Monthly data and short-term forecasting: an assessment of monthly data in a VAR model
✍ Scribed by Eduardo Salazar; Martin Weale
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 170 KB
- Volume
- 18
- Category
- Article
- ISSN
- 0277-6693
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✦ Synopsis
Use of monthly data for economic forecasting purposes is typically constrained by the absence of monthly estimates of GDP. Such data can be interpolated but are then prone to measurement error. However, the variance matrix of the measurement errors is typically known. We present a technique for estimating a VAR on monthly data, making use of interpolated estimates of GDP and correcting for the impact of measurement error. We then address the question how to establish whether the model estimated from the interpolated monthly data contains information absent from the analogous quarterly VAR. The techniques are illustrated using a bivariate VAR modelling GDP growth and in¯ation. It is found that, using in¯ation data adjusted to remove seasonal eects and the impacts of changes to indirect taxes, the monthly model has little to add to a quarterly model when projecting one quarter ahead. However, the monthly model has an important role to play in building up a picture of the current quarter once one or two months' hard data becomes available.