Articles by : Zubritskyi A. № 1/2015
Forecasting methods and models
ZUBRITSKYI A. 1
1 Research Institute of Financial Law (Irpin’)
Ukraine's export potential in the context of comparative advantages
ABSTRACT ▼
Export diversification and realization of the export potential of the national economy under libera-lization of world commodity markets is a major challenge for Ukraine. Given this, the identification of promising directions for improving the quantitative and qualitative structure of Ukrainian exports is particularly relevant.
The purpose of this article is to identify commodity items of domestic exports, which may be ex-ported with comparative advantages in the short term.
To identify products, which are the most promising in terms of realization of export potential, the study used Hausmann-Klinger original approach. In this case, there are two main criteria of prospec-tivity: simplicity of the transition to the production of goods on the basis of available resources and technology, as well as the potential of generating export revenues.
Based on the 2013 estimates, revealed that the main sectors of the export potential realization are livestock products (5 items), food products (6 items), chemical products (4 items), and metal products (4 items).At the same time, a group of commodity items was identified, categorized as prospective for the whole period under review (2007-2013.). It was established that the proportion of prospective commodity items in total exports did not exceed 2%.
Scientific and practical value of the study consists in the identification of commodity items, which, on condition of efficient promotion (including fiscal one) can strengthen the domestic export sector in the short run.
Keywords: export basket, revealed comparative advantage, commodity space, export potential
JEL: F11, F40
Article in Ukrainian (pp. 140 - 154) | Download | Downloads :831 |
REFERENCES ▼
1. Hausmann, Ricardo, Hwang, Jason and Rodrik, Dani (2005). What You Export Matters. CID Working Paper No. 123, December. doi:
doi.org/10.3386/w11905
2. Hausmann, Ricardo and Klinger, Bailey (2006). Structural Transformation and Patterns o f Comparative Advantage in the Product Space. CID Working Paper No. 128, August. doi:
doi.org/10.2139/ssrn.939646
3. Hausmann, Ricardo and Klinger, Bailey (2007). The Structure of the Product Space and the Evolution of Comparative Advantage. CID Working Paper No. 146, April. doi:
doi.org/10.1920/wp.cem.2007.2207
4. Mazaraki, A.A. (Ed)., Yukhymenko, V.V., Hrebelʹnyk O.P. (2007). Management of export potential of Ukraine. Kyiv National University of Trade and Economics Institute, Kyiv, 210 p. [in Ukrainian].
5. Melnyk, T. (2008). Export potential of Ukraine: methodology of evaluation and analysis. Mizhnarodna ekonomichna polityka – International economic policy, 1-2 (8-9), 241-271 [in Ukrainian].
6. Ivashhuk, S. (2009). Export Strategy in the modern system of public administration. Mizhnarodna ekonomichna polityka – International Economic Policy, 1-2 (10-11), 102-128 [in Ukrainian].
7. Heyets, V.M., Shynkaruk, L.V. (Eds) (2014). Integration opportunities in Ukraine: Prospects and Implications. Institute for Economics and Forecasting, NAS of Ukraine. Kyiv, 92 p. [in Ukrainian].
8. 8. Kaukin, A., Freinkman, L. (2009). Structure and productivity of Russian exports. Ekonomicheskaya politika – Economic policy, 5, 99-117 [in Russian].
9. Artemyeva, E., Balandina, M., Vorobiev, P., etc. (2010). Growth basket: potential export industries of the Sverdlovsk region. Zhurnal novoy ekonomicheskoy assotsiatsii – Journal of the New Economic Association, 6, 62-81 [in Russian].
10. Gnidchenko, A.A. (2014). Improving methods for assessing the structure and base of export potential due to the diversification of export. Zhurnal novoy ekonomicheskoy assotsiatsii – Journal of the New Economic Association, 1(21), 83-109 [in Russian].
11. Database UN statistics concerning trade in goods. Retrieved from
comtrade.un.org/data/ [in Ukrainian].
12. Database of the World Bank's international trade and tariffs WITS. Retrieved from
wits.worldbank.org/ [in Ukrainian].
№ 2/2015
Forecasting methods and models
MOKLIAK Maksym 1, CHERNOV P. 2, VDOVYCHENKO A. 3, ZUBRITSKYI A. 4
1Department of Coordination and Monitoring, State Fiscal Service of Ukraine
2Department of Coordination and Monitoring, State Fiscal Service of Ukraine
3 Research Institute of Financial Law (Irpin’)
4 Research Institute of Financial Law (Irpin’)
Spatial approach in forecasting tax revenues
ABSTRACT ▼
Study of tax revenue forecasting accuracy is an integral part of the planning and analysis of the tax system indicators as well as one of the central functions of the State Fiscal Service of Ukraine (SFS). This process is complicated by the lack of development of complex statistical apparatus that can be applied for forecasting purposes taking into account the size of tax data available in SFS of Ukraine. The purpose of this article is to demonstrate the advantages of us-ing panel regressions of various fit in forecasting tax revenues at the regional level. To analyze the advantages and disadvantages of usage panel data structure in forecasting tax revenues by region, the authors emplоy econometric tools, including univariate time series models for each region, spatial and dynamic panel regression. The scientific result is that, on the basis of the analysis, the authors prove the feasibility of using panel regressions with the purpose of forecasting taxes in cases with limited time series data. These developments in modeling and fore-casting tax revenues also have practical value, because they can be used directly in the analytical activities of SFS of Ukraine at the regional level.
Keywords: tax revenues, time series, dynamic panel regression spatial panel regression
JEL: C530, H20
Article in Ukrainian (pp. 7 - 20) | Download | Downloads :878 |
REFERENCES ▼
1. Barnard, Jerald R., Dent, Warren T. (1979). State tax revenues-new methods of forecasting. The Annals of Regional Science, 13 (3), 1-14. doi:
doi.org/10.1007/BF01287742
2. Litterman, Robert B., Supel, Thomas M. (1983). Using Vector Autoregressions to Measure the Uncertainty in Minnesota's Revenue Forecasts. Federal Reserve Bank of Minneapolis Quarterly Review, 7 (2), 10-22.
3. Gianakis, Gerasimos A. and Frank, Howard A. (1993). Implementing Time Series Forecasting Models: Considerations for Local Governments. State & Local Government Review, 25 (2), 130-144.
4. Mocan, H. Naci, Azad, Sam (1995). Accuracy and rationality of state General Fund Revenue forecasts: Evidence from panel data. International Journal of Forecasting, 11, 417-427. doi:
doi.org/10.1016/0169-2070(95)00592-9
5. Cirincione, C., Gurrieri, G. A., van De Sande, B. (1999). Municipal Government Revenue Forecasting: Issues of Method and Data. Public Budgeting & Finance, 19 (1), 26-46. doi:
doi.org/10.1046/j.0275-1100.1999.01155.x
6. Forecasting Local Revenues and Expenditures (2007). Local Budgeting. Washington, DC: The World Bank, 53-77.
7. Nemeth, Adam (2012). Assessment of Quantitative Techniques for Local Business Tax Forecasting in Cities with County Status. Master of Arts in Public Policy Thesis. CEU eTD Collection. Retrieved from
www.etd.ceu.hu/2012/nemeth_adam.pdf
8. McNichol, Elizabeth C. (2014). Improving State Revenue Forecasting: Best Practices for a More Trusted and Reliable Revenue Estimate. Center for Budget and Policy Priorities Report. Retrieved from
www.cbpp.org/cms/index.cfm?fa=view&id=4185.
9. Getis, Arthur (2008). A History of the Concept of Spatial Autocorrelation: A Geographer's Perspective. Geographical Analysis, 40, 297-309. doi:
doi.org/10.1111/j.1538-4632.2008.00727.x
10. Cliff, A. D. and Ord, J. K. (1973). Spatial Autocorrelation. London: Pion.
11. Paelinck, J. H. P., and Klaassen, L. H. (1979). Spatial Econometrics. Westmead, Farnborough, England: Saxon House.
12. Anselin, L. (1988). Spatial Econometrics: Methods and Models. Dordrecht, The Netherlands: Kluwer Academic Publishers. doi:
doi.org/10.1007/978-94-015-7799-1
13. Getis, A. and Griffith, D. (2002). Comparative Spatial Filtering in Regression Analysis. Geographical Analysis, 34, 130-40. doi:
doi.org/10.1111/j.1538-4632.2002.tb01080.x
14. Arellano, M. and Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment. Review of Economic Studies, 58, 277-297. doi:
doi.org/10.2307/2297968
15. Baltagi, Badi H. (2006). Forecasting with panel data. Deutsche Bundesbank Discussion Paper Series 1: Economic Studies, 25, 28.
16. Baltagi, Badi H., Fingleton, Bernard, Pirotte, Alain (2014). Estimating and Forecasting with a Dynamic Spatial Panel Data Model. Oxford Bulletin of Economics and Statistics, 1(76), 112-138. doi:
doi.org/10.1111/obes.12011
17. Drukker, David M., Peng, Hua, Prucha, Ingmar R. (2013). Creating and managing spatial-weighting matrices with the spmat command. The Stata Journal, 2, 242-286.
18. Drukker, David M., Prucha, Ingmar R., Raciborski, Rafal (2013). A command for estimating spatial-autoregressive models with spatial-autoregressive disturbances and additional endogenous variables. The Stata Journal, 2, 242-286.
19. Practical application of methods of forecasting of budget revenues in the case of Ukraine. Irpin': NDI finansovoho prava, 2014, 29 p. [in Ukrainian].
20. Tobler, W., (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46 (2), 234-240. doi:
doi.org/10.2307/143141