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Journal article

Deciding between alternative approaches in macroeconomics

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 and ‘rational expectations’ are then 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 ‘datadriven’ approaches, where theory-models’ parameter estimates are unaffected by selection despite searching over rival candidate variables, longer lags, functional forms, and breaks. Thus, theory is retained, but not imposed, so can be simultaneously evaluated against a wide range of alternatives, and a better model discovered when the theory is incomplete
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.ijforecast.2017.09.003

Authors

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Institution:
University of Oxford
Division:
SSD
Department:
Economics
Sub department:
EMOD
Role:
Author

Publisher:
Elsevier
Journal:
International Journal of Forecasting More from this journal
Volume:
34
Issue:
1
Pages:
119–135
Publication date:
2017-10-18
Acceptance date:
2017-07-21
DOI:
ISSN:
0169-2070

Keywords:
Pubs id:
pubs:713162
UUID:
uuid:677fabad-9d3a-426c-8f58-90ca01aa3b91
Local pid:
pubs:713162
Source identifiers:
713162
Deposit date:
2017-08-10
ARK identifier:

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