Introduction. GDP statistics is usually quarterly and with a significant delay, and the data of many other economic indicators (average wages, unemployment, exchange rates, etc.) are monthly or have an even higher frequency. Such indicators often carry important information about the current state of the economy and it is important to use this data with a high frequency to obtain qualitative short-term forecasts. That is why methods that use mixed frequency data are becoming increasingly popular in predicting current system states and in short-term forecasting.
Because the official statistics of Ukraine's GDP is released with a delay, there is a need in current forecasting of quarterly GDP or so-called nowcasting. This for the other basic economic indicators (that determine quarterly GDP, but are published with higher frequency (monthly or even more often) or with the same frequency) can be used.
Purpose. The purpose of the investigation is nowcasting of the quarterly Ukrainian real GDP with a small dynamic factor model based on the quarterly and monthly values of the basic socio-economic macroeconomic indicators of Ukraine’s development. The dependence of the indicators on the nowcast quality can be investigated with the release calendar of the statistical information.
Results. The dynamic factor model of Ukraine's GDP is based on the statistics on the 11 main indicators of the socio-economic development of Ukraine from 2002 to 2018. The input data are macroeconomic indicators, namely: volume of industrial products sold, average monthly nominal wages and salaries per employee, consumer price indices for goods and services, official exchange rate of Hryvnia against US dollar, average salary, turnover of retail trade, agricultural output, gross domestic product, export of goods and services, import of goods and services, capital investments, income of the population. The input data are mixed-frequency. A dynamic factor model is based on the assumption that a small number of factors can explain a large part of the fluctuations of many macroeconomic variables, what’s more predictors can be unobservable. Influence of each of the available indicators allows us to understand what indicators are necessary for an early forecast, which will allow us to do nowcast after the last important indicator
Conclusion. The forecast of quarterly Ukrainian GDP was developed for the last two quarters of 2018 and the first quarter of the 2019. The nowcasting of the third quarter of 2018 is based on data published prior to the official publication of GDP over this period, it is all monthly figures other than volumes of agriculture and quarterly investments that are already available in September 2018. Using the release calendar of statistical information, the change in model error was estimated, and it was found that science-education can be obtained after the publication of the indicator of average wages, average incomes and industrial output, i.e. on the 53rd day after the reporting period. All subsequent publications, namely the export and import of goods and services and capital investment, do not have a significant impact on improving the outcomes of the forecast.
1. Giannone D., Reichlin L., Small D. (2008).Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics, 55, 665- 676.
2. Jansen W. Jos, Xiaowen Jin, Jasper M. de Winter (2016). Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts. International Journal of Forecasting, 32.2, 411-436.
3. Foroni C. & Marcellino M. (2014) A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates. International Journal of Forecasting, 30.3, 554-568.
4. Banbura M., Giannone D., Reichlin L. (2014) Nowcasting. ECB Working Paper, No. 1275.
5. Golinelli R., Parigi G. (2014) Tracking world trade and GDP in real time. International Journal of Forecasting, 30, 847-862.
6. Ferrara L. Marsilli C.(2018) Nowcasting global economic growth: A factor-augmented mixed-frequency approach . The World Economy.
7. Rusnák M. (2016) Nowcasting Czech GDP in real time. Economic Modelling, 54, 26-39.
8. Chernis T. & Sekkel R. (2017) A dynamic factor model for nowcasting Canadian GDP growth. Empirical Economics, 53.1, 217-234.
9. Modugno M., Soybilgen B., Yazgan E. (2016) Nowcasting Turkish GDP and news decomposition. International Journal of Forecasting,32.4, 1369-1384.
10. Aastveit Knut Are & Tørres Trovik (2012). Nowcasting Norwegian GDP: The role of asset prices in a small open economy. Empirical Economics,42.1, 95-119.
12. Gruy A. & Lysenko R. (2017) Rapid Forecasting of Ukraine's GDP by Factor-Added VAR-model (FAVAR). Bulletin of the National Bank of Ukraine, no 242, pp. 5-14. (in Ukrainian)
12. State Statistics Service of Ukraine. Retrieved from http://www.ukrstat.gov.ua/ (Date of address 10 March, 2019) (in Ukrainian)