STATA LAB #3
Heteroskedasticity Robust S.E. Example 8.1 (WAGE1.RAW)
Estimate robust standard errors
Heteroskedasticity Robust S.E. Example 8.1
Computer 8.3 (modified) (HPRICE1.RAW)
Apply the full White test for heteroskedasticity to equation 8.18. Using t
(i)
C 3.7 Use the data in MEAP93.RAW to answer this question
Estimate the model math10 0 1 * log(exp end ) 2lunchprg u
And report results in the usual form, including the sample size and R-squared.
Are the signs of the slope coefficients what you expected
STATA TUTORIAL: LAB 1
1. STATA windows
The command window
The viewer/results window
The review of commands window
The variable window
2. Working with STATA
A.
B.
C.
D.
Opening Data
Using a log file
Useful Commands
Using a do file
A. Opening Data
Shows
1
TIME SERIES DATA
Chapter 10
I. Introduction
2
Estimating models using time series data
Observe more than one observation for variable(s).
Differences between cross-sectional data and time-series.
Properties of OLS
Need to account for seasonality or tren
1
MULTIPLE REGRESSION
ANALYSIS: SPECIFICATION
AND DATA ISSUES
Chapter 9
I. Introduction
2
Failure of zero conditional mean assumption
Correlation between error, u, and one or more explanatory
variables.
Functional Form Misspecification
Why variables can b
1
HETEROSKEDASTICITY
Chapter 8
I. Introduction
2
Previously have assumed homoskedasticity.
Variance of unobservable error, u, conditional on the
explanatory variables is constant.
Var(u|x1, x2, xk) = Var(u)= s2
Heteroskedasticity occurs if variance of u c
1
MULTIPLE REGRESSION
ANALYSIS
Chapter 3
I. Outline
2
Most problematic aspect of SLR is whether assumption of zero conditional
mean holds (ceteris paribus).
E(u|x)=0?
Example: Returns to Education: wage=0+1educ+u
Assuming E(u|educ)=0, 1 measures the cet
1
MULTIPLE REGRESSION
ANALYSIS
Chapter 6
I. Introduction
2
Issues in multiple regression analysis
Data
Scaling
Functional Form
Goodness of Fit and Selection of Regressors
Residual Analysis
I. Rescaling Data
Effects of Data Scaling
3
Consider effect of
1
MULTIPLE REGRESSION
ANALYSIS
Chapter 4
I. Introduction
2
Continue with the MLR model, but look further into
the statistical properties of the parameters: .
Make important assumptions about distribution of
parameters.
Statistical Inference & Hypothesis
MULTIPLE REGRESSION
ANALYSIS WITH QUALITATIVE
INFORMATION:
BINARY (OR DUMMY)
VARIABLES
Chapter 7
1
I. Introduction
2
Previous chapters, variables have had quantitative
meaning
Hourly
wages, years of education, GPA
Magnitude of the variable conveys usefu
Homework Solutions #2
Questions on Ch. 2
2.3 The following table contains the ACT scores and the GPA (grade point average) for eight
college students. Grade point average is based on a four-point scale and has been rounded to
one digit after the decimal
S
Homework Solutions #1
Questions on Background Material
1. A random sample of 22 business economists were asked to predict the percentage
growth in the consumer price index over the next year. The forecasts were:
3.6 3.1 3.9 3.7 3.5 3.7 3.4 3.0 3.6 3.4 3.1
DUSP 11.203
Microeconomics
Frank Levy
September 23, 2010
Problem Set #3 + Answers (Answer to 4 B Corrected)
1) This problem builds on Problem 2 of the last problem set. Consider a different plan to raise
farm incomes. As before, the government announces a
1
FAIRNESS
1.1
Ultimatum Game
a Proposer (P) and a receiver (R) split $10
P proposes s
R can accept or reject
if R accepts, the payos are (P,R)=(10 s, s)
if R rejects, they are (0, 0)
Evidence from In Search of Homo Economicus: Behavioral Experiment
Department of Urban Studies and Planning
11.203 Microeconomics
Frank Levy
Fall, 2010
Problem Set # 2
1) [10 points] The Case-Shiller Housing Price Index has become a standard reference to
track home prices. In Miami, for example, the Case-Shiller Index re
Vast body of experimental evidence, demonstrates that discount rates are
higher in the short-run than in the long-run.
Consider a nal thought experiment:
Choose a ten minute break today or a fteen minute break tomorrow.
Choose a ten minute break in 100
1
Consumption path experiment
Pick a consumption path (ages 31 to 60).
1. You are deciding at age 30 and face no uncertainty (e.g., health, demographics, etc).
2. Consumption represents consumption ows (e.g., consumption of housing is calculated on a ow b
Department of Urban Studies and Planning
11.203 Microeconomics
Problem Set # 1
Frank Levy
Fall, 2010
1) As part of an irrigation project, you need to install several thousand feet of irrigation
pipe in ditches dug in land you are improving. You have $