Econ B S2 Flashcards

1
Q

How many explanatory variables in a simple vs multiple regression model?

A

Simple - 1

Multiple - 2 or more

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2
Q

What is the population model also known as?

A

The true model

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3
Q

Note

A

model must be linear in parameters

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4
Q

How are beta estimates interpreted?

A

They are partial effects of Xi on Y

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5
Q

What is CLM1?

A

The mode of population can be written in the form (see form) where betas are unknown parameters of interest and u is unobservable random error

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6
Q

What is CML2? What does it mean?

A

The explanatory variables x1->xk are non-stochastic.

It refers to the choosing of inputted x-values (ie. x=5, x=10). It is appropriate for experimental settings but these rarely arise in economics

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7
Q

What is CML3?

A

The error has an expected value of 0

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8
Q

Why does error arise?

A

Because a model won’t be able to find every influence on y in an economic model therefore this term captures these omitted factors

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9
Q

What is CML4?

A

In the sample (and tf population), none of the IVs are constant and there are no exact linear combinations between the IVs

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10
Q

What is perfect collinearity? What is it’s implication?

A

If an IV is an exact linear combination of another, this is perfect collinearity tf model can’t be estimated by OLS

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11
Q

Under what other circumstance does CLM4 also fail?

A

If n is less than k+1

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12
Q

What is CLM5?

A

Each disturbance:

a) has same finite variance σ^2
b) is uncorrelated with every other disturbance uj

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13
Q

What does it mean that each disturbance has same finite variance?

A

Means the variance is the same for all combinations of outcomes of the explanatory variables

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14
Q

What does it mean that each disturbance is uncorrelated with every other disturbance (example)?

A

ie. if interests rates are UNUSUALLY high this period (this observation), they will still be expected as average for the next period

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15
Q

What is CML6?

A

The population error u is normally distributed with mean=0 and variance=σ^2

u~N(0,σ^2)

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16
Q

What is the strongest assumption of the CLMs and why?

A

CLM6 - since u is the sum of many different factors affecting y, we can use central limit theorem to conclude that u has an approximate normal distribution

17
Q

What are the two weaknesses of the assumption with CLM6?

A
  • the factors in u can have very different distributions in the population
  • the argument assumes all unobserved factors affect y in a separate, additive fashion
18
Q

How can we prove the OLS estimator is unbiased?

A

Using CLM1-4

19
Q

How can it be proved that the OLS estimator is the best linear unbiased estimator?

A

Using CML1-5, the Gauss-Markov Theorem states it

20
Q

How can we prove the OLS estimator is the Minimum Variance Unbiased Estimator?

A

Using CLM1-6, it can be proved it is the MVUE in the class of all linear and non-linear estimators