Post-Keynesian modelling: where are we, and where are we going to?
AbstractThis paper presents the current “state of the art” of Post-Keynesian modelling, as well as the most important issues raised by it. We first present a new formal statement of the Keynes' model, highlighting the importance of the “static model of a dynamic process”, and insisting on the influence of “true uncertainty” and of the “views concerning the future.” The paper then analyses the three most important classes of Post-Keynesian contemporary models: the Kaleckian models of growth; the Minskian models showing the destabilizing impacts of financial variables on the economy; and the path-dependant models insisting on the nature of time in economics, and on the absence of any “natural” anchor. We argue that, whereas the current Post-Keynesian models have a lot in common with Keynes’ model, none of them encompasses all its rich and realistic properties, and a synthetic dynamic Post-Keynesian model is desirable, and has still to be framed. The main barriers to this synthesis are underlined.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 30726.
Date of creation: 2010
Date of revision:
Post-Keynesian modelling; Keynes’ “static model of a dynamic process”; Kaleckian models; Minskian models; Models of path dependency;
Other versions of this item:
- Angel Asensio & Dany Lang & Sbastien Charles, 2012. "Post Keynesian modeling: where are we, and where are we going to?," Journal of Post Keynesian Economics, M.E. Sharpe, Inc., vol. 34(3), pages 393-412, April.
- E02 - Macroeconomics and Monetary Economics - - General - - - Institutions and the Macroeconomy
- E12 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Keynes; Keynesian; Post-Keynesian
- B40 - Schools of Economic Thought and Methodology - - Economic Methodology - - - General
- E20 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - General (includes Measurement and Data)
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