 Deciding between alternative approaches in macroeconomicsInternational Journal of Forecasting, 2018, vol. 34, issue 1, 119-135 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 ‘data-driven’ 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. Keywords: Model selection; Theory retention; Location shifts; Indicator saturation; Autometrics (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:34:y:2018:i:1:p:119-135 DOI: 10.1016/j.ijforecast.2017.09.003 Access Statistics for this article International Journal of Forecasting is currently edited by R. J. Hyndman More articles in International Journal of Forecasting from Elsevier |
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