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1
Q

Explain the rationale for the ADF test? What is it’s solution?

A

The DF test is only valid if u(t) is white noise

u(t) will be autocorrected if there was autocorrelation in the dependent variable (change in y(t))

Solution: augment the test using p lags of the dependent variable

2
Q

What does the ADF alternative model now become?

A

Same as before but with an added term

Sum(alpha(i).changey(t-i))
For i=1 to p

Same CVs

3
Q

What problem then arises with the ADF test? 2 solutions?

A

Problem arises in determining the optimal number of lags of dependent variable:
Can use 2 ways to decide:
- frequency of data
- information criteria

4
Q

What is the Phillips-Peron test?

A

Similar to ADF test except it incorporates an automatic correlation to allow for autocorrected residuals

5
Q

Main criticism to PP and DF tests?

A

Power of tests is low if process is stationary but has a roof close to the non stationary boundary (ie. 0.95)

6
Q

Main solution to low test power for DF and PP tests?

A

Use a stationarity test (ie. KPSS) as well;

H0: stationarity
H1: non-stationarity