Topic
Topic 4
Inference: Part II
ARE/ECN 240 A
Graduate Econometrics
Econometrics
Professor: scar Jord
Outline of this Topic
Outline of this Topic
Confidence Regions: confidence intervals,
simultaneous testing, Bonferroni/Holm procedure,
confidence region
Topic
Topic 5
Extensions to the Basic Framework I
ARE/ECN 240 A
Graduate Econometrics
Econometrics
Professor: scar Jord
Outline of this topic
Outline of this topic
Heteroskedasticity: reminder of OLS results and
White (1980) corrected standard errors, der
Topic
Topic 7
Extensions to the Basic Framework II
ARE/ECN 240 A
Graduate Econometrics
Econometrics
Professor: scar Jord
Outline of this topic
Outline of this topic
Nonlinear regression
Limited Dependent Variable regression
Applications of MLE
2
ARE/ECN 2
Topic
Topic 8
Introduction to Time Series Data
ARE/ECN 240 A
Graduate Econometrics
Econometrics
Professor: scar Jord
Outline of this topic
Outline of this topic
Dependence and covariance-stationarity,
ergodicity and CLT for MDS
Wold representation theorem
Topic
Topic 6
Instrumental Variable Regression
ARE/ECN 240 A
Graduate Econometrics
Econometrics
Professor: scar Jord
Outline of this topic
Outline of this topic
Randomized Experiments, natural experiments
and causation
Instrumental variables: causation ve
Topic
Topic 3
Inference: Part I
ARE/ECN 240 A
Graduate Econometrics
Econometrics
Professor: scar Jord
Outline of this topic
Outline of this topic
Elements of a hypothesis test:
The null and alternative hypotheses
null and alternative hypotheses
Errors of
Topic
Topic 1
Multivariate Regression: Part I
ARE/ECN 240 A
Graduate Econometrics
Econometrics
Professor: scar Jord
Outline of this topic
Outline of this topic
Statement of the objective: we want to explain
the behavior of one variable as a function of ot
Topic
Topic 0
Review of Basic Concepts
ARE/ECN 240 A
Graduate Econometrics
Econometrics
Professor: scar Jord
Outline of this topic
Outline of this topic
Quick review of bivariate linear regression
Example: Educational attainment and class size
Basic stati
Topic
Topic 2
Multivariate Regression: Part II
ARE/ECN 240 A
Graduate Econometrics
Econometrics
Professor: scar Jord
Outline of this Topic
Outline of this Topic
We have derived the OLS/MM/MLE estimators
for the linear regression model. Now we want to:
D
240A Winter 2007
Solutions to Problem Set 4
e
1.(a) We have = (X0 WX)1 X0 W(X + u) = + (X0 WX)1 X0 Wu.
e ] = + E[(X0 WX)1 X0 Wu] = + (X0 WX)1 X0 WE[u] = ,
So E[
using X and W constants and E[u] = 0.
e
e
e
(b) V[] = E[( )( )0 ] = E[(X0 WX)1 X0 Wu)(X0 WX)1
240A Winter 2010: Solutions to Problem Set 3
1.(a) We have
bb
b
b
b
b
y0 Ay = y0 A(y u) as y = y + u
0
0b
b
b
= y Ay y Au
1
b
b
b
= y0 Ay y0 (I ll0 )u
N
b
bb
b
= y0 Ay y0 Iu as l0 u = 0
0
b
b
bb
b
= y0 Ay as y0 u = X0 u = 0.
(b) Hence
bb
b
b
b
b
y0 Ay
y0
240A Winter 2010: Solutions to Problem Set 1
b
1.(a) By 2 which equals 2.91. So hourly wage up by $2.91.
(b) Using t-distribution for critical values, a 95% condence interval for hourly wage is
x t.025;N 1 s/ N = 14.030 t.025;554 21.097/ 555 = 14.030 1.96
240A Winter 2010: Solutions to Problem Set 5
1.
(a) 1/100
(b)
(c)
(d)
2.
b
E [y |x]
= 0.01 exp(1 + 0.01x)/[1 + exp(1 + 0.01x)].
x
P100
x=1 cfw_0.01 exp(1
+ 0.01x)/[1 + exp(1 + 0.01x)]
b
b
E [y|x = 50] E [y |x = 51]
or
x
x
b
E [y |x = 90]
x
b
b
b
b
E [