Simulative model for evaluation of investment processes in the regions of Ukraine

Abstract

To analyze and evaluate the investment processes in the regions of Ukraine, it is suggested to use a simulative model that, unlike existing ones, allows to take into account the influence of macroeconomic factors and to predict the future development of the economic system of the regions taking into account their investment potential. The examination of the assessed simulative models of the investment processes in the regions of Ukraine for adequacy is carried out using the determination coefficient and Fisher's criterion, by which the influence of the most significant economic variables of social and economic development of the regions on the investments formation is determined. Research of the investments impact on the dynamics of economic systems indicators of the regions has shown that 86% of the constructed models are adequate. The presence of statistically significant estimates of model parameters confirms the effectiveness of the proposed approach for conducting research on the analysis and forecasting of the patterns of significant indicators formation of investment activity at the regional level, as well as their impact on indicators of social and economic development.

Full text

“Simulative model for evaluation of investment processes in the regions of
Ukraine”
AUTH ORS
Ivan Blahun
Lesia Dmytryshyn
Halyna Leshuk
ARTICLE INFO
Ivan Blahun, Lesia Dmytryshyn and Halyna Leshuk (2017). Simulative model for
evaluation of investment processes in the regions of Ukraine. Investment
Management and Financial Innovations, 14(3), 322-329. doi:10.21511/imfi.14(3-
2).2017.03
DOI http://dx.doi.org/10.21511/imfi.14(3-2).2017.03
RELEASED ON Thursday, 23 November 2017
RECE IVED ON Tuesday, 20 June 2017
ACCEPTED ON Monday, 02 October 2017
LICENSE
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0
International License
JOURNAL "Investment Management and Financial Innovations"
ISSN PRINT 1810-4967
ISSN ONLINE 1812-9358
PUBLISHER LLC “Consulting Publishing Company “Business Perspectives”
FOUNDER LLC “Consulting Publishing Company “Business Perspectives”
NUMBER OF REFERENCES
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0
© The author(s) 2018. This publication is an open access article.
businessperspectives.org

322
Investment Management and Financial Innovations, Volume 14, Issue 3, 2017
Abstract
To analyze and evaluate the investment processes in the regions of Ukraine, it is sug-
gested to use a simulative model that, unlike existing ones, allows to take into account
the inuence of macroeconomic factors and to predict the future development of the
economic system of the regions taking into account their investment potential. e ex-
amination of the assessed simulative models of the investment processes in the regions
of Ukraine for adequacy is carried out using the determination coecient and Fisher’s
criterion, by which the inuence of the most signicant economic variables of social
and economic development of the regions on the investments formation is determined.
Research of the investments impact on the dynamics of economic systems indicators of
the regions has shown that 86% of the constructed models are adequate. e presence
of statistically signicant estimates of model parameters conrms the eectiveness of
the proposed approach for conducting research on the analysis and forecasting of the
patterns of signicant indicators formation of investment activity at the regional level,
as well as their impact on indicators of social and economic development.
Ivan Blahun (Ukraine), Lesia Dmytryshyn (Ukraine), Halyna Leshuk (Ukraine)
BUSINESS PERSPECTIVES
LLC “P “Business Perspectives”
Hryhorii Skovoroda lane, 10, Sumy,
40022, Ukraine
www.businessperspectives.org
SIMULATIVE MODEL FOR
EVALUATION OF INVESTMENT
PROCESSES IN THE REGIONS
OF UKRAINE
Received on: 20 of June, 2017
Accepted on: 2 of October, 2017
INTRODUCTION
e current trends in the social and economic and investment situ-
ation of the regional systems require to justify a model of regional
investment processes in order to identify possible expectations and
eects, as well as to form an eective policy for economic and invest-
ment development. Complex research of the investment processes and
general economic trends in the context of the regional economy is ac-
tualized in the process of raising the social and economic status of the
regions, since certain territorial units are characterized by certain pe-
culiarities (geographic, economic, demographic, social, etc.) and the
implementation of the relevant programs and strategies for regional
development.
1. ANALYSIS OF RECENT RESEARCH
AND PUBLICATIONS
Investment analysis is crucial for the success of any investment, as this
process is facilitated both by the analysis of discounted cash ows and
by the real analysis of options. Many researchers point out the restric-
tions for the rst of them in cases of volatility in the business envi-
ronment. Kinias, Tsakalos, and Konstantopoulos (2017) tried to nd
the optimal investment strategy in the liberalized world of electricity
© Ivan Blahun, Lesia Dmytryshyn,
Halyna Leshuk, 2017
Ivan Blahun, Doctor of Economics,
Professor, Vasyl Stefanyk
Precarpathian National University,
Ukraine.
Lesia Dmytryshyn, Doctor of
Economics, Professor, Vasyl Stefanyk
Precarpathian National University,
Ukraine.
Halyna Leshuk, Ph.D. (Economics),
Hrusevsky Transcarpathian
Institute of Interregional Academy
of Personnel Management,
Ukraine.
is is an Open Access article,
distributed under the terms of the
Creative Commons Attribution-Non-
Commercial 4.0 International license,
which permits re-use, distribution,
and reproduction, provided the
materials aren’t used for commercial
purposes and the original work is
properly cited.
investment, region, estimation, simulative modeling,
model
Keywords
JEL Classification R10, R15, R58, E17

323
Investment Management and Financial Innovations, Volume 14, Issue 3, 2017
market, where electricity prices uctuate, while
other factors dier in each country. e authors
consider time factors for investments and the lev-
el of prices for electricity, using the theory of real
variants.
Researching the problems of evaluation of di-
rect foreign investment, net exports and eco-
nomic growth by the example of the Republic
of Kazakhstan, Azatbek and Ramazanov (2016),
based on the method of calculating the expendi-
ture of gross domestic product (GDP) and using
the method of regression analysis, determined the
impact of foreign direct investment (FDI) and net
exports on GDP and the interaction of FDI and
net exports as components of GDP.
Ābeltia, Zvirgzdia, and Ozols (2016) emphasize
the importance of distinguishing the endogenous
factors in the regional development of Latvia, in
particular the most important factors inuencing
on regional development, the authors consider the
decision of the government and the existing in-
frastructure, as well as the inuence of migration,
distance from the center and availability of invest-
ments. ese ndings regarding regional develop-
ment are applied, in particular, to a small country
where the geographically and historically single,
highly developed economic and political centers
has developed.
In the research of the current state of investment
in xed assets and the justication of the ways of
state regulation in attracting investment into the
regions of Ukraine, Stepanenko (2013) states that
the assessment and forecasting of the investment
attractiveness of the regions of Ukraine should
be directly related to the state regional policy, the
purpose of which is to ensure the development of
individual regions, taking into account such fac-
tors as the rational use of the various economic
opportunities of each of Ukraine’s regions.
Johnston Robert A. and Caroline J. Rodier (1998),
investigating the various problems associated with
regional modeling of ITC, have identied the eco-
nomic welfare model by adapting it to previous
projects.
Research of the problems of regional investment
policy modeling is partly highlighted in the works
of Kononsky (2014) who indicates that it is the
adequate denition of the methods of modeling
regional investment policy that enables to devel-
op an objective and eective model as a modern
state of the regional investment policy, as well
as a planned, transitional one. At the same time,
the author suggests the use of all modern mod-
eling methods, since this will allow us to obtain
the most informative understanding of the cur-
rent state and prospects of regional investment
policy development. In our opinion, this approach
is suciently complex, but dicult to implement,
since a certain economic space of the region is
characterized by appropriate social and economic
trends, which necessitates the search for adequate
modeling methods.
Gudz (2013), in the research of theoretical aspects
of the economic and mathematical modeling of
the investment potential of a region, oers to de-
termine the investment potential of a region with
the help of economic and mathematical modeling,
the construction of economic and mathematical
model to implement it using progressive math-
ematical and statistical methods, most suitable for
the analysis, evaluation and forecasting of invest-
ment the potential of the regions.
e estimation of the econometric model of sym-
metric equations for formalizing the interconnec-
tions between the main indicators of the monetary
and scal sectors was studied by Dadashova (2014),
while the author determines econometric model
in the form of a system of symmetric equations
as a prerequisite for opportunity of its estimation.
Investigating the impact of macroeconomic indi-
cators of partner countries on the aggregated be-
havior of their households (on the example of the
Russian Federation and Ukraine), Zdrok (2013)
proposed a simulative model that can study and
predict the behavior of households and methods
of inuencing it at the international level.
During creating a complex econometric model of
the impact of tax policy on the country’s economic
development, Salo (2015) assessed the structure of
the relationship between the main macroeconom-
ic indicators and indicators of the Consolidated
Budget of Ukraine, their future proposed develop-
ment in dierent scenarios.

324
Investment Management and Financial Innovations, Volume 14, Issue 3, 2017
Irshak (2013) highlighted the main stages in the
formation of a simulative model of the banking
system of Ukraine: formation of the logical frame-
work of a model (determination of interrelations
between the variable of a model) and the model
specication, estimation of unknown parameters
of the simulative model with the help of developed
estimation methods, in particular, the two-step
method of smallest squares, as well as verication
of the adequacy and accuracy of the constructed
model, development of scenarios for the develop-
ment of the banking sector on the basis of the con-
structed model taking into account the dynamics
of the environment.
Emphasizing the general aspects of simulative
modeling, Lychkina (2009) notes that method-
ologically the creation of new concepts for the for-
malization and structuring of models that are ori-
ented on mathematical and information systems,
support for the entire simulation cycle: from task
set up and creation, conceptual model before the
analysis of the results of the calculated experiment
and decision-making, mathematical use of statis-
tical methods, mathematical methods, optimiza-
tion and decision making. Simulation modeling,
which enables to form a generalized model of the
system based on separate data and to study the dy-
namics of the development of social systems, is the
main method of system forming in regional tasks
of social and economic development.
Tikoudis, Sundberg, Karlström (2012) in their re-
search developed an issue concerning simulative
modeling on the example of modeling of two-
spatial OLG of transport infrastructure, with the
increase of state investments and the reduction of
freight costs for two regions, which, respectively,
is noted on lower regional price indices.
us, in the study of conceptual foundations of
the basis for simulation modeling of macro- and
microeconomic processes, evaluation of the inter-
connections between macro or microeconomic in-
dicators, the authors primarily focused on nation-
al tendencies and policies (tax, scal, monetary),
while scientic research is rather fragmentary in
coverage of instrumental support at the regional
level, which remains undeveloped and character-
ized by certain features and trends in the invest-
ment providing dimension.
2. THE AIM
OF THE RESEARCH
e aim of the research is to evaluate the invest-
ment processes and regularities of the regions of
Ukraine with the use of simulator model tools,
which, accordingly, makes it possible to study the
inuence of factors of the social and economic de-
velopment of the regions on the formation of in-
vestments and the investments impact on the dy-
namics of indicators of economic systems of the
studied regions (industrial production, number of
employees, exports, etc.).
3. MAIN RESULTS
OF THE RESEARCH
A complex algorithm for assessing the investment
attractiveness of the regions of Ukraine is based on
the following consecutive steps that are outlined in
the study of the Institute of Regional Studies of the
National Academy of Sciences of Ukraine: 1) the
determination of the actual level (or normalized
values) of each of the output indicators for which
evaluation of the investment attractiveness of a re-
gion is carried out; 2) forecasting the level of each
of the standardized indexes for the next year under
consideration, taking into account the dynamics of
the actual values of these indices for the selected
period; 3) construction of the regions ratings of the
studied totality by the level of their investment at-
tractiveness for each of the averaged actual and for
each of the predicted values of normalized indi-
ces; 4) construction of regions rating for each of
the selected 7 criteria of investment attractiveness
(or group ratings); 5) construction of general (or
integral) regions ratings based on the calculation
of integral indicators on the base of the total val-
ues of the group (actual and forecast) indicators
of investment attrac tiveness, determined at the
previous (4th) stage of evaluation; 6) construction
of a complex (or summary) region ranking based
on complex indicators determined by combining
the c o rresponding i ntegral ind i cators of invest-
ment attractiveness; 7) the distribution of regions
to th e relevant gro u ps by the l evel (actual and
predictable) of the indicators of their investment
attractiveness. In our opinion, the assessment of
inves t ment processe s in the re g ions should be
based not only on t h e analysis of investment at-

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Investment Management and Financial Innovations, Volume 14, Issue 3, 2017
tract iveness, but al so in the consideration of in-
terdependencies with social and economic trends,
in particular, the dynamics of the gross regional
produc t, volumes of sold industrial products by
main types of activity, export value, etc.
In order to analyze the investment processes in
the regions of Ukraine (selected Western regions:
Lviv, Ternopil, Tran scarpathian, Chernivtsi and
Ivano-Frankivsk reg i ons), as well as to assess of
the na ture of the im pact of in vestment activity
indic a tors on the pa rameters of social and eco-
nomic development of regions based on the statis-
tical information (formed on the basis of the State
Statistics Committee of Ukrain e), we used sam-
ples from the following indicators: t
KINV
– the
value of capital investments, billion UAH; t
FINV
– the amount of dir e ct foreign investment, mil-
lion USD;
t
GDP
– gross regional p r oduct at ac-
tual prices, billion UAH;
t
PROM
– volumes of
sold industrial products (works, services) by main
types of activity, billion UAH;
t
EMP
– the num-
ber o f the employed population of working age,
thousand of persons;
t
EX
– export value, million
USD;
t
BUD
– volume of executed construction
works, billion UAH;
t
TOV
– retail turnover, bil-
lion UAH;
t
REV
– the amount of income of the
population, billion UAH.
It should be noted t hat there are signicant ter-
ritor i al imbalances in the social and economic
development of these regions and investment pro-
cesses in particular: the growth of t
KINV
in the
Lviv region amounts to 9.646 billion UAH in 2010
to 18. 605 billion UA H in 2016; Ivano-Frankivsk
region – 4.378 bill ion UAH in 2010 to 7.947 bil-
lion UAH in 2016; Te rnopil region – 2.138 bil-
lion UAH in 2010 to 4.888 billion UAH in 2016;
Transcarpathian reg ion – 2.205 billion UAH in
2010 to 4.63 billio n UAH in 2016; Chernivtsi re-
gion – 1.714 billion UAH in 2010 to 2.668 billion
UAH in 2016.
At the same time, the gross regional product at ac-
tual prices
( )
t
GDP
was as follows: in the Lviv re-
gion, 41.655 billion UAH in 2010 up to 94.690 bil-
lion UAH in 2015; Ivano-Frankivsk region – 20.446
billion UAH in 2010 to 45.854 billion UAH in 2015;
Ternopil region – 12.276 billion UAH in 2010 to
26.65 6 billion UAH in 2015; Transcarpathian re-
gion – 15.299 billion UAH in 2010 to 28.952 bil-
lion UAH in 2015; Chernivtsi region – 9.892 billion
UAH in 2010 to 18.506 billion UAH in 2015.
Paying attention to the complicated nature of the
relat i onship between the main indicators of in-
vestment activity and the indicators of social and
econom ic development of the regions studied for
analysis in the framework of our research, we have
chose n the toolkit for simulative modeling. e
simul a tive investment models in the studied re-
gions allow to investigate the inuence of factors of
the social and economic development of the region
on the formation of investments and the impact of
invest ments on the dynamics of indicators of eco-
nomic systems of the studied regions (industrial
production, number of employees, exports, etc.).
e si mulative models of the investment sphere
cont a in 2 blocks: investment formation and in-
vestment use. e rst block of models makes it
possible to investigate the factors that inuence on
the formation of capital and direct foreign invest-
ment in the regions, and contains two equations.
e second block of simulative models in the in-
vest ment sphere consists of ve equations and al-
lows to investigate the inuence of the amount of
capital and direct foreign investments in the stud-
ied regions on the dynamics of the main macro-
economic indicators (gross regional product, pro-
duction of industrial products, etc.).
By t he results of the system analysis of the pro-
cesses of formation of investment development in-
dicators, and the impact of these indicators on the
para meters of social and economic development
of the regions, we have identied a list of endog-
enous and exogenous variables. To solve the prob-
lem of forming a set of priority indicators that de-
termine the regularities of changing the values of
bot h investment activity parameters and indica-
tors of social and economic development, matrices
from the coecients of pair correlation between
the studied indicators were formed for each region.
e ranking of the values of the pair correlation
coecients for the analyzed indicators for each re-
gion, as well as the appl ication of the method of
step-by-step regression analysis to select the fac-
tors that are the priority in terms of forming the
investment component of social and economic de-
velopment and its regulation, allowed to form the
following models:

326
Investment Management and Financial Innovations, Volume 14, Issue 3, 2017
Ivano-Frankivsk region
( )
, ,
t t tt
KINV f FINV TOV
ε
=
( )
,
t tt
FINV f KINV
ε
=
( )
,
t tt
GDP f TOV
ε
=
( )
,
t tt
PROM f GDP
ε
=
( )
, ,
t t tt
EMP f GDP BUD
ε
=
( )
, ,
t t tt
EX f FINV KINV
ε
=
( )
, ,
t t tt
TOV f GDP REV
ε
=
Lviv region
( )
, ,
t t tt
KINV f REV TOV
ε
=
( )
, ,
t t tt
FINV f GDP REV
ε
=
( )
, ,
t t tt
GDP f TOV PROM
ε
=
( )
,
t tt
PROM f GDP
ε
=
( )
,
t tt
EMP f REV
ε
=
( )
,
t tt
EX f REV
ε
=
( )
, ,
t t tt
TOV f REV GDP
ε
=
Transcarpathian region
( )
, ,
t t tt
KINV f FINV REV
ε
=
( )
, ,
t t tt
FINV f KINV EMP
ε
=
( )
,
t tt
GDP f TOV
ε
=
( )
,
t tt
PROM f REV
ε
=
( )
,
t tt
EMP f FINV
ε
=
( )
,
t tt
EX f FINV
ε
=
( )
,
t tt
TOV f GDP
ε
=
Ternopil region
( )
, ,
t t tt
KINV f REV FINV
ε
=
( )
,
t tt
FINV f KINV
ε
=
( )
, ,
t t tt
GDP f TOV PROM
ε
=
( )
, , ,
t t t tt
PROM f GDP TOV REV
ε
=
( )
,
t tt
EMP f KINV
ε
=
( )
,
t tt
EX f REV
ε
=
( )
, ,
t t tt
TOV f GDP PROM
ε
=
Chernivtsi region
( )
,
t tt
KINV f REV
ε
=
( )
, ,
t t tt
FINV f EMP PROM
ε
=
( )
, ,
t t tt
GDP f TOV REV
ε
=
( )
,
t tt
PROM f REV
ε
=
( )
, ,
t t tt
EMP f BUD FINV
ε
=
( )
,
t tt
EX f EMP
ε
=
( )
,
t tt
TOV f GDP
ε
=
Let us fulfill the following conventions:
1
y
–
the amount of capital investments, billion UAH;
2
y
– amount of direct foreign investment, mil-
lion USD;
3
y
– gross regional product in actual
prices, billion UAH;
4
y
– volumes of sold indus-
trial products (works, services) by main types of
activity, billion UAH;
5
y
– the number of the
employed population of working age, thousand
people;
6
y
– export value, million USD;
7
y
–
turnover of retail trade, billion UAH; 1
x
– the
amount of population income, billion UAH;
2
x
– volume of executed construction works, billion
UAH.
e estimation of parameters of simulative mod-
els is carried out by means of a number of meth-
ods, in particular methods of two-stage and three-

327
Investment Management and Financial Innovations, Volume 14, Issue 3, 2017
stage least squares, the method of maximum like-
lihood with limited or complete information, the
method of instrumental variables etc. e verica-
tion of the generated models in terms of rank and
order has showed that the equations of models
for all regions are identied or converted, which
makes it possible to use a two-stage least squares
for nding of their estimation (Two-Stage Least
Squares, TSNLS), which is implemented in the
SPSS (Statistical Package for Social Search) envi-
ronment. e idea of the two-stage least squares
method is to replace stochastic endogenous vari-
ables (which correlate with factor characteristics)
by instrumental (auxi l iary) variables, which are
mostly calculated as a linear combination of non-
stochastic exogenous variables.
As a result of the two-stage least squares method
application, the following evaluated models were
obtained:
for the Ivano-Frankivsk region:
2
1 25
10.108 0.211 0.022 , 0.802y y yR=+− =
2
21
268.458 71.458 , 0.91y yR=+=
2
35
256.971 0.572 , 0.808y yR=−+ =
2
43
0.353 0.733 , 0.899y yR=+=
2
5 23
456.075 20.692 0.898 , 0.838y x yR
=++ =
2
6 12
1598.009 123.83 2.566 , 0.755y y yR
= +− =
2
7 13
0.854 0.192 2.514 , 0.988y x yR=−+ =
for the Lviv region:
2
1 17
0.359 0.255 0.174 , 0.617y x yR=−+ − =
2
2 13
152.051 3.166 4.628 , 0.801y x yR=+− =
2
3 47
6.099 0.761 0.745 , 0.986y y yR=++ =
2
43
1.233 0.604 , 0.968y yR=−+ =
2
52
991.448 10.529 , 0.507y xR=+=
2
62
1152.755 26.112 , 0.647y xR=+=
2
7 23
5.368 3.738 0.547 , 0.987y x yR=−+ + =
for the Transcarpathian region:
2
1 12
2.609 6.146 0.006 , 0.798y x yR=+− =
2
2 15
606.577 27.859 2.138 , 0.598y y yR=−− + =
2
37
4.338 0.198 , 0.965y yR=+=
2
41
3.117 31.413 , 0.596y xR=−+ =
2
52
458.276 0.103 , 0.339y yR=+=
2
62
741.621 1.456 , 0.509y yR=+=
2
73
18.008 4.845 , 0.965y yR=−+ =
for the Ternopil region:
2
1 12
3.285 0.103 0.048 , 0.952y x yR=+− =
2
21
80.314 6.062 , 0.779y yR=−=
2
3 47
2.059 0.441 0.463 , 0.992y y yR=−− + =
2
4 13
5.955 0.426 0.201 , 0.957y x yR=−+ + =
2
51
382.266 3.555 , 0.434y yR=+=
2
61
99.633 6.333 , 0.742y xR=+=
2
7 34
5.92 1.939 1.254 , 0.996y y yR=++ =
for the Chernivtsi region:
2
11
0.586 0.069 , 0.672y xR=+=
2
2 45
269.327 2.691 1.067 , 0.756y y yR=−−+ =
2
3 11
0.358 0.583 0.338 , 0.954y x yR=++ =
2
41
3.46 0.376 , 0.926y xR=−+ =
2
5 22
276.868 27.67 0.389 , 0.792y x yR=++ =
2
65
172.155 0.899 , 0.683y yR=−+ =
2
73
5.361 4.483 , 0.971y yR=−+ =

328
Investment Management and Financial Innovations, Volume 14, Issue 3, 2017
Verication of the estimated models for adequacy by
means of the determination coecient and Fischer’s
criterion has shown that 86% of the constructed
models are adequate. e presence of statistically
signicant estimates of model parameters (as shown
by the analysis, such as from 55 to 78% of assess-
ments depending on the region) conrms the eec-
tiveness of the proposed approach for conducting a
research on the analysis and forecasting of the pat-
terns of formation of key indicators of investment
activity at the regional level, as well as their impact
on indicators of social and economic development.
CONCLUSION
e analysis of investment processes in the regions of Ukraine by means of simulative modeling in the
study between the magnitude of capital investments and social and economic indicators has made it
possible to determine the priority directions of investment policy of a region and the development of
their social and economic environment. e presented research of the current state of investment pro-
cesses in the regions by means of simulative modeling allowed to determine the structure of intercon-
nections between the magnitude of capital investments and social and economic indicators of a region,
which also made it possible to study the inuence of factors on the formation of investments and the
impact of investments on the indicators of regions development. It should be noted that there is a sig-
nicant disproportionate and uneven territorial distribution of the magnitude of capital investment and
direct foreign investment, which has a corresponding eect on the formation of the gross regional prod-
uct, volumes of sold industrial products (works, services) by major types of activity, export value, retail
turnover, volume of executed construction works, and also forms the basis of regional employment of
the population and the size of their income.
us, the formation of the investment attractiveness of the Western regions of Ukraine and the increase
of investment activity, which in aggregate contributes to the improvement of the indicators of the social
and economic situation of the regions, rst of all should be based on the use of signicant potential of
recreational resources, advanced level of international scientic cooperation, system of diversication of
services, active use of innovative technologies in various sectors of the economic space of the regions, etc.
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