BOB HOPE AIRPORT: BURBANK, CALIFORNIA
by
Sarah Lowery
University of Oregon
SGL@UOREGON.EDU
November 2011
ABSTRACT
This paper plans to analyze the size and growth of Bob Hope
Airport in Burbank, California from the years 1993 and 2009.
The growth rate of a
University of Oregon
Department of Economics
March 18, 2014
Professor Caroline E. Weber
Winter 2014
Version A
Final Exam
Economics 421
Introduction to Econometrics
INSTRUCTIONS:
Please clear your desk of everything except a pen or pencil, a non-programmab
10/31/12 EC 421 Read Chapter 11, 12, 13 Finish Simultaneous Equation I) Intro II) Structural Model III) Reduced Form IV) Simultaneity Bias Simultaneity Bias if had then we use IV to correct it, like you have Demand equation then for IV use supply equation
Sarah Lowery
November 1, 2011
EC 421
HW #4
1. Write out a mathematical representation of the theory. Suggest using different functional forms
if you think it better fits your data. Explicitly identify the variables relevant to your analysis.
Think about u
Sarah Lowery
June 22, 2011
951010439
Homework #2
Chapter 2 and 3
PA2-1
1. Lesters Home and Healthcare Services is organized as a Sole Proprietorship because
there is only one owner, Jennifer Lester.
2.
Assets
Liabilities
Owner's Equity
Cash
$
40,000
$
(13
University of Oregon
Department of Economics
Professor Caroline Weber
Winter 2015
Lecture: Review
Economics 421
Public Economics: Taxation
I.
II.
III.
IV.
V.
VI.
VII.
VIII.
I.
Introduction
Population vs. Sample
Assumptions of OLS
Calculating Estimates
Cal
421 Midterm Review Questions - Answers
I- True/False, Short Answer:
1) F, 2) T, 3) F, 4) T, 5) F, 6) F
7) Breusch-Pagan test or Whites test
8) a- If there is a 1% change in X, Y changes by 3 %.
b- test X2=X4=0
II- Problems:
1- H0 : 3 0, HA : 3 > 0
Test st
11/28/12 EC 421 IV) Detection:Look at data & look for trending variable Use correlogram (to see for trending variable) It is correlation between X and time t at some different time period. Ex- 1 This says it goes away with time. We expect correlation betw
11/26/12 EC 421 Non- Stationary Time Series 1) Intro 2) Stationary, Non Stationary 3) Consequences 4) Detecting 5) Cointegration Non- Stationary Time Series Stationary Variance will explode on you A) Stationary Time Series:(1) AR Model If expected value a
University of Oregon
Department of Economics
Professor Caroline Weber
Winter 2015
Lecture: Heteroskedasticity
Economics 421
Public Economics: Taxation
I.
II.
III.
IV.
V.
VI.
Introduction
Consequences
One Way to Fix the Problem: Robust Standard Errors
Test
Are Emily and Greg More Employable Than Lakisha and
Jamal? A Field Experiment on Labor Market Discrimination
By MARIANNE BERTRAND AND SENDHIL MULLAINATHAN*
We study race in the labor market by sending fictitious resumes to help-wanted ads
in Boston and Ch
University of Oregon
Department of Economics
Professor Caroline Weber
Winter 2015
Lecture: Review
Economics 421
Public Economics: Taxation
I.
II.
III.
IV.
V.
VI.
VII.
VIII.
I.
Introduction
Population vs. Sample
Assumptions of OLS
Calculating Estimates
Cal
University of Oregon
Department of Economics
Professor Caroline Weber
Winter 2015
Lecture: Non-Stationary Time Series
Economics 421
Introduction to Econometrics
I. Stationarity and Weak Dependence
II. Random Walk
III. Fixes
I. Stationarity and Weak Depend
University of Oregon
Department of Economics
Professor Caroline Weber
Winter 2015
Lecture: Autocorrelation
Economics 421
Introduction to Econometrics
I. Introduction
II. Detection
III. Fixes
I. Introduction
What is autocorrelation? cov(ut , us ) = 0 when
University of Oregon
Department of Economics
Professor Caroline Weber
Winter 2015
Lecture: Non-Stationary Time Series
Economics 421
Introduction to Econometrics
I. Stationarity and Weak Dependence
II. Random Walk
III. Fixes
I. Stationarity and Weak Depend
University of Oregon
Department of Economics
Due: In Lab Week of February 18
Professor Caroline Weber
Winter 2015
Assignment #6
Economics 421
Introduction to Econometrics
For each part of this assignment, paste the code in a word document along with any g
University of Oregon
Department of Economics
Professor Caroline E. Weber
Winter 2015
Lecture: Panel Data
Economics 421
Introduction to Econometrics
I.
II.
III.
IV.
V.
Introduction
Treatment Eects
Fixed Eects and First-Dierences
Comparing FE and FD
Random
11/21/12 EC 421
will be 2 if no autocorrelation Cochane&Orchat you do it till it converges (means till number settles down) VI) Lagged Values: one period
so in (1) a RHS variable is correlated with the disturbance term. We have assumptions that RHS variab
11/19/12 EC 421 This time Autocorrelation I) Intro II) Assumptions III) Violations Consequences IV) Detecting V) Fixing Autocorrelation means something unexpected happens and they are now sticking with the error term. Error term causes me pain now and wil
9/24/2012 EC 421 Econometrics Goal of this course is to know 90% of Econometrics which Economists use. Process of Econometrics: What problem you are trying to solve? Why are you doing so? Find the answer of problem in theory What do you need to make theor
EC 421 Last Time: 1. Intro to class 2. Process of Economics This Time: Review of 320 I. Intro II. Population vs. Sample III. Assumptions of OLS IV. Getting Estimates V. Testing VI. Measure Fit VII. Other -Dummies -Transformation -Specification Issues For
10/1/12 EC 421 Last Time Review EC 320 I) Intro II) Population vs. Sample III) Assumptions of OLS IV) Getting Estimates V) Testing Restriction VI) Measuring Fit VII) Misc HW 2 Write a draft for your paper Go to reference library www.scholargoogle.com Writ
10/3/12 EC 421 1) HW 2) Labs 3) Next Heteroshedasticity I) Intro II) Defn. III) Consequences IV) Causes of V) Detecting VI) Fix ups
Heteroshedasticity
I. Introduction: In the classical Model we get the idea
i.e the variance is same for all OLS = Best Line
10/8/12 EC 421 Hetro I) II) III) IV) V) VI) Intro Defn. Consequences Causes Detecting Fixing
R2 is bounded by 0 and 1. If we add variable then R2 will be same or increases but never fall down. Adjusted R2 It put penalty for adding variables, If you add va
10/10/12 EC 421 I) II) III) IV) V) I) Plims Stochastic RHS Measurement Error Instrumental Variable Review Probability Limits: Some equation does not solve in expectation but collapse in probability. Plim: Plim(X) = a Means it collapse in a If x =a no matt
10/15/12 EC 421 Last I) Intro II) Plims III) Stochastic Regressors A B C IV) Measurement Error V) IV(Instrumental Variable) Things needed to fix measurement error IV's IV) Measurement Error:We have model we wish to estimate. RHS variable is measured with
10/24/12 EC 421 About Paper:Last paragraph of introduction should be road map of the paper. Like Section one I have done this, section two I have empirical formula, section three empirical results, hypothesis testing etc. etc. Space for Figure 1 and 2 Fig
10/29/12 EC 421 Simultaneous Equation I) Intro II) Demand and Supply A. 3 cases what can you do B. Definition C. Structural +Reduced Form D. Simultaneity Bias E. Identification F. Under identification G. Over identification H. 2SLS I. Order J. Durbin Haus
11/5/12 EC 421 Time Series I) Introduction A) Overview B) Assumptions II) Dynamic Models Introduction: In Time Series we have to change the data of sample. Entire history of data is one observation. Time is a continuous variable. Most of the things are co