UCSD Core Econometrics Sequence Reflection Essay 1 page reflection note(). It will be a very simple essay for you who have already learned this before. It

UCSD Core Econometrics Sequence Reflection Essay 1 page reflection note().

It will be a very simple essay for you who have already learned this before. It does not take a very long time to write this note.

What were the most important 1-2 new things you learnt from the lecture and/or

readings that you did not know before class? Describe one way in which what you

learnt connects to either a different subject/topic you are interested in, or a personal

experience.

2. What were 1-2 points discussed in lecture/readings that you are still confused/unclear

about and would like some further clarification on?

3. What topics/questions would you like to learn more about or discuss more based on

content covered in the lecture/readings?

Each student needs to submit their own reflection. We will use the Turnitin software to check for plagiarism and compare assignments. AVAR
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Econometrics 120C: OVB Example in STATA
Kaspar Wüthrich
This lecture will be recorded and made available asynchronously via Canvas.
1
Data
• The dataset wage2.dta contains a sample with individual wages (lwage), education
(educ), and IQ (IQ), which can be used as a proxy for ability.
• Let’s get an overview over the dataset using the commands describe and summarize.
. describe lwage educ IQ
storage
display
value
variable name
type
format
label
variable label
————————————————————————————-lwage
float
%9.0g
natural log of wage
educ
byte
%9.0g
years of education
IQ
int
%9.0g
IQ score
. sum lwage educ IQ
Variable |
Obs
Mean
Std. Dev.
Min
Max
————-+——————————————————–lwage |
935
6.779004
.4211439
4.744932
8.032035
educ |
935
13.46845
2.196654
9
18
IQ |
935
101.2824
15.05264
50
145
2
Long Models
The long model is: Y =
. reg
y
lwage
I age
w
X IQ
E
educ
0
+
1 X1
+
educ
2 X2
+u
1Q
Source |
SS
df
MS
————-+———————————Model | 21.4779447
2 10.7389723
Residual | 144.178339
932 .154697788
————-+———————————Total | 165.656283
934 .177362188
Number of obs
F(2, 932)
Prob > F
R-squared
Adj R-squared
Root MSE
=
=
=
=
=
=
935
69.42
0.0000
0.1297
0.1278
.39332
B
—————————————————————————–lwage |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
————-+—————————————————————o
educ |
.0391199
.0068382
5.72
0.000
.0256998
.05254
IQ |
.0058631
.0009979
5.88
0.000
.0039047
.0078215
a
_cons |
5.658288
.0962408
58.79
0.000
5.469414
5.847162
—————————————————————————–3
Short model
The short model is: Y =
X
Y educ
. reg lwage
0
+
1 X1
+e
educ
I age
w
Source |
SS
df
MS
————-+———————————Model | 16.1377042
1 16.1377042
Residual | 149.518579
933 .160255712
————-+———————————Total | 165.656283
934 .177362188
Number of obs
F(1, 933)
Prob > F
R-squared
Adj R-squared
Root MSE
=
=
=
=
=
=
935
100.70
0.0000
0.0974
0.0964
.40032
short
p
—————————————————————————–lwage |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
————-+—————————————————————educ |
.0598392
.0059631
10.03
0.000
.0481366
.0715418
_cons |
5.973063
.0813737
73.40
0.000
5.813366
6.132759
——————————————————————————
y
4
Regression of omitted on included
IQ
The regression of omitted on included is: X2 =
educ
IG
0
+
educ
1 X1 + r
. reg IQ educ
Source |
SS
df
MS
————-+———————————Model | 56280.9277
1 56280.9277
Residual | 155346.531
933 166.502177
————-+———————————Total | 211627.459
934 226.581862
Number of obs
F(1, 933)
Prob > F
R-squared
Adj R-squared
Root MSE
=
=
=
=
=
=
935
338.02
0.0000
0.2659
0.2652
12.904
F
—————————————————————————–IQ |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
————-+—————————————————————educ |
3.533829
.1922095
18.39
0.000
3.156616
3.911042
_cons |
53.68715
2.622933
20.47
0.000
48.53962
58.83469
—————————————————————————–5
Results
Let us summarize the results and compute the OVB:
Iwage
educ
IQ
• Long model: Y = 0 + 1 X1 + 2 X2 + u
ˆ1 = .0391199, ˆ2 = .0058631
wage
• Short model: Y =
0
+
educ
+e
Idifference
1 X1
OVB
ˆ1short = .0598392
la
• Regression of omitted on included: X2 =
0
+
educ
+r
1 X1
ˆ1 = 3.533829
• OVB formula: ˆ1short = ˆ1 + ˆ2 · ˆ1 , OVB = ˆ2 · ˆ1 = .0207193
finite sample 043
formula
6

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