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DSGE Model-Based Forecasting of Non-modelled Variables

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  • Frank Schorfheide
  • Keith Sill
  • Maxym Kryshko

Abstract

This paper develops and illustrates a simple method to generate a DSGE model-based forecast for variables that do not explicitly appear in the model (non-core variables). We use auxiliary regressions that resemble measurement equations in a dynamic factor model to link the non-core variables to the state variables of the DSGE model. Predictions for the non-core variables are obtained by applying their measurement equations to DSGE model-generated forecasts of the state variables. Using a medium-scale New Keynesian DSGE model, we apply our approach to generate and evaluate recursive forecasts for PCE inflation, core PCE inflation, the unemployment rate, and housing starts along with predictions for the seven variables that have been used to estimate the DSGE model.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 14872.

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Date of creation: Apr 2009
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Publication status: published as Schorfheide, Frank & Sill, Keith & Kryshko, Maxym, 2010. "DSGE model-based forecasting of non-modelled variables," International Journal of Forecasting, Elsevier, vol. 26(2), pages 348-373, April.
Handle: RePEc:nbr:nberwo:14872

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  1. Coenen, Günter & McAdam, Peter & Straub, Roland, 2008. "Tax reform and labour-market performance in the euro area: A simulation-based analysis using the New Area-Wide Model," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 32(8), pages 2543-2583, August.
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Citations

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Cited by:
  1. Rangan Gupta & Rudi Steinbach, 2010. "Forecasting Key Macroeconomic Variables of the South African Economy: A Small Open Economy New Keynesian DSGE-VAR Model," Working Papers 201019, University of Pretoria, Department of Economics.
  2. Andrés González Gómez & Lavan Mahadeva & Diego Rodríguez & Luis Eduardo Rojas, . "Monetary Policy Forecasting in a DSGE Model with Data that is Uncertain, Unbalanced and About the Future," Borradores de Economia 559, Banco de la Republica de Colombia.
  3. Herbst, Edward & Schorfheide, Frank, 2012. "Evaluating DSGE model forecasts of comovements," Journal of Econometrics, Elsevier, Elsevier, vol. 171(2), pages 152-166.
  4. Martin Fukac & Vladimir Havlena, 2011. "Note on the role of natural condition of control in the estimation of DSGE models," Research Working Paper, Federal Reserve Bank of Kansas City RWP 11-03, Federal Reserve Bank of Kansas City.
  5. Wolden Bache, Ida & Sofie Jore, Anne & Mitchell, James & Vahey, Shaun P., 2011. "Combining VAR and DSGE forecast densities," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 35(10), pages 1659-1670, October.
  6. Rangan Gupta & Patrick T. kanda & Mampho P. Modise & Alessia Paccagnini, 2013. "DSGE Model-Based Forecasting of Modeled and Non-Modeled Inflation Variables in South Africa," Working Papers 201374, University of Pretoria, Department of Economics.
  7. Alessia Paccagnini, 2012. "Comparing Hybrid DSGE Models," Working Papers, University of Milano-Bicocca, Department of Economics 228, University of Milano-Bicocca, Department of Economics, revised Dec 2012.
  8. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters, Bank for International Settlements, in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8 Bank for International Settlements.
  9. Stelios Bekiros & Alessia Paccagnini, 2013. "On the predictability of time-varying VAR and DSGE models," Empirical Economics, Springer, Springer, vol. 45(1), pages 635-664, August.
  10. Marco Del Negro & Frank Schorfheide, 2012. "DSGE model-based forecasting," Staff Reports, Federal Reserve Bank of New York 554, Federal Reserve Bank of New York.

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