Arbeitspapier
An automatic leading indicator, variable reduction and variable selection methods using small and large datasets: Forecasting the industrial production growth for euro area economies
This paper assesses the forecasting performance of various variable reduction and variable selection methods. A small and a large set of wisely chosen variables are used in forecasting the industrial production growth for four Euro Area economies. The results indicate that the Automatic Leading Indicator (ALI) model performs well compared to other variable reduction methods in small datasets. However, Partial Least Squares and variable selection using heuristic optimisations of information criteria along with the ALI could be used in model averaging methodologies.
- Sprache
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Englisch
- ISBN
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978-92-899-1586-1
- Erschienen in
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Series: ECB Working Paper ; No. 1773
Bayesian Analysis: General
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Model Evaluation, Validation, and Selection
dynamic factor model
euro area
forecasting
Kalman filter
partial least squares
Kapetanios, George
Papailias, Fotis
Weale, Martin R.
- Handle
- Letzte Aktualisierung
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20.09.2024, 08:23 MESZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Camba-Méndez, Gonzalo
- Kapetanios, George
- Papailias, Fotis
- Weale, Martin R.
- European Central Bank (ECB)
Entstanden
- 2015