Abstract
Macroeconomic time-series data are aggregated, inaccurate, non-stationary, collinear and rarely match theoretical concepts. Macroeconomic theories are incomplete, incorrect and changeable: location shifts invalidate the law of iterated expectations so `rational expectations' are systematically biased. Empirical macro-econometric models are non-constant and mis-specified in numerous ways, so economic policy often has unexpected effects, and macroeconomic forecasts go awry. In place of using just one of the four main methods of deciding between alternative models, theory, empirical evidence, policy relevance and forecasting, we propose nesting 'theory-driven' and 'data-driven' approaches, where theory-models' parameter estimates are unaffected by selection despite searching over rival candidate variables, longer lags, functional forms, and breaks.
About Sir David F. Hendry
Hendry is a Professor of Economics at Nuffield College, University of Oxford. He is the Director of the Program in Economic Modeling, Institute of New Economic Thinking at the Oxford Martin School.
Hendry investigates the theory and practice of econometric modelling and forecasting in a non-stationary and evolving world, when the model differs from the real world economy in many essential ways. This means that he addresses challenges everyone meets in empirical macroeconomics.
His numerous contributions to dynamic econometrics include estimation theory, exogeneity, and causality, modelling methodology, model discovery, and forecasting. Hendry sees his work as in line with the course that Trygve Haavelmo pointed out in his "Probability Approach"’.
Professor Hendry has contributed to the PhD programmes at the University of Oslo and the Norwegian University of Science and Technology in Trondheim. He has also visited Norges Bank and Statistics Norway several times giving seminars and taking part in workshops.