Unformatted text preview: III) Consequences of Non- Stationary:1. Inconsistence Estimator:
̄ )( (
̄) Consistency arises in OLS if data are “weakly persistent”( Stationary) if not then
2. Spurious Regression:
a) Deterministic Trend You get huge R2 , huge t’s
b) Random Walk with drift mistakenly run
You get huge R2 , huge t’s
“OUCH” IV) Detection:Look at data & look for trending variable
Use correlogram (to see for trending variable) √
It is correlation between X and time t at some different time period.
Ex- 1 | | This says it goes away with time. We expect correlation between today and yesterday to be stronger
than correlation between today and three time period.
Space for figure 1 Ex-2 M A (1) = moving average of errors that goes one period. Space for figure 2
Ex-3 Space for figure 3
This means it hold out for few time periods. This is stationary process because But you can see that past is sticking with time. So you don’t know? whether it is stationary or not. Therefore we have
to test whether ther...
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- Fall '08
- Variance, Autocorrelation, Time series analysis, Trend Stationary