On Wed, 20 Sep 2000, Abu Hammour wrote:
> Hi,
> I am using R interactively for estimation and even data manipulation. I
want
> to use lag() function within lm() function in a way looks like:
>
> mymodel<- lm(y~x+I(lag(y,-1)-1, data=anydata)
>
> What I get is always perfect fit; (that is, coefficient=1) which is not
> true.
But it *is* true: the model is not what you meant I suspect. The problem
is that lag(y, -1) is a time series with an altered time base, and as a
regressor it is unchanged from y. If you really mean this as a model, you
have random regressors and least squares is inappropriate. Use arima0 to
fit it.
> If the model looks like
> mymodel<- lm(y~z+x+I(lag(x,-1)-1, data=anydata)
^)> summary returns only one coefficient for x and I(lag(x,-1))
Actually, if drops the third term as completely aliased with the
second. You need x to be a time series to have any hope here.
anydata <- data.frame(x = ts(rnorm(100)), y = rnorm(100), z = rnorm(100))
mymodel<- lm(y ~ z + cbind(x, lag(x,-1))[-101,] - 1, data=anydata)
might do what you want.
> Is there any way to do both of these models without going through long
> process of transforming of x and y and making ts.intersect before running
> lm() function.
See above. It's a short process, but you do need to apply time-series
functions to time series.
Perhaps simpler is to shift the series manually:
mymodel<- lm(y ~ z + x + c(NA, x[-100]) - 1, data=anydata)
or use embed if you want natural (positive) lags.
--
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272860 (secr)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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