Heuristic optimisation in economics and econometrics | vox – Research-based policy analysis and commentary from leading economists

Via Scoop.itBounded Rationality and Beyond

Economics has a long tradition of relying on quantitative models for both presenting its theory and testing it empirically. In fact, Joseph Schumpeter, in the first edition of Econometrica in 1933, described economics as the most quantitative of all sciences. Optimisation is an inherent part of this methodology. In theoretical models, agents are presented as utility maximisers and firms try to maximise profit or to minimise cost. Selecting and estimating models for given data sets amounts to optimisation as well – sums of squares are minimised and likelihoods are maximised so routinely today that often researchers may not even be aware that fitting a model means optimising it.
When building models, economists are often limited by the fact that the model later needs to be solved, ideally in a closed-form. Some researchers have abandoned relying on “representative agents” and opted for more complex models, relying on computer simulations to obtain results. Such agent-based models, if they are to be a viable alternative to more standard approaches, need to be “tuned” such that the results from these models coincide with empirical facts. This, again, is an estimation – and hence optimisation – problem. In this column, we will primarily discuss estimation problems in econometrics, but the problem is much more general and applies to many areas in economics (see Winker (2001), chapter 2).
Via voxeu.org

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