Conclusion
David Hendry has made path-breaking contributions to econometrics: in modelling, in forecasting, in software, and in policy. Hendry (1995), Banerjee, Dolado, Galbraith and Hendry (1993), and Hendry and Doornik (2014)— three pioneering books on econometric methodology, cointegration, and model design—set the foundations for systematic empirical economic modelling with machine learning.
David has applied that approach to a wide range of substantive empirical studies, including on consumers’ expenditure, mortgage and housing markets, money demand, and climate change.In economic forecasting, David and Mike Clements developed a taxonomy of forecast errors that has yielded valuable insights into the nature of forecasting. David—often with Mike and (more recently) Jennie Castle—has provided new perspectives on many existing forecast techniques, including mean square forecast errors, add factors, leading indicators, pooling of forecasts, and multi-step estimation. David has also developed new forecast tools, such as forecast encompassing, and he has improved existing ones, such as nowcasting and the robustification of forecasts to breaks.
David’s studies in modelling and forecasting have had direct implications for economic policy. Practical implementation and assessment in modelling, forecasting, and policy require computer software, and David and Jurgen Doornik’s suite of software packages continues to embody best-practice econometrics. Overlaps are common between different strands in David’s research, with the analysis of real-world problems motivating and benefiting from that research.