Christopher L. Gilbert , Università degli Studi di Trento Duo Qin , Queen Mary, University of London
July 1, 2005
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We characterize modern econometrics in terms of the emergence a widely accepted analytical framework. A major theme which dominated much of the debate through the century was whether and how econometric models can reflect theory-generated economic structures. In the period prior to the 2nd world war, economists adopted a wide variety of analytical methods, some ad hoc but others reflecting advances in statistical methodology. Business cycle analysis and demand analysis were the two major areas in which statistical theory was employed. Methods became increasingly formalized but problems of data adequacy, estimation and identification were not always well distinguished. During and immediately after the war, Cowles Commission research sought to base econometrics on autonomous probabilistic models specified in terms of underlying structural parameters. Least squares would not normally be consistent in such models and maximum likelihood estimation was to be preferred. Subsequently, however, the pendulum swung back towards least squares-based methods and this was reflected in the textbook expositions of what was accepted as standard econometrics in the late sixties and early seventies. Subsequently, the paradigm was undermined by the challenges imposed by rational expectations modelling, which challenged standard identification assumptions, and by the poor forecasting performance of many macroeconomic models by comparison with black box time series competitors. The result was a revival of non-structural modelling, particularly in the analysis of macroeconomic data.
J.E.L classification codes: B16, B23, C10, C50
Keywords:Econometrics, History, Estimation, Identification