TY - JOUR
T1 - Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods
AU - Kapetanios, George
AU - Marcellino, Massimiliano
AU - Papailias, Fotis
PY - 2016/8/1
Y1 - 2016/8/1
N2 - Forecasting macroeconomic variables using many predictors is considered. Model selection and model reduction approaches are compared. Model selection includes heuristic optimisation of information criteria using: simulated annealing, genetic algorithms, MC3 and sequential testing. Model reduction employs the methods of principal components, partial least squares and Bayesian shrinkage regression. The problem of unbalanced datasets is discussed and potential solutions are suggested. An out-of-sample forecasting exercise provides evidence that these methods are useful in predicting the growth rates of quarterly GDP and monthly inflation.
AB - Forecasting macroeconomic variables using many predictors is considered. Model selection and model reduction approaches are compared. Model selection includes heuristic optimisation of information criteria using: simulated annealing, genetic algorithms, MC3 and sequential testing. Model reduction employs the methods of principal components, partial least squares and Bayesian shrinkage regression. The problem of unbalanced datasets is discussed and potential solutions are suggested. An out-of-sample forecasting exercise provides evidence that these methods are useful in predicting the growth rates of quarterly GDP and monthly inflation.
U2 - 10.1016/j.csda.2015.02.017
DO - 10.1016/j.csda.2015.02.017
M3 - Article
SN - 0167-9473
VL - 100
SP - 369
EP - 382
JO - COMPUTATIONAL STATISTICS AND DATA ANALYSIS
JF - COMPUTATIONAL STATISTICS AND DATA ANALYSIS
IS - 0
ER -