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Combining var estimation and state space model reduction for simple good predictions
Author(s) -
Gilbert Paul D.
Publication year - 1995
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980140307
Subject(s) - simple (philosophy) , monte carlo method , computer science , state space , state space representation , reduction (mathematics) , focus (optics) , estimation , econometrics , algorithm , mathematical optimization , mathematics , statistics , philosophy , physics , geometry , management , epistemology , optics , economics
A combination of VAR estimation and state space model reduction techniques are examined by Monte Carlo methods in order to find good, simple to use, procedures for determining models which have reasonable prediction properties. The presentation is largely graphical. This helps focus attention on the aspects of the model determination problem which are relatively important for forecasting. One surprising result is that, for prediction purposes, knowledge of the true structure of the model generating the data is not particularly useful unless parameter values are also known. This is because the difficulty in estimating parameters of the true model causes more prediction error than results from a more parsimonious approximate model.