midterm formulae Flashcards

(40 cards)

1
Q

Independent variable is endogenous if

A

Correlated with the error term

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2
Q

-

A

Average

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3
Q
A

Predicted

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4
Q

B1^

A

Sum (x1-x-)(y1-y-) / sum (x1 - x-)^2

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5
Q

B0^

A

Y- - B1^x-

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6
Q

Ssr

A

Sum u1- squared

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7
Q

SST

A

Sum (y1 minus yhat)squared

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8
Q

R squared

A

Variation in y explained by the variables in this model
1 - ssr/sst

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9
Q
A
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10
Q

Mpc ^

A

Slope

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11
Q

Apc^

A

Cons^ / income

B0/income. + b1

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12
Q

B1log(sale)
B1 means

A

Elasticity of y with respect to sales

A 1% increase in sales increase y by b1

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13
Q

Y

A

Dependent variable

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14
Q

X

A

Independent variable

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15
Q

U

A

Error term / unobserved factors

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16
Q

B0

A

Intercept value of y when x = 0

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17
Q

B1

A

Slope / effect of x on y

Y will (+-) by b1 for every x

18
Q

N

19
Q

Omitted variable

A

Variable the omitting of would cause a negative bias

20
Q

For wrong sign

A

Need a positive variable with negative correlation
Or
Need a negative variable with positive correlation

21
Q

Diff in y

A

B1(triangle) + b2(triangle)

Triangles = difference veteran ppl A and B

23
Q

Log log

A

Log y = b1 log x

B1 is percentage change in y when x changes by 1%

24
Q

Level log

A

B1log x

B1/100 is change in y per percentage change in x

25
Log level
Log y B1 is percentage change in y when x changes by 1 unit
26
27
Billionare = 0.195log (Pop)
A 1% Increase in pop means 0.195 increase in billionaires
28
Change in siblings
1=0.94changesibs
29
Residuals u1^
Y1 - y1^ Diff between actual and predicted values
30
31
Ols
Estimates the relationship between variables while minimising residual errors.
32
E(points|attempts) = b0 + b1attempts
The average of points in the population lies on the straight line described by b0 + b1atttwmpts.
33
Mean
Average
34
Omitting variables (x2, x3)
Causes bias in the estimation of b1
35
The bias depends on
The correlation between x1 and the omitted variables The size of the coefficients b1 (large partial efffects mean larger bias)
36
If x1 is highly correlated with x2, omitting them leads to
A significant change in the estimates coefficient of x1.
37
B1(squiggly line) from simple regressions and b1^ can be
Very different due to the omitted variable bias
38
The more correlates x1 is with x2 and x3,
The larger the effects fo x2 and x3 on y are, the greater difference between b1 squiggle and b1^
39
B1 squaggle
B1^ + b2^music note 1 squiggle
40