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Kawalec Paweł (John Paul II Catholic University of Lublin)
Perspectival Representation in DSGE Models
Economics and Business Review, 2017, vol. 3 (17), nr 3, s. 80-99, bibliogr. 64 poz.
Słowa kluczowe
Model dynamicznej stochastycznej równowagi ogólnej, Badania naukowe, Eksperyment badawczy, Zarządzanie przez cele, Badania empiryczne, Nauki empiryczne, Makroekonomia, Pomiary
Dynamic Stochastic General Equilibrium (DSGE), Scientific research, Scientific experiment, Management by objectives, Empirical researches, Empirical science, Macroeconomics, Measurement
Klasyfikacja JEL: B41, C11, E17
summ., The author gratefully acknowledges the support of the Polish National Science Center (NCN) under the grant no UMO-2014/15/B/HS1/03770.
DSGE models (Introduction) have recently been criticized by P. Romer (2016) as pseudoscientific (Section 1). Their dominance is attributed to the uncritical "deference to authority" that has dominated macroeconomics "for the last 30 years". In contrast, the paper aims to support the widespread view that - their problems notwithstanding - DSGE models meet the epistemic standards of scientific research. The argument turns on the recent advancements in theories of scientific representation (Section 1) and of empirical grounding (Section 2). The latter is illustrated with a historical case, which also substantiates Romer's constructive point on the role of theory in design of measurements. (original abstract)
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Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka Szkoły Głównej Handlowej w Warszawie
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Biblioteka Główna Uniwersytetu Ekonomicznego w Poznaniu
Pełny tekst
  1. An, S., & Schorfheide, F. (2007). Bayesian analysis of DSGE models. Econometric Reviews, 26(2-4), 113-172.
  2. Blanchard, O. (2016). Do DSGE models have a future? (No. PB 16-11). Peterson Institute for International Economics.
  3. Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. The American Economic Review, 79(4), 655-673.
  4. Braun, B. (2017). Central bank planning? Unconventional monetary policy and the price of bending the yield curve. In J. Beckert, & R. Bronk (Eds.), Uncertain futures: imaginaries, narratives, and calculation in the economy (p. forthcoming). Cambridge: Cambridge University Press.
  5. Breuss, F. (2016). Would DSGE models have predicted the great recession in Austria? (No. 530/2016). WIFO.
  6. Callebaut, W. (2012). Scientific perspectivism: A philosopher of science's response to the challenge of big data biology. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 43(1), 69-80.
  7. Canova, F., & Sala, L. (2009). Back to square one: Identification issues in DSGE models. Journal of Monetary Economics, 56(4), 431-449.
  8. Chari, V. V., Kehoe, P. J., & McGrattan, E. R. (2009). New Keynesian models: Not yet useful for policy analysis. American Economic Journal: Macroeconomics, 1(1), 242-266.
  9. Christiano, L. J., Trabandt, M., & Walentin, K. (2010). DSGE models for monetary policy analysis. In B. M. Friedman & M. Woodford (Eds.), Handbook of monetary economics (Vol. 3, pp. 285-367). Amsterdam: Elsevier.
  10. Collins, H. M., & Evans, R. (2002). The third wave of science studies: Studies of expertise and experience. Social Studies of Science, 32(2), 235-296.
  11. Collins, H. M., & Evans, R. (2009). Rethinking expertise. Chicago: Univ. of Chicago Press.
  12. Dawid, R. (2013). String theory and the scientific method. Cambridge: Cambridge University Press.
  13. de Vroey, M. (2016). A history of macroeconomics from Keynes to Lucas and beyond. New York: Cambridge University Press.
  14. de Vroey, M., & Malgrange, P. (2010). From The Keynesian revolution to the Klein- Goldberger model: Klein and the dynamization of Keynesian theory. University of Louvain, Department of Economics, Discussion Paper, (2010-19).
  15. de Vroey, M., & Pensieroso, L. (2016). The rise of a mainstream in economics (Discussion Paper No. 2016-26). Louvain: University of Louvain, Department of Economics.
  16. Del Negro, M., & Schorfheide, F. (2013). DSGE model-based forecasting. In G. Elliott & A. Timmermann (Eds.), Handbook of economic forecasting (Vol. 2, pp. 57-140). Amsterdam: Elsevier.
  17. Diaconis, P., & Zabell, S. L. (1982). Updating subjective probability. Journal of the American Statistical Association, 77(380), 822-830.
  18. Fernandez-Villaverde, J. (2010). The econometrics of DSGE models. SERIEs, 1(1-2), 3-49.
  19. Fernandez-Villaverde, J., & Francisco Rubio-Ramirez, J. (2004). Comparing dynamic equilibrium models to data: a Bayesian approach. Journal of Econometrics, 123(1), 153-187.
  20. Fernandez-Villaverde, J., Ramirez, J. F. R., & Schorfheide, F. (2016). Solution and estimation methods for DSGE models. Cambridge, MA: National Bureau of Economic Research.
  21. Frigg, R., & Nguyen, J. (2017). Scientific representation is representation-as. In H.-K. Chao, & J. Reiss (Eds.), Philosophy of science in practice (pp. 149-179). Cham: Springer.
  22. Galbacs, P. (2015). The theory of new classical macroeconomics: a positive critique. Cham: Springer.
  23. Galbacs, P. (2016). Beyond the realism of mainstream economic theory. Phenomenology in economics. Economics and Business Review, 2 (16)(4), 3-24.
  24. Giere, R. N. (2006). Scientific perspectivism. Chicago: University of Chicago Press.
  25. Grabek, G., Kłos, B., & Koloch, G. (2010). SOE PL 2009-Model DSGE małej otwartej gospodarki estymowany na polskich danych. Specyfikacja, oceny parametrów, zastosowania (Materiały i Studia No. 251). Warszawa: Narodowy Bank Polski. Retrieved from
  26. Halpern, E. F. (1974). Posterior consistency for coefficient estimation and model selection in the general linear hypothesis. The Annals of Statistics, 2(4), 703-712.
  27. Hendry, D. F. (1976). The structure of simultaneous equations estimators. Journal of Econometrics, 4(1), 51-88.
  28. Hendry, D. F. (1987). Econometric methodology: a personal perspective. In T. F. Bewley (Ed.), Advances in econometrics (pp. 29-48). Cambridge: Cambridge University Press.
  29. Herbst, E. P., & Schorfheide, F. (2016). Bayesian estimation of DSGE models. Princeton: Princeton University Press.
  30. Howson, C., & Urbach, P. (2005). Scientific reasoning: the Bayesian approach (3rd ed). Chicago: Open Court.
  31. Kawalec, P. (2016). Interaction and structural representation in calibration of economic models. Studia Metodologiczne, 36(4), 131-145.
  32. Klein, L. R., & Goldberger, A. S. (1955). An econometric model for the United States, 1929-1952. Amsterdam: North Holland.
  33. Kocherlakota, N. (2010). Modern macroeconomic models as tools for economic policy. The Region, (May), 5-21.
  34. Ladyman, J., Bueno, O., Suarez, M., & van Fraassen, B. C. (2011). Scientific representation: A long journey from pragmatics to pragmatics: Bas C. van Fraassen: Scientific representation: Paradoxes of perspective. Oxford: Clarendon Press, 2008. Metascience, 20(3), 417-442.
  35. Leijonhufvud, A. (1994). Hicks, Keynes, and Marshall. In H. Hagemann & O. F. Hamouda (Eds.), The legacy of Hicks: his contribution to economic analysis (pp. 147-162). London: Routledge.
  36. Linde, J., Smets, F., & Wouters, R. (2016). Challenges for central banks' macro models. In C. R. Taylor & H. Uhlig (Eds.), Handbook of macroeconomics (Vol. 2, pp. 2185- 2262). Amsterdam: Elsevier.
  37. Lucas, R. E. (1976). Econometric policy evaluation: A critique. Carnegie-Rochester Conference Series on Public Policy, 1, 19-46.
  38. Lucas, R. E., & Sargent, T. (1989). After Keynesian macroeconomics. In F. E. Morris (Ed.), After the Phillips curve: persistence of high inflation and high unemployment. Proceedings of a Conference Held at Edgartown, Massachusetts June 1978 (pp. 49- 72). Boston: The Federal Reserve Bank of Boston.
  39. Maki, U. (2013). Contested modeling: the case of economics. In U. Gahde, S. Hartmann, & J. H. Wolf (Eds.), Models, simulations, and the reduction of complexity (pp. 87- 106). Berlin, Boston: de Gruyter.
  40. Nachane, D. (2016). Dynamic stochastic general equilibrium (DSGE) modelling in practice: identification, estimation and evaluation. Macroeconomics and Finance in Emerging Market Economies, 1-28.
  41. Pearson, E. S. (1966). Some thoughts on statistical inference. In The selected papers of E.S. Pearson (pp. 276-183). Cambridge: Cambridge University Press.
  42. Pesaran, M. H., & Smith, R. (1995). The role of theory in econometrics. Journal of Econometrics, 67(1), 61-79.
  43. Qin, D. (1997). The formation of econometrics: a historical perspective (1. paperback ed). Oxford: Clarendon Press.
  44. Qin, D. (2013). A history of econometrics: the reformation from the 1970s. Oxford: Oxford University Press.
  45. Reicher, C. (2016). A note on the identification of dynamic economic models with generalized shock processes. Oxford Bulletin of Economics and Statistics, 78(3), 412-423.
  46. Rios-Rull, J.-V., Schorfheide, F., Fuentes-Albero, C., Kryshko, M., & Santaeulalia-Llopis, R. (2012). Methods versus substance: Measuring the effects of technology shocks. Journal of Monetary Economics, 59(8), 826-846.
  47. Rothenberg, T. J. (1971). Identification in parametric models. Econometrica, 39(3), 577-591.
  48. Romer, P. (2016). The trouble with macroeconomics. Commons.
  49. Sanbonmatsu, D. M., Posavac, S. S., Behrends, A. A., Moore, S. M., & Uchino, B. N. (2015). Why a confirmation strategy dominates psychological science. PLOS ONE, 10(9), e0138197.
  50. Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1-48.
  51. Sims, C. A. (1996). Macroeconomics and methodology. The Journal of Economic Perspectives, 10(1), 105-120.
  52. Smets, F., & Wouters, R. (2003). An estimated dynamic stochastic general equilibrium model of the Euro area. Journal of the European Economic Association, 1(5), 1123-1175.
  53. Smets, F., & Wouters, R. (2007). Shocks and frictions in US business cycles: A bayesian DSGE approach. The American Economic Review, 97(3), 586-606.
  54. Solow, R. (2010). Building science for a real world. Testimony presented at a hearing before the Subcommittee on Investigations and Oversight, Committee on Science and Technology (US House of Representatives). Retrieved from https://www.gpo. gov/fdsys/pkg/CHRG-111hhrg57604/pdf/CHRG-111hhrg57604.pdf.
  55. Spanos, A. (1990). The simultaneous-equations model revisited. Journal of Econometrics, 44(1-2), 87-105.
  56. Tovar, C. E. (2009). DSGE models and central banks. Economics: The Open-Access, Open-Assessment E-Journal, 3(2009-16), 1-32. ja.2009-16.
  57. van Fraassen, B. C. (1999). Conditionalization, a new argument for. Topoi, 18(2), 93-96.
  58. van Fraassen, B. C. (2008). Scientific representation: paradoxes of perspective. New York: Oxford University Press.
  59. van Fraassen, B. C. (2009). The perils of Perrin, in the hands of philosophers. Philosophical Studies, 143(1), 5-24.
  60. van Fraassen, B. C., & Halpern, J. Y. (2016). Updating probability: tracking statistics as criterion. The British Journal for the Philosophy of Science, axv027. https://doi. org/10.1093/bjps/axv027.
  61. Welfe, W. (2013). Macroeconometric models. Berlin: Springer.
  62. Williamson, S. (2017, January). The trouble with Paul Romer. Retrieved January 25, 2017, from Html.
  63. Williamson, T. (2002). Knowledge and its limits. Oxford: Oxford University Press.
  64. Wimsatt, W. C. (2007). Re-engineering philosophy for limited beings: piecewise approximations to reality. Cambridge, MA: Harvard University Press.
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