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Research > Macro & Forecasting > [WP09/2014] An Automatic Leading Indicator, Variable Reduction and Variable Selection Methods Using Small and Large Datasets: Forecasting the Industrial Production Growth for Euro Area Economies 

[WP09/2014] An Automatic Leading Indicator, Variable Reduction and Variable Selection Methods Using Small and Large Datasets: Forecasting the Industrial Production Growth for Euro Area Economies

 


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An Automatic Leading Indicator, Variable Reduction and Variable Selection Methods Using Small and Large Datasets: Forecasting the Industrial Production Growth for Euro Area Economies

Gonzalo Camba-Mendez, George Kapetanios, Fotis Papailias & Martin R. Weale

quantf research Working Paper Series: WP09/2014


Abstract. 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.

Keywords. Bayesian Shrinkage Regression, Dynamic Factor Model, Euro Area, Forecasting, Kalman Filter, Partial Least Squares

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F. Papailias - D. Thomakos, (c) 2014
 
 
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