Weights in 'nls' and in forecasting are two very different things.

Weights in functions like 'nls', 'lm', 'lme', and often also 'optim' are

typically justified from a maximum likelihood argument. In that case,

the weights are (exactly or metaphorically, depending on context)

inversely proportional to the variances of the observations. Negative

weights in that context implies imaginary standard deviations; I'll let

you extrapolate from there.

Weights in forecasting, however, commonly occur when modeling, for

example, the output of a reactor: If the reactor delivers less than its

standard output on one cycle, it will often do the opposite on the next.

This is common with straight "moving average" models in the standard

time series literature, e.g., the famous Box and Jenkins (or Box,

Jenkins and Reinsel now) book "Time Series Analysis, Forecasting and

Control". Any good book on "arima" / "Box Jenkins" modeling should

discuss this. You can get started on this with the time series chapter

in the Venables and Ripley book, "Modern Applied Statistics with S".

hope this helps,

spencer graves

BBands wrote:

> On 4/28/06, Dirk Eddelbuettel <

[hidden email]> wrote:

>> So negative weights don't really fit that framework. That said, from a purely

>> numerical as opposed to statistical point of view you can probably minimize a

>> suitable expression with nls() or optim(). But you'd be 'on your own out

>> there'.

>

> Hi Dirk,

>

> I was looking for an all-in sort of solution, but preprocessing the

> data will get me where I need to go, so no traipsing around in the

> 'out there' for me. Perhaps I don't have the necessary statistical

> sophistication, but negative weights for linear models seem like a

> perfectly reasonable solution to the problem of different forecasting

> abilities at different horizons.

>

> jab

> --

> John Bollinger, CFA, CMT

> www.BollingerBands.com

>

> If you advance far enough, you arrive at the beginning.

>

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