[R] Time series Regression with lags

Hi, I am working on zoo (time series) objects. Is there any way to do a time series regression with a lag period? E.g., Y(t) = b1*X1(t)+b2*X(t-1)+b3*X2(t) Is "dynlm" the default one to use? Anything else Thanks! [[alternative HTML version deleted]] __

[R] R - Time Series Regression with a p-value check for each additional added point.

Hi All, Here is my sample data set.. y x 7/4/2009 -0.2368 -1.2727 7/11/2009 -0.5039 -5.2805 7/18/2009 -0.6655 -6.9641 7/25/2009 -0.3936 -3.6937 8/1/2009 -0.3463 -5.6457 8/8/2009 -0.3000 -1.7368 8/15/2009 0.2378 6.4600 8/22/2009 -0.2962 -3.1113 8/29/2009 -0.4346 -4.2039 9/5/2009 -0.6971 -7.8216 9/1

[R] time series regression

Hi Everyone, I am doing a time series regression (one dependent time series variable, 7 independent time series variables and 32 annual observations). I have the problem of cointegration, autocorrelation and multicollinearity. I am considering an error correction model of the form: diff(lnY(t))=a+

[R] time series regression

Dear, I am doing a time series regression (one dependent time series variable, 7 independent time series variables and 32 annual observations). I have the problem of cointegration, autocorrelation and multicollinearity. I am considering an error correction model of the form: diff(lnY(t))=a+b1*lnY(t

[R] time series regression

Hi Everyone, I am trying to do a time series regression using the lm function. However, according to the durbin watson test the errors are autocorrelated. And then I tried to use the gls function to accomodate for the autocorrelated errors. My question is how do I know what ARMA process (order) to