※ 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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