Dynamic factor model of gdp short-term forecasting of Ukraine

Larysa Mykolaivna Zomchak, Anastasiia Serhiivna Rakova


Introduction. The short-term (quarterly) forecast of GDP is based on factor variables of the financial and non-financial sectors of the economy, indicators of foreign economic activity, indicators of economic activity, etc. Although the statistics of these indicators are available on a monthly basis, but its disclosure comes with a certain lag, and values over time can be reviewed and clarified. These data can be used to estimate the quarterly value of GDP before the official information about its empirical volume is published.

Purpose. The article aims to forecast the quarterly real GDP of Ukraine by means of a dynamic factor model on the basis of the quarterly and monthly values of the main social and economic macro indicators of Ukraine.

The method (methodology). To achieve the task, we have used the econometric methods of macroeconomic modelling, namely the dynamic factor model, the Kalman filter, the method of the main components, etc.

Results. The forecast of GDP of Ukraine for the first two quarters of 2018 has been obtained with the help of a dynamic factor model. On the basis of comparison of the obtained forecast with the empirical values of Ukraine's GDP for the similar period, which is published by the Ministry of Finance of Ukraine, it has been proven the adequacy of the model and the high quality of the results has been concluded.


dynamic factor model; real GDP; short-term forecast; Kalman filter; method of the main components; macroeconomic indicator

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Cheung, C., & Demers, F. (2007). Evaluating forecasts from factor models for Canadian GDP growth and core inflation (No. 2007, 8). Bank of Canada Working Paper.

Camacho, M., & Sancho, I. (2003). Spanish diffusion indexes. Spanish economic review, 5(3), 173-203.

den Reijer, A. H. (2005). Forecasting Dutch GDP using large scale factor models (No. 028). Netherlands Central Bank, Research Department.

Schneider, M., & Spitzer, M. (2004). Forecasting Austrian GDP using the generalized dynamic factor model (No. 89).

Urasawa, S. (2014). Real-time GDP forecasting for Japan: A dynamic factor model approach. Journal of the Japanese and International Economies, 34, 116-134.

Dias, F., Pinheiro, M., & Rua, A. (2015). Forecasting Portuguese GDP with factor models: pre-and post-crisis evidence. Economic Modelling, 44, 266-272.

Rusnák, M. (2016). Nowcasting Czech GDP in real time. Economic Modelling, 54, 26-39.

Antipa, P., Barhoumi, K., Brunhes-Lesage, V., & Darné, O. (2012). Nowcasting German GDP: A comparison of bridge and factor models. Journal of Policy Modeling, 34(6), 864-878.

Gupta, R., & Kabundi, A. (2008). A dynamic factor model for forecasting macroeconomic variables in South Africa. International Journal of Forecasting.

Bessec, M. (2013). Short‐Term Forecasts of French GDP: A Dynamic Factor Model with Targeted Predictors. Journal of Forecasting, 32(6), 500-511.

Schumacher, C. (2007). Forecasting German GDP using alternative factor models based on large datasets. Journal of Forecasting, 26(4), 271-302.

Artis, M. J., Banerjee, A., & Marcellino, M. (2005). Factor forecasts for the UK. Journal of forecasting, 24(4), 279-298.

Ajevskis, V., & DĀVIDSONS, G. (2008). Dynamic Factor Models in Forecasting Latvia's Gross Domestic Product (No. 2008/02). Riga: Latvijas Banka.

State Statistics Service of Ukraine. URL: http://www.ukrstat.gov.ua/

Ministry of Finance of Ukraine URL: https://index.minfin.com.ua/

Michałek, A. (2010). The Importance of Calculating the Potential Gross Domestic Product in the Context of the Taylor Rule. Dynamic Econometric Models, 10, 132-143.

Orphanides, A., & Norden, S. V. (2002). The unreliability of output-gap estimates in real time. Review of economics and statistics, 84(4), 569-583.

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

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.

Demetra+ URL: https://ec.europa.eu/eurostat/cros/content/software-jdemetra_en.

DOI: http://dx.doi.org/10.35774/econa2018.04.017

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Science Works Journal "Ekonomichnyy analiz"

ISSN 1993-0259 (Print)  ISSN 2219-4649 (Online) DOI: 10.35774/econa

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