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.

Вплив Агрохолдингів На Розвиток Села В Умовах Децентралізації Економіки

“…* Індикатор конкретних заходів стимулювання, в тому числі фінансових -RDI, регіональна співпраця -RDRP Джерело: Євростат [24] Іншим показником, який демонструє вразливість асоціативних форм щодо цілі максимального економічного розвитку сільських територій (ціль 3), є можливості просування холдингових компаній -ролі фінансової підтримки для розвитку холдингових компаній та розвитку села. Рівень коефіцієнта регресії мінімальний і від'ємний (−0,034).…”

“…Щоб оцінити вплив сільського господарства на розвиток регіональної економіки, у дослідженні, проведеному [20 ], було застосовано аналіз витрат і випуску. Крім того, автори розробили регіональну модель, здатну кількісно визначити внесок первинного сектора в Дві важливі статті в роз'ясненні питань прогнозування вартості продукції як чинника сталого розвитку аграрного сектору реалізовано в [23] і [24]. У дослідженні впливу автори демонструють, що модель SARIMA (Seasonal Autoregressive Integrated Moving Average) може бути використана щодо таких факторів, як доступ до транспортної мережі, обсяги імпорту та експорту продукції, логістичний перерозподіл вантажів, зміни в аграрній політиці.…”

Agricultural business in independent Ukraine: thirty-year dynamics of the reorganization process

“…It seems that since 1999 this indicator has fluctuated almost around the same value: without any signs of falls or grows. Official data [41] show that number of livestock is different in groups, but the total tendency of the number of cows, pigs, sheep, and goats in farms decreases year by year. It can be illustrated with infographics in the Table 1.…”