Automatic time series modeling and forecasting: A replication case study of forecasting real GDP, the unemployment rate and the impact of leading economic indicators

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 have been addressed before, but our results are more statistically significant using more recently developed time series modelling techniques and software. In this replication case study, we apply the Hendry and Doornik automatic time series PC-Give (AutoMetrics) methodology to the well-studied macroeconomic series, US. real GDP and the unemployment rate. The Autometrics system substantially reduces regression sum of squares measures relative to traditional variations on the random walk with drift model. The LEI are a statistically significant input to real GDP. A similar conclusion is found for the impact of the LEI and weekly unemployment claims series leading the unemployment rate series. We tested the forecasting ability of best univariate and best bivariate models over 60- and 120-period rolling windows and report considerable forecast error reductions. The adaptive averaging autoregressive model forecast ADA-AR and the adaptive learning forecast, ADL, produced the smallest root-mean-square errors and lowest mean absolute errors. Our results are greatly supportive of the significance for modeling and forecasting of the suggested input variables and they imply considerable improvements over all traditional benchmarks.

Keywords

• adaptive learning forecasting automatic time series modelling
• forecasting
• leading indicators