Re: [問題] 為什麼跑AR時 可以不考慮correlationꨠ…
※ 引述《wwwwwww (哪個王八蛋一天上十九次됩》之銘言：
: ※ 引述《liton (歐吉桑留學生)》之銘言：
: : 這些該念的我都念過了
: : 我是對Time Series 和Cross Section的不同處理方式有疑問
: : 在CrossSection中X=alpha+a*Y+b*Z
: : Y和Z的相關性很高的話
: : 我們會用instrument variables等方法來處理
: : 但在AR中X=alpha+a*X(-1)+b*X(-2) 如果ACF和PACF很高的話
: : 我們反倒覺得變數自己的遞迴性很高
: : 用該變數自己的歷史資料便可預測下一期的X
: : 那這樣不就代表Corr[X,X(-1)]或Corr[X,X(-2)]會很高
: : 在Cross Section中 這是個很嚴重的問題
: : 但在Time Series中 這怎反倒變成是一個很好的性質?
: Instrument variables is mainly used to deal with the difficulty
: that the explanatory variables and error terms are correlated.
: AR models have no such difficulty.
: But ARMA models do have and can be treated by instrument variables.
: For example, in the ARMA(1,1) case, you cannot get a consistent estimator of
: AR coeff. by regressing x_{t} on x_{t-1}.
: But you can get a consistent estimator of the AR coff. by regressing
: x_{t} on x_{t-2}. Now x_{t-2} is the instrument variable.
Well, I just take one example to overcome the correlation problem in
cross section.
In practice, there are many methods to handle with the problem.
For example, I can drop the independent variables in the regression.
A best practice for cross section model always includes testing the
correlation between independend variables.
The key is that correlation in cross section is a serios problem, no matter
in theories or practice. Correlation will result in at least three
kinds of trouble:
1.measurement error or errors in variables
2.endogeneity
3.omitted variables
However, it seems that time series care more about unit roots.
I think that's another problem in time series, but the unit
roots theory has not resolve the correlation problem.
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