Sir David F. Hendry, Kt

Deputy Director, Climate Econometrics, Institute for New Economic Thinking at the Oxford Martin School; Deputy Director, Climate Econometrics and Senior Research Fellow, Nuffield College

About

Professor Sir David F. Hendry, Kt, is Deputy Director of the Climate Econometrics project (formerly Programme for Economic Modelling) at the Institute for New Economic Thinking, Oxford Martin School, and of Climate Econometrics and Senior Research Fellow, Nuffield College, Oxford University.

He was previously Professor of Economics at Oxford (1982-2018), Professor of Econometrics at LSE and a Leverhulme Personal Research Professor of Economics, Oxford (1995-2000).

Professor Hendry was Knighted in 2009; is an Honorary Vice-President and past President of the Royal Economic Society; Fellow of the British Academy, Royal Society of Edinburgh, Econometric Society, Academy of Social Sciences, Econometric Reviews and Journal of Econometrics; Foreign Honorary Member of the American Economic Association and American Academy of Arts and Sciences; Honorary Fellow, International Institute of Forecasters and Founding Fellow, International Association for Applied Econometrics.

He has received eight Honorary Doctorates, a Lifetime Achievement Award from the Economic and Social Research Council (ESRC), and the Guy Medal in Bronze from the Royal Statistical Society. The ISI lists him as one of the world’s 200 most cited economists, he is a Thomson Reuters Citation Laureate, and has published more than 200 papers and 25 books on econometric methods, theory, modelling, and history; computing; empirical economics; and forecasting.

Professor Hendry investigates the theory and practice of econometric modelling and forecasting in a non-stationary and evolving world. When the processes being modelled are not time invariant, many of the famous theorems of both macroeconomic analysis and forecasting no longer hold. Conditional expectations cease to be unbiased predictors, and the mathematical basis of inter-temporal derivations fails, so dynamic stochastic general equilibrium (DSGE) models are inherently non-structural. A generalized taxonomy of forecast errors reveals the central role of unanticipated location shifts in forecast failure. Co-breaking, corrections to reduce forecast-error biases, and model transformations all help robustify forecasts in the face of location shifts. Although model selection poses great difficulties, research has revealed high success rates in operational studies of selection strategies. Automatic model selection algorithms can handle multiple shifts, embed theory insights, and avoid models omitting substantive relevant effects. Autometrics offers a viable approach to tackling more candidate variables than observations while controlling spurious significance. These tools are equally applicable to empirical modelling of climate change as it is driven by economic activity.