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Title: |
Learning about Models and Their Fit to Data |
| Author: |
Pagan, Adrian |
| Author
Affiliation: |
Australian National U and U Oxford |
| Source: |
International Economic Journal, Summer 2002, v. 16, no. 2, pp. 1-18 |
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Publication Date: |
Summer 2002 |
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Abstract: |
The paper asks
what is the most informative way of assessing the fit of a model to
data. Often an answer comes from the context. In particular, from a
consideration of how the model is to be used. Such information often
leads one to seek transformations of the data that deliver the requisite
information. Even in those instances in which we are sure of the best
way of looking at fit, e.g. by the mean of the sample scores of an
alternative model, it is often useful to augment the information
provided by these tests through a decomposition of them. In time series
such decompositions have often involved recursive analysis. In this
paper we propose that the moments underlying tests be re-written as an
integrated conditional moment, where the conditioning variable is chosen
to elicit useful information. The idea is potentially useful in
assessing nonlinear models. To implement the approach nonparametric
methods generally need to be applied to simulated data in order to
perform the decomposition. A range of applications of the idea, drawn
from published articles, is used to illustrate the advantages of the
method. |
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