ho: guilty, Ha: Not guilty • Wage increased, when in reality the wage stayed the same -> Consequence: Waste money on policy Type 2: Fail to reject the null when it is actually false. • Ex. Not guilty when he/she did commit the crime. -> Consequence: murder walks free • Wage did not increase; when in reality it did not. -> Consequence: Could have improved people’s lives. Using the Central Limit Theorem we can standardize it to make it a t- distribution (x-bar - µ)/s/sqrt(n) Study standardize central limit theorem and setting up hypothesis tests* Wage example X-bar = 14 µ =13 s^2 = 4 n =9 df = n-1= 8 Ho: µ = 13 Ha: µ > 13 14-13/(2/3)=1.5 (t-value) critical value = 1.86Reject Ex. 2 X-bar = 13.8 µ = 13 s^2 =4 n =100 Ho: µ = 13 Ha: µ ≠ 13 13.8 – 13/ (2/10) = 4 1.98 1.98 Reject the null ( 2 tailed a = 1.98) Confidence Interval 95% of the time the confidence interval will contain the true µ (x – t* (s/sqrt(n) x-bar + t* s/sqrt(n)) ________________________________________________________ _ Econometrics is slightly different from statistics because it is a quantitative social science. We’re often looking for estimates. • Estimate: your best guess of a population parameter given your data • Causal Effect: A relationship between two things (or events) whereby one causes the other to happen. o Ex. People who go to college earn more money, that doesn’t mean going to college gets you more money. Maybe, really smart people go to college and that’s what affects the relationship • Counter-factual (opposite-what really happened) o Ex. College vs. no college. You can’t know what happens in 2 scenarios because you can’t be in 2 states at once Data • Observational: data that consists of information collected about people/firms from the real world o Problems: don’t get counter-factual, don’t know if they are random variables, incorrect causality (tedtalk) § Married men live longer then non married marriage. Men who are healthy & have a higher life expectancy get married more often vs. men with lower life expectancies § Kids who sleep with the lights on tend to be short sighted. Short sighted parents like to leave lights on because it’s a genetic disorder, so their kids tend to be near sighted (because of genetics) ú Lesson: CORRELATION DOES NOT IMPLY CAUSATION • Causality: If A then B. • Correlation: If A then sometimes, maybe, B. • Confounders: when we observe a relationship between A and B, but in actuality it is a third variable that causes both o Ex. A -> B (observed) but in reality C (confound) ->A & B § Ex. Genetics in nearsighted example above • Reverse Causality: We observe a relationship between A and B, conclude A causes B, but in reality B causes A o Ex. Marriage -> expected longer life in reality it’s the opposite • Simultaneity: We observe a relationship between A and B, conclude A causes B, but in reality part of A cause B and part of B causes A. o Ex. Spending on police officers and crime (negative relationship) => doesn’t necessarily mean if you spend more on cops the crime will decrease • Experiment: randomized control trials o Ex. Tennessee Star Experiment – split up classrooms into smaller classrooms to see if class size affects test scores • Drawbacks o Cost!!!! It’s very expensive § You have to pay to implement the study and sometimes you have to pay participants o Ethical concerns! § Ex. You can go to college but you other guys can’t o This leaves us with Observational Data. L Yes it’s good but it’s not the best • Macro or fiancé use time series data • Labor issues use panel data or repeated cross sectional • Time series: Observations correspond to different time periods but for the same individual. o You’re comparing things to themselves (past vs. now) § Ex. Stock data, GDP • Cross Sectional: Observations correspond to different individuals for the same time period. o Ex. Census • Repeated Cross Sectional: Series of cross sections appended to each other, different individuals • Panel: Time series for several individuals, repeated individuals Majority of what you do in econometrics is regression* Know how to do it well! Regression- the best fitting line (relationship) that can be made on a graph • Things to consider: o How do we allow for things other than x to affect y? o What functional relationship is there between x and y? o How do we make sure we capture ceteris paribus (able to control everything else) effect of x on y? Yi = βo + β1x1 + µ Yi = βo +β1x1 +β2x-sub2^2 + µi

×

×

### BOOM! Enjoy Your Free Notes!

We've added these Notes to your profile, click here to view them now.

×

### You're already Subscribed!

Looks like you've already subscribed to StudySoup, you won't need to purchase another subscription to get this material. To access this material simply click 'View Full Document'

## Why people love StudySoup

Steve Martinelli UC Los Angeles

#### "There's no way I would have passed my Organic Chemistry class this semester without the notes and study guides I got from StudySoup."

Kyle Maynard Purdue

#### "When you're taking detailed notes and trying to help everyone else out in the class, it really helps you learn and understand the material...plus I made \$280 on my first study guide!"

Steve Martinelli UC Los Angeles

Forbes

#### "Their 'Elite Notetakers' are making over \$1,200/month in sales by creating high quality content that helps their classmates in a time of need."

Become an Elite Notetaker and start selling your notes online!
×

### Refund Policy

#### STUDYSOUP CANCELLATION POLICY

All subscriptions to StudySoup are paid in full at the time of subscribing. To change your credit card information or to cancel your subscription, go to "Edit Settings". All credit card information will be available there. If you should decide to cancel your subscription, it will continue to be valid until the next payment period, as all payments for the current period were made in advance. For special circumstances, please email support@studysoup.com

#### STUDYSOUP REFUND POLICY

StudySoup has more than 1 million course-specific study resources to help students study smarter. If you’re having trouble finding what you’re looking for, our customer support team can help you find what you need! Feel free to contact them here: support@studysoup.com

Recurring Subscriptions: If you have canceled your recurring subscription on the day of renewal and have not downloaded any documents, you may request a refund by submitting an email to support@studysoup.com

Satisfaction Guarantee: If you’re not satisfied with your subscription, you can contact us for further help. Contact must be made within 3 business days of your subscription purchase and your refund request will be subject for review.

Please Note: Refunds can never be provided more than 30 days after the initial purchase date regardless of your activity on the site.