Turning points in agriculture development in Ukraine: results of analysis on the base of purified data
Abstract
Purpose. The purpose of the paper is to study agriculture development in independent Ukraine using methods for decomposition of data sets to obtain structural components according to their level of determination and to investigate main tendencies without random fluctuation.
Methodology / approach. This research uses econometrics methods of regression and correlation dynamics, data filtering from random factors impact, comparison method to reconstruct trends in agriculture development. The accent is made on the differences between results of analysis on the base of uncleaned and purified data. The cleaning process was made by the Hodrick-Prescott method for data sets decomposition without loss of information into several functions that describe behavior of system formed under deterministic, periodical, and stochastic influence. The calculations and their visualization were held by means of GNU Octave. The research covered the period of 1996–2018 years in development of agriculture, forestry, and fisheries in Ukraine.
Results. This paper emphasizes on the necessity to clean data before its analysis. Unlike the analysis based on the raw data the reconstruction of relationships between filtered data sets allows building models of agriculture dynamics more precisely. The article presents reconstructed main correlated dynamics that were formed by deterministic impacts in Ukraine agriculture. Besides, the crucial turning points in Ukraine agriculture development were revealed because of analysis of filtered data. It is shown how data cleaning raises analysis quality.
Originality / scientific novelty. Reconstruction of economy regression models on the dynamic data sets can be followed by the risks of wrong interpretation of results. To have correct explanation of regularity it matters to eliminate from data sets the fluctuations formed by random impacts and to study relation of tendencies in connection with structural shifts in the development of the country. The advisability and the necessity to use such approaches are proved by this research that was held on the base of statistical data from World Bank official site. It is the first reconstruction of the long-term agriculture dynamics of Ukraine on the base of filtered correlated data sets of 1996–2018. The main advantages of this research are the revealed turning points in Ukraine agriculture development that can be used by other researchers in investigation of economy and economic history.
Practical value / implications. To raise precision of analysis and avoid wrong interpretation of outcomes it should be taken into consideration all factors that impact on events and thus it is rational to verify data and clean them from random oscillations especially studying long-term tendencies in the development of such systems as economy branches or country entire. Moreover, it helps to reveal peaks of growth and recession points in business activity and make more coherent predictions on the future development. The article presented the effectiveness of filter methods applying and results of analysis that can be used in management and forecast of long-term development in Ukraine agriculture. Such methodic can be applied to the analysis of both individual industries and the economy as a whole.
References
2. Nigatu, G. and Adjemian, M. (2020), A wavelet analysis of price integration in major agricultural markets. Journal of Agricultural and Applied Economics, vol. 52(1), pp. 117–134. https://doi.org/10.1017/aae.2019.35.
3. Krishnamurthy, V., Leoff, E., and Sass, J. (2018), Filterbased stochastic volatility in continuous-time hidden Markov models. Econometrics and Statistics, vol. 6, pp. 1–21. https://doi.org/10.1016/j.ecosta.2016.10.007.
4. Leippold, M. and Yang, H. (2019), Particle filtering, learning, and smoothing for mixed-frequency state-space models. Econometrics and Statistics, vol. 12, pp. 25–41. https://doi.org/10.1016/j.ecosta.2019.07.001.
5. Czudaj, R. L. (2019), Dynamics between trading volume, volatility, and open interest in agricultural futures markets: a Bayesian time-varying coefficient approach. Econometrics and Statistics. vol. 12, pp. 78–145. https://doi.org/10.1016/j.ecosta.2019.05.002.
6. Codjo, O. S, Acclassato, D., Fiamohe, R., Kpenavoun, S, and Biaou, G. (2020), Comparative analysis of the preference of producers and processors for domestic rice production contracts in Benin. Agribusiness, vol. 36, pp. 242–258. https://doi.org/10.1002/agr.21618.
7. Ayuda, M. I. and Pinilla, V. (2020), Agricultural exports and economic development in Spain during the first wave of globalisation. Scandinavian Economic History Review, pp. 1–18. https://doi.org/10.1080/03585522.2020.1786450.
8. Baležentis, T. and Lansink, A. O. (2019), Measuring dynamic biased technical change in Lithuanian cereal farms. Agribusiness, vol. 36, is. 2, pp. 208–225. https://doi.org/10.1002/agr.21623.
9. Hachula, M. and Rieth, M. (2020), Estimating the Impact of Financial Investments on Agricultural Futures Prices using Changes in Volatility. American Journal of Agricultural Economics, vol. 102, issue 3, pp. 759–785. https://doi.org/10.1093/ajae/aaz024.
10. Abay, K. A. (2020), Measurement errors in agricultural data and their implicationson marginal returns to modern agricultural inputs. Agricultural Economics, vol. 51, pp. 323–341. https://doi.org/10.1111/agec.12557.
11. Ramsey, A. F. (2019), Probability distributions of crop yields: a bayesian spatial quantile regression approach. American Journal of Agricultural Economics, vol. 102, is. 1, pp. 220–239. https://doi.org/10.1093/ajae/aaz029.
12. Sotoudeh, M. A. and Worthington, A. C. (2017), Nonlinear effects of oil prices on consumer prices: a comparative study of net oil consuming and producing countries. Review of Economic Analysis, vol. 9, pp. 57–79.
13. Peters, E. E. (2004), Fractal market analysis: applying Chaos theory to investment and economics (Internet-trading Trans.), Internet-trading, Moscow, Russia. (Oraginal work published 2003).
14. Hodrick, R. J. and Prescott, E. C. (1997), Postwar U.S. business cycles: an empirical investigation. Journal of Money, Credit and Banking, vol. 29, no. 1, pp. 1–16.
15. Bekaert, G., Hodrick, R. J. and Kiguel, A. (2019), Variance risk in global markets. SSRN Columbia Business School Research Paper Forthcoming. https://doi.org/10.2139/ssrn.3442649.
16. Reznikova, O. O., Voitovsky, K. E. and Lepikhov, A. V. (2020), Natsional’ni systemy otsinyuvannya ryzykiv i zahroz: krashchi svitovi praktyky, novi mozhlyvosti dlya Ukrayiny [National systems of risk and threat assessment: best world practices, new opportunities for Ukraine], NISS, Kyiv, Ukraine.
17. Tyshchenko, L. and Chaibok, A. (2017), Financial Stress Index for Ukraine. Bulletin of the National Bank of Ukraine, vol. 240, pp. 5–14.
18. Sviatets, Yu. A. (2017), Information field of historical research. Dnipropetrovsk University bulletin. History & archaeology series, vol. 25(1), pp. 29–42. https://doi.org/10.15421/261703.
19. Kobets, S. and Luzina, A. (2019), Application of adaptive models for forecasting a net sales. Efektyvna ekonomika, vol. 4, available at: http://www.economy.nayka.com.ua/?op=1&z=6991.
20. Polasek, W. (2011), The Hodrick-Prescott (HP) filter as a bayesian regression model. Economics Series (277). Institute for Advanced Studies, Vienna, Austria.
21. The World Bank (2020), Ukraine. World Development Indicators, available at: http://api.worldbank.org/v2/en/country/UKR?downloadformat=excel.
22. Dmytriieva, V. A. (2018), Tendencies in Ukrainian dynamics of crop production: effects of data sets smoothing. Efektyvna ekonomika, vol. 12. https://doi.org/10.32702/2307-2105-2018.12.87.
23. Davydenko, N. and Pasichnyk, Y. (2017), Features of socio-economic development of the Baltic States and Ukraine. Baltic Journal of Economic Studies, vol. 3, no. 5, pp. 97–102. https://doi.org/10.30525/2256-0742/2017-3-5-97-102.
24. The Verkhovna Rada of Ukraine (2004), The Law of Ukraine “On state support of agriculture of Ukraine”, available at: https://zakon.rada.gov.ua/laws/show/1877-15.
25. The Verkhovna Rada of Ukraine (2009), The Law of Ukraine “On amendments to certain laws of Ukraine concerning the improvement of mechanisms for state regulation of the market of agricultural products”, available at: https://zakon.rada.gov.ua/laws/show/1447-17.
26. AgroPortal (2019), Infographics. The first agricultural results of the year in infographics, available at: https://agroportal.ua/ua/publishing/infografika/pervye-agrarnye-itogi-goda-v-infografike.
27. Gutkevych, S. (2019), Investment attractiveness of industries: features and trends. Baltic Journal of Economic Studies, vol. 5, no. 3, pp. 50–58. https://doi.org/10.30525/2256-0742/2019-5-3-50-58.
