Nowcasting GDP Growth for Small Open Economies with a Mixed-Frequency Structural Model



This paper proposes a mixed-frequency small open economy structural model, in which the structure comes from a New Keynesian dynamic stochastic general equilibrium (DSGE) model. An aggregation rule is proposed to link the latent aggregator to the observed quarterly output growth via aggregation. The resulting state-space model is estimated by the Kalman filter and the estimated current aggregator is used to nowcast the quarterly GDP growth. Taiwanese data from January 1998 to December 2015 are used to illustrate how to implement the technique. The DSGE-based mixed-frequency model outperforms the reduced-form mixed-frequency model and the MIDAS model on nowcasting Taiwan’s quarterly GDP growth.


DSGE model Mixed frequency Nowcasting Kalman filter 

JEL Classification

C5 E1 



The authors are grateful for helpful comments from Kenneth West, Barbara Rossi, Frédérique Bec, Yu-Ning Huang, Yi-Ting Chen, and participants at the 2016 International Symposium in Computational Economics and Finance (ISCEF) in Paris.


  1. Aadland, D., & Huang, K. X. D. (2004). Consistent high-frequency calibration. Journal of Economic Dynamics and Control, 28, 2277–2295.CrossRefGoogle Scholar
  2. Aruoba, B., Diebold, F., & Scotti, C. (2009). Real-time measurement of business conditions. Journal of Business Economics and Statistics, 27(4), 417–427.CrossRefGoogle Scholar
  3. Bernanke, B. S., Gertler, M., & Watson, M. W. (1997). Systematic monetary policy and the effects of oil price shocks. Brookings Papers on Economic Activity, 1, 91–157.CrossRefGoogle Scholar
  4. Blanchard, O., & Kahn, C. (1980). The solution of difference equations under rational expectations. Econometrica, 48, 1305–1311.CrossRefGoogle Scholar
  5. Boivin, J., & Giannoni, M. (2006). DSGE models in a data-rich environment. NBER Working Paper.Google Scholar
  6. Calvo, G. (1983). Staggered prices in a utility maximizing framework. Journal of Monetary Economics, 12, 383–398.CrossRefGoogle Scholar
  7. Christiano, L. J., & Eichenbaum, M. (1987). Temporal aggregation and structural inference in macroeconomics. Carnegie-Rochester Conference Series on Public Policy, 26, 64–130.CrossRefGoogle Scholar
  8. Clarida, R., Gali, J., & Gertler, M. (2000). Monetary policy rules and macroeconomic stability: Evidence and some theory. Quarterly Journal of Economics, 115(1), 147–180.CrossRefGoogle Scholar
  9. Clements, M. P., & Galvao, A. B. (2008). Macroeconomic forecasting with mixed-frequency data: Forecasting US output growth. Journal of Business and Economic Statistics, 26, 546–554.CrossRefGoogle Scholar
  10. Durbin, J., & Koopman, S. J. (2001). Time series analysis by state space methods. Oxford: Oxford University Press.Google Scholar
  11. Edge, R., Kiley, M., & Laforte, J. (2008). A comparison of forecast performance between federal reserve staff forecasts, simple reduced-form models, and a DSGE model. Federal Reserve Board of Governors: Manuscript.Google Scholar
  12. Foroni, C., & Marcellino, M. (2014a). A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates. International Journal of Forecasting, 30, 554–568.CrossRefGoogle Scholar
  13. Foroni, C., & Marcellino, M. (2014b). Mixed-frequency structural models: Identification, estimation, and policy analysis. Journal of Applied Econometrics, 29, 1118–1144.CrossRefGoogle Scholar
  14. Foroni, C., Marcellino, M., & Schumacher, C. (2015). Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials. Journal of the Royal Statistical Society, Series A (Statistics in Society), 178(1), 57–82.CrossRefGoogle Scholar
  15. Gali, J., & Monacelli, T. (2005). Monetary policy and exchange rate volatility in a small open economy. Review of Economic Studies, 72, 707–734.CrossRefGoogle Scholar
  16. Ghysels, E., Santa-Clara, P., & Valkanov, R. (2004). The MIDAS touch: Mixed data sampling regression models. Chapel Hill, N.C.: Mimeo.Google Scholar
  17. Ghysels, E., Sinko, A., & Valkanov, R. (2006). MIDAS regressions: Further results and new directions. Econometric Reviews, 26(1), 53–90.CrossRefGoogle Scholar
  18. Giannone, D., Monti, F., & Reichlin, L. (2009). Incorporating conjunctural analysis in structural models. In V. Wieland (Ed.), The science and practice of monetary policy today (pp. 41–57). Berlin: Springer.Google Scholar
  19. Giannone, D., Reichlin, L., & Small, D. (2008). Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics, 55, 665–674.CrossRefGoogle Scholar
  20. Kim, T. B. (2010). Temporal aggregation bias and mixed frequency estimation of New Keynesian model. Mimeo: Duke University.Google Scholar
  21. Klein, P. (2000). Using the generalized Schur form to solve a multivariate linear rational expectations model. Journal of Economic Dynamics and Control, 24, 1405–1423.CrossRefGoogle Scholar
  22. Liu, H., & Hall, S. G. (2001). Creating high-frequency national accounts with state-space modelling: A Monte Carlo experiment. Journal of Forecasting, 20, 441–449.CrossRefGoogle Scholar
  23. Lubik, T., & Schorfheide, F. (2007). Do central banks respond to exchange rate movements? A structural investigation. Journal of Monetary Economics, 54, 1069–1087.CrossRefGoogle Scholar
  24. Mariano, R., & Murasawa, Y. (2010). A coincident index, common factors, and monthly real GDP. Oxford Bulletin of Economics and Statistics, 72, 27–46.CrossRefGoogle Scholar
  25. Mariano, R. S., & Murasawa, Y. (2003). A new coincident index of business cycles based on monthly and quarterly series. Journal of Applied Econometrics, 18(4), 427–443.CrossRefGoogle Scholar
  26. Rondeau, S. (2012). Sources of fluctuations in emerging markets: DSGE estimation with mixed frequency data. Ph.D. thesis, Columbia University.Google Scholar
  27. Rubaszek, M., & Skrzypczynski, P. (2008). On the forecasting performance of a small-scale DSGE model. International Journal of Forecasting, 24, 498–512.CrossRefGoogle Scholar
  28. Schorfheide, F., Sill, K., & Kryshko, M. (2010). DSGE model-based forecasting of non-modelled variables. International Journal of Forecasting, 26, 348–373.CrossRefGoogle Scholar
  29. Schorfheide, F., & Song, D. (2015). Real-time forecasting with a mixed frequency VAR. Journal of Business and Economic Statistics, 33, 366–380.CrossRefGoogle Scholar
  30. Schumacher, C., & Breitung, J. (2008). Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data. International Journal of Forecasting, 24(3), 386–398.CrossRefGoogle Scholar
  31. Sims, C. A. (2002). Solving linear rational expectations models. Computational Economics, 20, 1–20.CrossRefGoogle Scholar
  32. Stock, J. H., & Watson, M. W. (1989). New indexes of coincident and leading economic indicators. NBER Macroeconomics Annual, 4, 351–409.CrossRefGoogle Scholar
  33. Stock, J. H., & Watson, M. W. (1991). A probability model of the coincident economic indicators. In K. Lahiri & G. H. Moore (Eds.), Leading economic indicators (pp. 63–89). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  34. Teo, W. L. (2009). Estimated dynamic stochastic general equilibrium model of the Taiwanese economy. Pacific Economic Review, 14, 194–231.CrossRefGoogle Scholar
  35. Uhlig, H. (1999). A toolkit for analyzing nonlinear dynamic stochastic models easily. In R. Marimon & A. Scott (Eds.), Computational methods for the study of dynamic economies (pp. 114–142). Oxford: Oxford University Press.Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of EconomicsNational Central UniversityTaoyuanTaiwan, ROC
  2. 2.Department of EconomicsWestern Michigan UniversityKalamazooUSA
  3. 3.School of EconomicsZhongnan University of Economics and LawHubeiChina

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