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Title: On the use of mean square error and directional forecast accuracy for model selection: A Monte Carlo investigation Authors:  Mauro Costantini - University of L'aquila (Italy)
Robert Kunst - Institute for Advanced Studies (Austria) [presenting]
Abstract: A new procedure is proposed for model selection based on simultaneously targeting the mean square error and directional forecast accuracy criteria. The procedure combines the two accuracy measures using a weighting scheme for the selection of the forecasting models. Monte Carlo analysis under different scenarios serves as a tool that assesses the strength of the procedure. To this end, we consider various time series models as generation mechanisms, in particular, time-homogeneous univariate and vector autoregressions but also generating laws that involve thresholds and structural breaks. Among the forecast models fitted to the generated data, we also consider Bayesian vector autoregressions