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Comparing ex‐ante forecasts from a sem and var model: An application to the italian economy
Author(s) -
Boero Gianna
Publication year - 1990
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.3980090103
Subject(s) - ex ante , econometrics , economics , autoregressive model , inflation (cosmology) , model selection , vector autoregression , econometric model , interest rate , computer science , macroeconomics , physics , machine learning , theoretical physics
The predictive performance of a large‐scale structural econometric model (SEM) of the Italian economy the Prometeia model is compared in this paper with a vector autoregressive (VAR) model estimated for a selection of six main variables of interest. The paper concentrates on the quarterly ex‐ante forecasts of GDP growth rate and the annual forecasts of GDP growth and inflation rate, over the period 1980‐85. It concludes that no forecaster is systematically better than the other. In particular, the VAR model outperforms the SEM in short‐run forecasts, suggesting that, for the latter, more careful attention should be addressed to questions of dynamic specification. On the other hand, for longer intervals, the SEM forecasts are more accurate than the VAR forecasts, in that they can benefit from the judgemental interventions of the model users and the model can pick up the non‐linearities of the economy which cannot be captured by the VAR. Given the different kinds of information that can be extracted from the two approaches, it seems more reasonable to consider them as complementary rather than alternative tools for modelling and forecasting. Therefore, rather than attempting to establish the superiority of one type of model over the other, this kind of comparisons should be seen as a useful diagnostic tool for detecting types of model misspecification.