||A special issue of the Journal of Forecasting will publish a selection of papers on the
issue “Advances in Forecasting in Macroeconomics and Financial Markets”.
Guest Editors: Anindya Banerjee (University of Birmingham); Stephen G. Hall
(Leicester University), Georgios P. Kouretas (Athens University of Economics and
Business), George S. Tavlas (Bank of Greece and Hoover Institution).
During the last ten to fifteen years, a range of forecasting techniques has been
developed and existing techniques have been extended and improved. These
techniques include dynamic factor models, factor augmented VARS, recursive
estimation, time varying parameters, and neural networks and machine learning. In
addition, recent work on forecasting has explored ways to use combinations of
techniques to take uncertainties into account (for example, by assigning probabilities
to each model and updating through Bayesian learning) and improve forecasts. These
techniques have been applied to a variety of data, including very high frequency data
and so-called big data. This special issue would consist of papers presented at the 26th
International Conference on Macroeconomic Analysis and International Finance to be
held at the University of Crete during May 26-28, 2022. Papers that would be
included in the special issue would apply these (and other) methods to a range of
macroeconomic and financial-market variables, for example, exchange rates,
inflation, output, share prices, bond yields, and commodity prices. These techniques,
including forecast combinations, will be compared with more traditional forecasting
tools including ARIMA models and the workhorse random walk model. In line with
the conference orientation, papers selected would emphasize international
comparisons of forecasting performance.