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.
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. https://doi.org/10.2139/ssrn.1005171
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. https://doi.org/10.1111/j.1368-423X.2010.00328.x
Artis, M. J., Banerjee, A., Marcellino, M. (2005). Factor forecasts for the UK. Journal of Forecasting, 24(4), 27-298. https://doi.org/10.1002/for.957
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. https://doi.org/10.1016/j.ijforecast.2010.01.011
Bernanke, B.S., Boivin, J. (2003). Monetary policy in a data-rich environment. Journal of Monetary Economics, 50(3), 525-546. https://doi.org/10.1016/S0304-3932(03)00024-2
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. https://doi.org/10.1162/0033553053327452
Boivin, J., Ng, S. (2005). Understanding and comparing factor-based forecasts. International Journal of Central Banking, 1(3), 117-151. Retrieved from https://www.ijcb.org/journal/ijcb05q4a4.pdf
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. https://doi.org/10.17016/feds.2014.94
Brave, S.A., Butters, R. A. (2014). Nowcasting using the Chicago FED national activity index. Economic Perspectives, 38, 19-37. Retrieved from https://www.chicagofed.org/publications/economic-perspectives/2014/1q-brave-butters
Breitung, J., Eickmeier, S. (2006). Dynamic factor models. Modern Econometric Analysis, 25-40. https://doi.org/10.1007/3-540-32693-6_3
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. https://doi.org/10.1002/for.872
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 https://www.bancaditalia.it/pubblicazioni/temi-discussione/2001/2001-0435/index.html
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 https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp712.pdf
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. https://doi.org/10.1198/016214504000002050
Giannone, D., Reichlin, L., Sala, L. (2004). Monetary policy in real time. NBER Macroeconomics Annual, 19, 161-200. https://doi.org/10.1086/ma.19.3585335
Giannone, D., Reichlin, L., Small, D. (2008). Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics, 55(4), 665-676. https://doi.org/10.1016/j.jmoneco.2008.05.010
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. https://doi.org/10.17016/feds.2005.42
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. https://doi.org/10.1080/10242690903569056
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. https://doi.org/10.1177/001316446002000116
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. https://doi.org/10.1016/j.econlet.2003.07.018
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. https://doi.org/10.26531/vnbu2016.235.043
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, https://doi.org/10.2139/ssrn.2616248
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. https://doi.org/10.1198/016214502388618960
Stock, J.H., Watson, M.W. (2006). Forecasting with many predictors. Handbook of Economic Forecasting, Chapter 10, 515-554. https://doi.org/10.1016/S1574-0706(05)01010-4
Stock, J.H., Watson, M.W. (1999). Forecasting inflation. Journal of Monetary Economics, 44(2), 293-335. https://doi.org/10.1016/S0304-3932(99)00027-6