Flashcards in Term 2 Deck (69)

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1

## Discuss Simple and Multiple Regressions

###
A ~ represents a simple

A ^ represents multiple

2

## What is the simple relationship between B~1 which does not control for X2 and B^1 which does (Bias)

### B~1=B^1+B^2d~1

3

## What are the two cases of B~ and B^?

###
If x2's effect on Y is positive, x1 and x2 are positive correlated

B~1>B^1

If x1 and x2 are negatively correlated

B~1

4

## What is bias equal to for B~1?

### Bias(B~1)=B2D~1

5

## What is asymptotic theory?

### As N gets larger, the probability that Z is different from its mean falls

6

## What is the CLT?

### As a sample size increases, the sample becomes normally distributed

7

## What is the consistency of OLS?

### As sample size increases, a coefficient tends to its true value

8

## What is the normality of OLS?

### As the sample size increases, the distribution becomes normal

9

## What are the consequences of heteroskedasticity?

###
OLS is unbiased,

Incorrect estimators therefore cannot use T and F tests

OLS no longer BLUE

10

## How do you estimate variance of a coefficient under heteroskedasticity?

### Sum(x-Xbar)^U2/ Variance

11

## Why is it not a good idea to only compute robust SE?

### They are worse than usual SE

12

## How can we detect heteroskedasticity?

###
Graphs

The Breusch-Pagan Test

White Test

13

## How do you perform the Breusch-Pagan Test?

###
Estimate the Regression, Square the residuals

Regress U^2 using explanatory variables, F test for joint significance

14

## How do you perform the white test?

### Same as BP but with indicators

15

## How do you calculate the WLS?

### Replace every coefficent by RootX

16

## What is the difference between CS and TS data?

###
TS data is ordered, thus is not randomlyy sampled

There is therefore correlation

17

## What are the types of TS data models?

###
Static: Same time period

Finite Distributed Lag (FDL): Y can be affected by upto Q periods in the past

18

## What is lag distribution and how is it calculated

### Plots the coefficents of each lagged variable on a graph

19

## What is the impact propensity? What occurs if log form?

###
The coefficent of Z in the current time period - immediate change

Short run instantaneous elasticity

20

## What is the long run propensity? What occurs if log form?

###
The sum of all lag coefficents

Tells us what happens if Z permanently increases

Called long run elasticity

21

## What is an autoregressive model? What does its order determine?

###
A model where past Y's influence current Y's

Order is number of lags

22

## What assumptions are required for finite sample OLS to be unbiased? (1-3)

###
TS1 - Linear in Paramaters

TS2 - No perfect collinearity

TS3 - Errors conditional mean is zero

These assumptions allow OLS to be unbiased

23

## What assumptions are required for finite sample OLS to be unbiased? (4-6)

###
TS4- Homoscedaticity (Variance does not depend on X or change over time

TS5- No serial correlation (errors are not correlated)

TS6 - Normality

24

## What is contemporaneous exogenity?

### A weaker assumption of TS3, that assumes no conditional mean for only variables within the same time period

25

## What are the three types of correlaton?

###
Explanatory variables over time

Violates TS2

Explanatory variables and errors

Violates TS3 and bais

Errors over time

Violates TS5

26

## How do you calculate variance of a coefficent in a TS model?

### Variance(B) = Var/SST(1-R2)

27

## What is the problem associated with TS data and R2

### If their is a high trend within the data, R2 will be higher than it should be

28

## What is weakly dependant data?

###
The condition that we impose on TS data to ensure CLT and LLN holds

Correlation between observations gets smaller as time between grows

29

## How do you calculate the corr for weakly dependant data?

### Coefficent of Yt-1 raised to time period in advance

30