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Kolasa Marcin (Warsaw School of Economics, Poland)
Rejoinder to Perspectival Representation in DSGE Models by Paweł Kawalec
Economics and Business Review, 2017, vol. 3 (17), nr 3, s. 148-150, bibliogr. 6 poz.
Model dynamicznej stochastycznej równowagi ogólnej, Badania naukowe, Eksperyment badawczy, Zarządzanie przez cele, Badania empiryczne, Nauki empiryczne, Makroekonomia, Pomiary, Eseje
Dynamic Stochastic General Equilibrium (DSGE), Scientific research, Scientific experiment, Management by objectives, Empirical researches, Empirical science, Macroeconomics, Measurement, Essays
In this short rejoinder I am going to share my own views on the current stance and prospects for DSGE models, drawing from my experience as a researcher and central bank economist. It is useful to start from a proposition that DSGE models are a compromise between rigorous academic research and policy makers' pragmatism. The former favors relatively small models, where key mechanisms can be clearly understood. The structure ideally includes only those ingredients that are necessary for exposing these mechanisms, and its validation uses only a narrow set of data that the model seeks to explain. It very often does not make sense to apply such models to address questions that are too distant from the ones originally asked. In other words, academic models are usually quite specialized, even in macroeconomic applications, and despite the fact that their very core, consisting of a description of household preferences or production technology, might be highly standardized. (fragment of text)
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  1. Blanchard, O. (2016). Do DSGE models have a future? Policy Brief, No. 16-11. Peterson Institute of International Economics.
  2. Del Negro, M., & Schorfheide, F. (2013). DSGE model-based forecasting. In: G., Elliott, C., Granger, & A. Timmermann (Eds.), Handbook of Economic Forecasting, 2(2), 57-140. Elsevier.
  3. Kolasa, M., & Rubaszek, M. (2016). Does foreign sector help forecast domestic variables in DSGE models? KAE Working Paper, No. 2016-022, Warsaw School of Economics.
  4. Pagan, A. (2003). Report on modelling and forecasting at the Bank of England. Bank of England Quarterly Bulletin, Spring, 60-88.
  5. Romer, P. (2016). The Trouble with macroeconomics. The American Economist (forthcoming).
  6. Terasvirta, T. (2006). Forecasting economic variables with nonlinear models. In: G., Elliott, C., Granger, & A. Timmermann (Eds.), Handbook of Economic Forecasting, 1(1), 413-457. Elsevier.
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