The dynamic development of the digitized society generates large-scale information data flows. Therefore, data need to be compressed in a way allowing its content to remain complete and informative. In order for the above to be achieved, it is advisable to use the principal component method whose main task is to reduce the dimension of multidimensional space with a minimal loss of information. The article describes the basic conceptual approaches to the definition of principle components. Moreover, the methodological principles of selecting the main components are presented. Among the many ways to select principle components, the easiest way is selecting the first k-number of components with the largest eigenvalues or to determine the percentage of the total variance explained by each component. Many statistical data packages often use the Kaiser method for this purpose. However, this method fails to take into account the fact that when dealing with random data (noise), it is possible to identify components with eigenvalues greater than one, or in other words, to select redundant components. We conclude that when selecting the main components, the classical mechanisms should be used with caution. The Parallel analysis method uses multiple data simulations to overcome the problem of random errors. This method assumes that the components of real data must have greater eigenvalues than the parallel components derived from simulated data which have the same sample size and design, variance and number of variables. A comparative analysis of the eigenvalues was performed by means of two methods: the Kaiser criterion and the parallel Horn analysis on the example of several data sets. The study shows that the method of parallel analysis produces more valid results with actual data sets. We believe that the main advantage of Parallel analysis is its ability to model the process of selecting the required number of main components by determining the point at which they cannot be distinguished from those generated by simulated noise.
Обґрунтовано актуальність статистичного оцінювання та моделювання інвестиційної привабливості України. Зазначено, що важливу роль щодо залучення іноземного капіталу відіграє інвестиційний клімат країни. Існує низка проблем в частині інвестиційного менеджменту, зокрема, недосконалість нормативно-правового законодавства України, наростаючі фінансові ризики, низька інвестиційна активність власних інвесторів тощо. В ході аналізу встановлено, що показники експортно-імпортної діяльності країни суттєво впливають на залучення прямих іноземних інвестицій. Так, у 2020 р. порівняно з 2015 р. рівень прямих іноземних інвестицій у ВВП зменшився на 45%, що було спричинено негативним впливом імпортної квоти України, що становила 57% у 2015 р. та 40,4% у 2020 р., відповідно. Для оцінювання рівномірності територіального розподілу прямих іноземних інвестицій в Україні обчислено коефіцієнти локалізації та концентрації, які засвідчили про високий ступінь концентрації прямих іноземних інвестицій в окремих регіонах України, а саме в м. Київ (6,36) та Дніпропетровській (1,23) області. Нерівномірність розподілу частки ПІІ та частки зайнятого населення підтверджує розрахований коефіцієнт концентрації, що становить 0,48. Авторами здійснено кластерний аналіз регіонів України за інвестиційною привабливістю. До першого кластеру віднесено Дніпропетровську та Донецьку області, як промислові центри України, що характеризуються потужним виробничим потенціалом. Київ віднесено до окремого кластеру, як нетиповий регіон, що займає вигідне економіко-географічне положення, має добре розвинену інфраструктуру, потужний промисловий комплекс, високі показники соціально-економічного розвитку тощо. До другого кластеру віднесено всі решта (22) регіони України, які утворюють однорідну групу із середніми значеннями досліджуваних показників. Проаналізовано взаємозалежність між обсягами прямих іноземних інвестицій та макроекономічними показниками: обсягом експорту та імпорту, ВВП, курсом долара США, індексом споживчих цін, фактором часу. За результатами кореляційного аналізу встановлені висококорельовані змінні, які було вилучено з аналізу. Побудовано дві моделі, що описують взаємозв’язок ПІІ з показниками ЗЕД.
The importance of proper statistical support of the financial and banking system of the country is substantiated. It was noted that the statistics of the financial sector forms indicators of monetary and credit statistics, statistics of financial markets, statistics of financial accounts and indicators of financial stability. The criteria of financial stability were described: the financial system is liquidated and capitalized; payments and settlements are studied on time; the financial system effectively transforms free funds of citizens and businesses into loans and investments, etc. The goal of macroprudential policy is given and the system of indicators that characterizes the level of financial stability of the country is described. It was defined the key groups of indicators, on the basis of which the models of stress testing of banking systems by the National Bank of Ukraine are implemented. Particular attention was focused on the financial stress index, which covers five sub-indices (banking sector, corporate securities, government securities, foreign exchange market, household behavior) and takes into account the effect of their correlation.The dynamics of the IFS for January - May 2022 were analyzed. It was noted that at the beginning of January, the financial stress index was 0.01, and as of February 24, 2022, it has grown rapidly to 0.466 for all components, which indicates the systemic nature of stress for the financial sector in general. It was noted that the level of the sub-index of household behavior was relatively stable, since there was no significant outflow of deposits due to the preservation of public trust in the banking system. The value of the banking sector sub-index (0.075) improved by the end of May thanks to the improvement in the level of liquidity, while the currency sub-index (0.113) maintains quite high values. In May, the IFS fell to 0.265, but the level of stress in the financial market remains quite high by historical standards.The structure of the money supply was analyzed and it was determined that its share of cash was a third of the entire money supply in circulation. The share of cash in February and March 2022 significantly increased compared to the corresponding months of 2021 - by 3.2 percentage points and by 2.9 percentage points, respectively. The share of deposits in the national currency in March 2022 amounted to 34.7%, which is 6.0 percentage points more than compared to March of the previous year. It was determined that the credit demand of the population decreased significantly during the war, in particular, the attractiveness of long-term deposits decreased. Based on the oscillation coefficient, it was determined that in the first half of 2022, the official exchange rate hryvnia to dollar was more stable than the hryvnia to euro exchange rate.The financial losses of Ukraine as a result of the war were characterized. Thus, losses of physical capital from the destruction of enterprises, housing and infrastructure reached 100 billion dollars of the USA, which is equivalent to 50% of GDP in 2021. It is noted that currently the main source of covering the budget deficit is international aid.
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