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David F. Hendry

David F. Hendry is Professor of Economics and Director of the Program in Economic Modeling, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.

Titles by This Author

Automatic Selection Methods in Econometrics

Economic models of empirical phenomena are developed for a variety of reasons, the most obvious of which is the numerical characterization of available evidence, in a suitably parsimonious form. Another is to test a theory, or evaluate it against the evidence; still another is to forecast future outcomes. Building such models involves a multitude of decisions, and the large number of features that need to be taken into account can overwhelm the researcher. Automatic model selection, which draws on recent advances in computation and search algorithms, can create, and then empirically investigate, a vastly wider range of possibilities than even the greatest expert. In this book, leading econometricians David Hendry and Jurgen Doornik report on their several decades of innovative research on automatic model selection.

After introducing the principles of empirical model discovery and the role of model selection, Hendry and Doornik outline the stages of developing a viable model of a complicated evolving process. They discuss the discovery stages in detail, considering both the theory of model selection and the performance of several algorithms. They describe extensions to tackling outliers and multiple breaks, leading to the general case of more candidate variables than observations. Finally, they briefly consider selecting models specifically for forecasting.

In their second book on economic forecasting, Michael Clements and David Hendry ask why some practices seem to work empirically despite a lack of formal support from theory. After reviewing the conventional approach to economic forecasting, they look at the implications for causal modeling, present a taxonomy of forecast errors, and delineate the sources of forecast failure. They show that forecast-period shifts in deterministic factors—interacting with model misspecification, collinearity, and inconsistent estimation—are the dominant source of systematic failure. They then consider various approaches for avoiding systematic forecasting errors, including intercept corrections, differencing, co-breaking, and modeling regime shifts; they emphasize the distinction between equilibrium correction (based on cointegration) and error correction (automatically offsetting past errors). Their results on forecasting have wider implications for the conduct of empirical econometric research, model formulation, the testing of economic hypotheses, and model-based policy analyses.

Titles by This Editor

Historically, the theory of forecasting that underpinned actual practice in economics has been based on two key assumptions?-that the model was a good representation of the economy and that the structure of the economy would remain relatively unchanged. In reality, forecast models are mis-specified, the economy is subject to unanticipated shifts, and the failure to make accurate predictions is relatively common.

In the last decade, economists have developed new theories of economic forecasting and additional methods of forecast evaluation that make less stringent assumptions. These theories and methods acknowledge that the economy is dynamic and prone to sudden shifts. They also recognize that forecasting models, however good, are greatly simplified representations that will be incorrect in some respects. One advantage of these newer approaches is that we can now account for the different results of competing forecasts.

In this book academic specialists, practitioners, and a financial journalist explain these new developments in economic forecasting. The authors discuss how forecasting is conducted, evaluated, reported, and applied by academic, private, and governmental bodies, as well as how forecasting might be taught and what costs are induced by forecast errors. They also describe how econometric models for forecasting are constructed, how properties of forecasting methods can be analyzed, and what the future of economic forecasting may bring.