Macroeconomics and a Bit More Reality

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Macroeconomics, in its widest sense, is a topic which is too diverse to be usefully discussed in a single article. I will therefore take a very narrow view of what might be included under this general title and focus my attention on my own main area of interest, the development and use of large macro-econometric models. This is an area which has a long tradition and literature stemming form the pioneering work of Tinbergen (I937) and the Cowles commission (Klein (I950)). An extensive survey of the development of modelling may be found in Bodkin, Klein, and Marwah (i99i)). Macroeconometric modelling grew rapidly in importance during the late I950S and the I960s, going on to achieve a very influential role inmacroeconomic policy-making during the I970s. Its failure to deliver the detailed economic control which it had seemed to promise then led to a barrage of attacks, ranging from disillusion and sceptiscism on the part of policy makers to detailed and well argued acadeinic criticism of the basic methodology of the approach. Perhaps the most powerful and influential of these academic arguments came from Sims (I980) in his article 'Macroeconomics and Reality'. Sims argued, on three quite separate grounds, against the basic process of model identification, which lies at the heart of the Cowles Commission methodology. Firstly that economic theory gives rise to identification restrictions which are typically more complex than those traditionally applied in macroeconometric models. In particular he said that theory normally implies complex cross equation restrictions which require system estimation and which cannot be imposed on a single equation basis. Secondly that traditional identification conditions are often met simply because of the presence of dynamics in the models. Sims argued that this identification is spurious and technically invalid, as purely dynamic terms cannot help in structural identification in the conventional sense. Finally he said that the importance of expectations effects and the interaction of policy regimes and agents expectations make identification very difficult Sims argued that any one of these problems would form a challenging, but feasible, research agenda, but that 'Doing all of these at once would be a programme which is so challenging as to be impossible in the short run'. He then proposed a methodology based on vector auto regressive (VAR) models