Trend Models for the Prediction of Economic Cycles
Many different approaches have been proposed to deal with the signal extraction problem in general. In line with this problem, trend estimation has also received a great deal of attention in the time series literature, especially when the interest is focused on forecasting turning points. In spite of all the differences among methods, one common feature remains in most of them. This is that trends tend to extrapolate themselves into the future as a line with a slope that depends on the recent past information. Although this is an optimal (e.g. in a Mean Square Error sense) and a sensible way to do it, it can be systematically erroneous when turning points are at hand. Nor those trend changes could be detected. In those situations, the main source of forecast errors is due to the trend. In this paper two linear trend models with a non-linear like forecast function are explored, namely the Smoothed Random Walk and the Double Integrated Autoregressive model. A combination of two frequency domain methods are explored as the procedure for the identification and estimation of these models. The trend models are compared with standard ones and its forecast performance tested on several real time series examples.
When requesting a correction, please mention this item's handle: RePEc:rec:cycles:v:3:y:2002:i:1_3. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (M. Carmen Guisan)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.