Nowcasting Ukraine's GDP Using a Factor-Augmented VAR (FAVAR) Model
a National Bank of Ukraine, Kyiv, Ukraine

This article presents an approach for nowcasting the current value of Ukraine’s quarterly GDP. The approach uses leading indicators with a different disclosure frequency. We generalize data from a set of explanatory variables into several factors by using principal components analysis and estimate the factor-augmented VAR (FAVAR) model. Our system incorporates new data as they are published throughout a quarter to adjust GDP nowcasts. In addition, we research the influence of separate data releases on the accuracy of forecasts.

Publication History
Avaliable online 27 December 2017
Full Text
Cite as: Grui, A., Lysenko, R. (2017). Nowcasting Ukraine's GDP Using a Factor-Augmented VAR (FAVAR) Model. Visnyk of the National Bank of Ukraine, 242, 5-13.
Citation Format


Altissimo, F., Bassanetti, A., Cristadoro, R., Forni, M., Hallin, M., Lippi, M., Veronese, G. (2001). EuroCOIN: A real time coincident indicator of the euro area business cycle. Discussion Papers, 3108. CEPR.

Angelini, E., Camba-Mendez, G., Giannone, D., Reichlin, L., Rünstler, G. (2011). Short-term forecasts of euro area GDP growth. Econometrics Journal, 14(1), C25-C44.

Artis, M. J., Banerjee, A., Marcellino, M. (2005). Factor forecasts for the UK. Journal of Forecasting, 24(4), 27-298.

Banbura, M., Runstler, G. (2011). A look into the factor model black box: publication lags and the role of hard and soft data in forecasting GDP. International Journal of Forecasting, 27(2), 333-346.

Bernanke, B.S., Boivin, J. (2003). Monetary policy in a data-rich environment. Journal of Monetary Economics, 50(3), 525-546.

Bernanke, B.S., Boivin, J., Eliasz, P. (2005). Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach. Quarterly Journal of Economics, 120(1), 387-422.

Boivin, J., Ng, S. (2005). Understanding and comparing factor-based forecasts. International Journal of Central Banking, 1(3), 117-151. Retrieved from

Bragoli, D., Metelli, L., Modugno, M. (2014). The importance of updating: evidence from a Brazilian nowcasting model. Finance and Economics Discussion Series, 2014-94. Washington: Federal Reserve Board.

Brave, S.A., Butters, R. A. (2014). Nowcasting using the Chicago FED national activity index. Economic Perspectives, 38, 19-37. Retrieved from

Breitung, J., Eickmeier, S. (2006). Dynamic factor models. Modern Econometric Analysis, 25-40.

Brisson, M., Campbell, B., Galbraith, J. W. (2003). Forecasting some low-predictability time series using diffusion indices. Journal of Forecasting, 22(6-7), 515-531.

Cristadoro, R., Forni, M., Reichlin, L., Veronese, G. (2001). A core inflation index for the euro area. Working Papers, 435. Bank of Italy. Retrieved from

Forni, M., Giannone, D., Lippi, M., Reichlin, L. (2004). Opening the black box: structural factor models vs structural VARs. Working Paper Series, 712. European Central Bank. Retrieved from

Forni, M., Hallin, M., Lippi, M., Reichlin, L. (2005). The generalized dynamic factor model: one-sided estimation and forecasting. Journal of the American Statistical Association, 100(471), 830-840.

Giannone, D., Reichlin, L., Sala, L. (2004). Monetary policy in real time. NBER Macroeconomics Annual, 19, 161-200.

Giannone, D., Reichlin, L., Small, D. (2008). Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics, 55(4), 665-676.

Giannone, D., Reichlin, L., Small, D.H. (2006). Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases. Working Paper Series, 633. European Central Bank.

Gupta, R., Kabundi, A., Ziramba, E. (2010). The effect of defense spending on US output: a factor augmented vector autoregression (favar) approach. Defence and Peace Economics, 21(2), 135-147.

Itkonen, J. (2016). How do we know where the economy is heading today? Bank of Finland Bulletin, 90(3), 51-61.

Kaiser, H.F. (1960). The application of electronic computers to factor analysis. Educational and psychological measurement, 20(1), 141-151.

Kapetanios, G. (2004). A note on modelling core inflation for the UK using a new dynamic factor estimation method and a large disaggregated price index dataset. Economics Letters, 85(1), 63-69.

Lysenko, R., Kolesnichenko, N. (2016). Nowcasting of economic development indicators using the NBU's business survey results. Visnyk of the National Bank of Ukraine, 235, 43-56.

Porshakov, A., Deryugina, E., Ponomarenko, A. A., Sinyakov, A. (2015). Nowcasting and short-term forecasting of Russian GDP with a dynamic factor model. Discussion Papers, 19/2015, 4-40. BOFIT Bank of Finland,

Stock, J.H., Watson, M.W. (2002). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97(460), 1167-1179.

Stock, J.H., Watson, M.W. (2006). Forecasting with many predictors. Handbook of Economic Forecasting, Chapter 10, 515-554.

Stock, J.H., Watson, M.W. (1999). Forecasting inflation. Journal of Monetary Economics, 44(2), 293-335.

Rights and Permissions
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.
Submit Your Paper