PRACTICE EXAM 1
MULTIPLE CHOICE (10 QUESTIONS, 30 points)
1) The expected value of a discrete random variable
A) is the outcome that is most likely to occur.
B) can be found by determining the 50% value in the c.d.f.
C) equals the population median.
D) is
Practice long answer questions for 120B final. (Caveats: not meant to be exhaustive study
direction of OVB, for example, and note that final is comprehensive covering chapters 2-8, 11, 12.)
1) You have learned that earnings functions are one of the most i
Extra Credit Quiz 9
Troy Kravitz
September 5, 2012
We estimate the following probit regression equation:
P r(Yi = 1|Xi ) = (1.32 + .32 Xi )
1) What is the predicted change in the probability that Yi = 1 when Xi increases from 1 to 2?
Answer: 2.5%.
2) The
Extra Credit Quiz 10
Troy Kravitz
September 6, 2012
1) This problem causes incorrect standard errors but leaves everything else unchanged:
A Inconsistency
B Perfect Multicollinearity
C Heteroskedasticity
D Omitted Variable Bias
Answer: C
2) Which term is
Quiz 1, Chapter 4
1) Which of the following is/are coefficients?
A) intercept
B) slope
C) both intercept and slope
D) none of the above
Answer: C
2) Which is NOT another name for the Y variable?
A) dependent variable
B) explaining variable
C) regressand
D
QUIZ ON CHAPTER 5
1) The t-statistic is calculated by dividing
A) the OLS estimator by its standard error.
B) the slope by the standard deviation of the explanatory variable.
C) the estimator minus its hypothesized value by the standard error of the estim
Quiz on Chapter 6
1) Multiple regression (in chapter 6) differs from simple linear regression in that:
A) we include multiple regressands
B) we include multiple explanatory variables
C) the regression model is no longer linear
D) we no longer minimize the
Quiz 4
Econ 120B: Troy Kravitz
July 25, 2011
1) What is the interpretation of 1 in the following regression: Yi = 0 + 1 ln Xi
a) A one-unit change in X is predicted to cause a 1 unit change in Y , holding all else constant.
b) A one-unit change in X is pr
Summary of Previous Lectures
Causality and regression analysis
Omitted variable bias (OVB)
Omitted variable bias formula
Three examples to show the direction of bias:
(1) Midterm, WebCT sessions and homework scores.
(2) Log(wage), education, and ability.
Econ 120B
Lecture 1: Introduction
What is Econometrics?
What did you do in Econ 120A?
Probability theories and statistics
What is Economics?
People have unlimited wants but limited resources
Study how people make choices
Economic theory gives us qua
Econ 120B
Dr. Maria Cndido
Fall 2012
Homework 1
Econometrics 120B
Due Wednesday, October 31st, at 5:00 pm
For this homework, you have to turn in a small ANSWER SHEET and a LOG file.
(1) Write your answers in an answer sheet (2-3 pages maximum). Certain an
Economics 100B: Microeconomics, Part B
Winter 2017, Professor Simone Galperti
This is the second of three courses in the core microeconomics sequence. It builds on the material presented
in Economics 100A. The principal themes of the course will be the th
University of California, San Diego
Department of Economics
Economics 120C Econometrics
Winter 2017
MWF 9:00 9:50 am, Center Hall 214
Instructor
Maria Teresa Cndido
Office: 110A Economics
Office Phone #: 534-2518
Office Hours: Mondays 12:30 am 2:00 pm
Ema
Lecture Notes #5
(Chapter 7)
Economics 120B
Econometrics
Prof. Dahl
UC San Diego
Outline
1.
2.
3.
4.
Hypothesis tests and confidence intervals for a single coefficie
Joint hypothesis tests on multiple coefficients
Other types of hypotheses involving multi
Lecture Notes #2
Economics 120B
Econometrics
Prof. Dahl
UC San Diego
Linear Regression with One Regressor
(SW Chapter 4)
Linear regression allows us to estimate, and make
inferences about, population slope coefficients.
Ultimately our aim is to estimate t
Economics 120B
Econometrics
Professor Dahl
UC San Diego
Brief Overview of the Course
Economics suggests important relationships, often with policy
implications, but virtually never suggests quantitative
magnitudes of causal effects.
What is the quantitat
Econ 120B
Chapters 4 and 5 Extra Practice Problems
1.
Sir Francis Galton, a cousin of James Darwin, examined the relationship between the height of children and
their parents towards the end of the 19th century. It is from this study that the name regress
Extra Credit Quiz 7
Troy Kravitz
August 30, 2012
Using N = 400 observations, we estimate the following model
Yi = 0 + 1 X1,i + 2 X2,i + 3 X3,i + 4 X4,i + 5 X5,i + 6 X6,i + ui
We want to test the hypothesis
H0 : 1 = 2 = 0, 5 = 1
1) What is the correct test
Extra Credit Quiz 8
Troy Kravitz
August 31, 2012
Consider the regression
Earni = 0 + 1 Edu + 2 Edu Black + 3 Edu F emale + 4 Edu Black M ale
1) What is the predicted change in earnings for each additional year of schooling for white females ?
Answer: 1 +
Summary of Lecture 1
Economics as a science
Scientific method and randomized experiment
Econometrics
Write down models
Estimate parameters
Use those estimated parameters to:
a) predict outcomes
b) test theories
Causation versus correlation
Types o
Summary of Lecture 2
1. Probability framework for statistical
inferences
a) Population, random variable, and distribution
b) Moments of a distribution (mean, variance, standard
deviation, covariance, correlation)
c) Conditional distributions and condition
Summary of Lecture 3
Estimators and estimates
Distribution of a sample of data drawn
randomly from a population
Y
Sampling distribution of the sample mean
The law of large numbers
The Central Limit Theorem
Why we use Y to estimate Y
Least squares a
Summary of Lecture 4
Confidence Intervals
Confidence interval of the sample mean
From probabilities to statistics
Confidence interval of the population mean
Standard error
Hypothesis Testing
P-value
t-test
Student t distribution
Why we dont use
Summary of Lecture 5
California Test Score Data:
Estimate group means
Hypothesis testing
Confidence interval
Linear regression with one regressor
Population regression line
Terminology
Ordinary Least Squares (OLS)
1
Lecture 6
Derivation of OLS est
Summary of Lecture 6
Derivation of OLS estimators
Interpretation of OLS regression results
Using OLS to make prediction
Measures of fit: R-squared and SER
Least squares assumptions:
E(u|X) = 0
(Xi, Yi) are i.i.d.
Large outliers in Xi and Yi are rare.
Summary of Lecture 7
Recap of all important materials.
Sampling distribution of the OLS estimators.
Hypothesis testing and confidence intervals
1
Lecture 8
Hypothesis testing and confidence intervals
Regression when X is binary.
Homoskedasticity ver
Lecture 9
Causality and regression analysis
Omitted variable bias (OVB)
Multiple regression model
Interpretation of coefficients in multiple
regression
1
Causality and regression analysis
What is, precisely, a causal effect?
The common-sense definition o
Review of Key Concepts
The relationship between correlation and covariance:
XY
cov X , Y
XY
XY
var X var Y
Independence:
Eui | X i E ui
E ui | X i 0 covui , X i 0
If u and X have non-zero covariance, then they are correlated.
1
Review of Key Concep
Summary of Lecture 11
Multiple regression model
Interpretation of coefficients in multiple
regression
Derivation of the OLS estimators
Omitted variable bias (again!)
Measure of fit
1
Lecture 12
Least Squares Assumptions for Multiple
Regression
Samp