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Abstract
We test and report on time series modelling and forecasting using several US. Leading economic indicators (LEI) as an input to forecasting real US. GDP and the unemployment rate. These time series ...
Highlights
We test and report on time series modelling and forecasting using several US
We present results of a rolling window forecasting exercise where we illustrate the practical usefulness of our previous analysis across two themes: first, we show that the inclusion of either of our two previous leading indicator variables is useful in providing either on-par performance or performance enhancements compared to standard benchmarks; second, we show that the use of adaptive learning forecasting, a new method recently proposed by Kyriazi et al (2019), helps to improve even more the forecasting enhancements of the first theme
For easier readability we provide results for the best univariate and best bivariate models, the adaptive averaging autoregressive model ADA-AR—which is not included in the individual model ranking—as well as the best adaptive learning forecast ADL—the latter is computed by the combination of the best two models
Summary
We test and report on time series modelling and forecasting using several US. Leading economic indicators (LEI) as an input to forecasting US. real GDP and the unemployment rate.
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