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.

ISBN
978-92-899-1586-1
Language
Englisch

Bibliographic citation
Series: ECB Working Paper ; No. 1773

Classification
Wirtschaft
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
Subject
Bayesian shrinkage regression
dynamic factor model
euro area
forecasting
Kalman filter
partial least squares

Event
Geistige Schöpfung
(who)
Camba-Méndez, Gonzalo
Kapetanios, George
Papailias, Fotis
Weale, Martin R.
Event
Veröffentlichung
(who)
European Central Bank (ECB)
(where)
Frankfurt a. M.
(when)
2015

Handle
Last update
10.03.2025, 11:44 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Camba-Méndez, Gonzalo
  • Kapetanios, George
  • Papailias, Fotis
  • Weale, Martin R.
  • European Central Bank (ECB)

Time of origin

  • 2015

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