modeling of the investment and construction ......construction trend in russia inna nikolaevna...
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International Journal of Civil Engineering and Technology (IJCIET)
Volume 8, Issue 10, October 2017, pp. 1432–1447, Article ID: IJCIET_08_10_145
Available online at http://http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=8&IType=10
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication Scopus Indexed
MODELING OF THE INVESTMENT AND
CONSTRUCTION TREND IN RUSSIA
Inna Nikolaevna Geraskina
St. Petersburg State University of Architecture and Civil Engineering,
Russia, 190005, Petersburg, 2-nd Krasnoarmeiskaya St., 4
Andrey Vladimirovich Zatonskiy
Perm National Research Polytechnic University, Russian Federation, 614990,
Perm, 29 Komsomolsky prospekt
Alexander Alekseevich Petrov
St. Petersburg State University of Architecture and Civil Engineering,
Russia, 190005, Petersburg, 2-nd Krasnoarmeiskaya St., 4
ABSTRACT
The research results are especially relevant under conditions of the current systemic
cyclical crisis caused by the change long waves of economic development and
technological structures. Such processes have the ability to generate a new order, not
forced by the exogenous force but having a spontaneous character as a result of the
endogenous factors transformation. The fluctuations occurring in the socio-economic
environment, instead of fading, are amplifying, and the socio-economic system develops
in the direction of arbitrary self-organization. Taking into account the fact that
complicated self-organized systems cannot be imposed with the way for their
development, new approaches are required to trend forecasting and system management
taking into account natural patterns and properties revealed in the process of economic
and mathematical modeling. This allows shifting the bifurcation diagram at a certain
period of time, bypassing the system critical point which leads to an undesirable
outcome. Timely and qualitative forecasting of crisis points, administrative effects
modeling with the purpose of transition to a new favorable way of the economic system
development is reasonable and justifiable. Since there are compelling reasons to regard
the investment and construction sphere of Russia as a field of the synergistic patterns
action and its evolution cyclical nature revealed, the trend can be set with a certain
degree of accuracy by the differential equation systems, which allows identifying existing
alternatives of system behavior and obtaining more complete information about the
future. The article presents a new approach to the modeling and evolution forecasting of
complex cyclic and stochastic economic systems. Based on the qualitative and
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quantitative analysis of the Russian investment and construction industry statistical data
the main order parameters and system control variables are identified. A model is
developed, which is based on a second-order differential equation that makes it possible
to use statistical data and forecast in the long term the system behavior depending on
managerial effects. The dynamics is identified of the control variables impact on system
order parameter within different time periods.
Key words: investments, construction, modeling, prediction, development.
Cite this Article: Inna Nikolaevna Geraskina, Andrey Vladimirovich Zatonskiy and
Alexander Alekseevich Petrov, Modeling of the Investment and Construction Trend in
Russia, International Journal of Civil Engineering and Technology, 8(10), 2017, pp.
1432–1447.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=10
1. INTRODUCTION
Under the conditions when the Russian economic system, with a shortage of financial resources,
is experiencing an acute need for innovative and structural modernization of a number of
important economic spheres, including investment and construction, it is necessary to develop
complex programs and projects that promote system self-organization at the minimal costs for
modeling and future forecasting. For that end it is important:
– to posses a reliable and complete information regarding trends status of the economic system;
– to define the system regularities, features, indicators, which characterize to a greater extent
(phase parameters) the scope of the economic activity and variables having a significant impact
on them (control parameters).
Investment and construction activities are one of the priority areas of the national economy
and they make a significant contribution (about 5.5%) to the national macroeconomic indicators
growth. Despite this, the scientists examine it without a detailed understanding of the
development immanent properties and patterns as a complex self-regulating system. The
strategic task for the nearest future is to search for objective ways to manage this subsystem
focusing towards achieving the self-organization and sustainable development.
In terms of system approach, the investment and construction field (ICF) of Russia is a
complex and multifaceted subject of scientific research, one of the most important self-regulating
subsystems of the national economy representing an organized set of structural elements with
nonlinear connections that have independence in choosing the optimal operation mode, focus
towards the cost-effective activities and satisfaction of public needs for construction sites, and
having the synergistic systems properties. The latter include: the plurality of dissimilar entities
and relationships between them (transactional links, ownership schemes); the presence of non-
linear relationships that lead to the emergence of relatively stable structures; the integration and
coherent processes; high adaptive ability for turbulent environmental conditions and interference;
the systemic relations are of the organized nature at most; the entities integration is carried out on
the basis of direct, reverse and reserve links; the cyclic path dependency, etc. [1].
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2. METHOD
The traditions of modern factorial and regression analysis are such that, as a rule, the economic-
mathematical models are placed on algebraic polynomials when building the linear multifactor
models (LMM). In case of dynamic models developing the formula 1 is used:
0 i ii
y t y x t a a x t , and more often
0n i i ni
y t a a x t , (1)
where t – arbitrary point of time,
tn – time of the next values counting in the series of factors.
Such models are interpreted in a simplified manner, approximately as follows: investing in an
enterprise according to the diagram x1(t), allows obtaining a pure discounted income (or another
economic performance indicator) ) )) taking into account the market requirements to
the products (disturbance) z1(t). At that, it is assumed a priori that there is only a linear
relationship between the factors and system response value, and the only dynamic element in the
model is the time lag Δt. Similar approaches are used in doctoral dissertations [2; 3; 4], where it
is assumed by default that there are only direct links between factors and reaction force, and the
only dynamic element is a delay. For example, in models of the type (formula 2):
( ⃗ )) ∑ ) (2)
This assumption is unjustified. According to the general philosophical reasoning it follows
that the application of force always leads to a change in certain process acceleration, Newton
second law at least. In this regard, it would be logical to put the differential equation of the type
(formula 3) as the basis of the model [5]:
2
2
1d x tF t
mdt
, (3)
The "forces" here should be the values of factors (control variables).
For complex systems, such as ICF, the regression identification of coherence between y(t)
and xi(t) without convincing evidence of their mutual independence leads to the pointlessness of
modeling and forecasting. The same results bring up an attempt to extrapolate values y(t)
according to the time series data, including with the use of autoregressive models.
The research results [6; 7; 8; 9; 10] develop the ideas of the economic systems modeling
using the regression-differential equation. The latter can reconstruct the oscillation and cyclic
transient processes without additional mathematical efforts (identification and exclusion of the
periodic process, trend, etc.), the possibility of the response value output to the asymptote, which
is typical for many objects, including economic systems. This can not be taken into account in
LMM.
In order to determine the outlines of ICF economic-mathematical model, it is necessary to
perform the regression analysis, as well as to determine the form and order of the differential
equation. It is important to get a fairly simple tool for ICF trend forecasting, where there is no
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any problem with data identification, but lacking LMM disadvantages. The use of the 1st order
ordinary differential equation (ODE) in the model leads to a high prediction error and a piecewise
broken trend in the partial approximation of factors integration, derivative values jumps, which is
infeasible based on ICF properties.
Regression-differential modeling (RDM) provides a reliable coincidence of the first and
second derivatives of reaction values series. This means the following: if the original series
increases with slowdown, then the segment obtained by post prognosis will also grow with a
slowdown, although it may "overrun" and "lag" behind the original series. Forecasting based on
RDM is not yet free of shortcomings and ambiguities. For example, one has to use the trial-and-
error method, gradually including and excluding factors, and observing the change in the total
root-mean-square deviation of the model throughout all the initial data series, or within the
horizon of post-forecast.
Thus, the construction of RDM of n-order has the following form (formula 4):
1
01 1
2
1 1 1
n in m
i i i in ii i
m m m
ij i i j j i i ii j i
d y t d y tg a b y t c x t
dt dt
d x t x t f x t
, (4)
where ig – the influence coefficients of reaction lower derivatives, a – the constant
describing effect of one n-derivative of the reaction in constructing a trend, b – the feedback
factor describing the reaction value impact on its own n-derivative, ic – factors influence
coefficient, :ijd i j – factors mutual influence coefficient, i iif d – factors square influence
coefficient, i – lag of i-factor, 0 – lag in the feed back, is produced by the sequential inclusion
in it of a series of factors and refusal to include the factor if it worsens the error of the model.
RDM is complemented by the n-1st initial condition:
'0
0dyy
dt
, 2
''02
0d yy
dt , … ,
1
1
01
0n
n
n
d yy
dt
.
The unknown variables in this case are all the initial conditions, and also: 0'y , a, b, ci, dij, fi,
0, i. Their search is performed by minimizing the criterion, that is, by solving the minimization
problem:
0 0
0 0
' , , , , , , , :
: ' , , , , , , , min
i ij i i
i ij i i
y a b c d f
S y a b c d f
In particular, if we do not take lags into account, we obtain for the second-order RDM
(formula 5):
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2
21
2
1 1 1
m
i ii
m m m
ij i j i ii j i
d y t dy tg a b y t c x t
dtdt
d x t x t f x t
(5)
Search for the unknown variables is performed by minimizing the criterion (formula 6)
2
исх1
K
k kk
S y t y t
(6)
computed value root-mean-square deviation y t from the reaction series statistical values
исх ky t , that is, solve the minimization problem (formula 7):
0 0 0 0' , , , , , , , : ' , , , , , , , mini ij i i i ij i iy a b c d f S y a b c d f (7)
To exclude the influence of dimensions, the series of factors are preliminarily normalized to
an interval [0, 1] by the formula 8:
min
max min
i k i kk
i ki k i k
kk
x t x t
x tx t x t
(8)
The reactions series is normalized in a similar way. Technically, the minimization can be
performed in MatLAB or Mapl environment.
3. RESULTS
3.1. The Analysis of the Dynamics of the Main Statistical Indicators
The dynamics analysis of the basic statistical indicators [11] and graphical representation of their
time series in the form of phase curves, allowed considering that the only possible ICF operation
mode is cyclic dynamics with a trend towards cycle time reduction (table 1, figure 1, figure 2)
[12]. The system had three cycles within a period from 1990 to 2016, indicated in figure 1 with a
dotted line. The reasons for this trend are identified: exogenous factors that take into account the
fluctuation effects of higher-order economic systems, and the endogenous mechanism of
parameters cyclic behavior associated with variable and control parameters nonlinear interaction.
This confirms the presence in the phase space of an attractor with a significant attraction force.
Herewith, the system space dimensionality reduction occurs, and ICF development can be
described with a small number of statistical parameters, since they will represent the process
sufficiently complete with an enormous set of variables.
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Table 1 Scope of work performed in the investment and construction field in billion rubles and
commissioning of buildings, structures, individual production facilities, houses, social and cultural
facilities in Russian Federation, million square meters, 1990 - 2016
Year
Projects
commissioning,
mln m2
Scope of work,
RUB bn Year
Projects
commissioning, mln
m2
Scope of work,
RUB bn
Value Index Value Index Value Index Value Index
1990 61.7 - 0.1 - 2004 60.0 1.1 1313.6 1.26
1991 49.4 0.8 20.5 168.0 2005 66.3 1.1 1754.4 1.34
1992 41.5 0.8 50.2 2.5 2006 75.6 1.1 2350.8 1.34
1993 41.8 1.0 58.7 1.2 2007 98.1 1.3 3293.3 1.40
1994 39.2 0.9 80.5 1.4 2008 102.5 1.0 4528.1 1.40
1995 41.0 1.0 153.7 1.9 2009 95.1 0.9 3998.3 0.88
1996 34.3 0.8 225.8 1.5 2010 91.5 0.9 4454.1 1.11
1997 32.7 0.9 242.6 1.1 2011 99.0 1.1 5140.3 1.15
1998 40.8 1.2 240.9 1.0 2012 110.4 1.1 5714.1 1.11
1999 42.1 1.0 329.9 1.4 2013 117.8 1.1 6019.5 1.05
2000 44.7 1.0 558.5 0.8 2014 138.6 1.2 6125.2 1.02
2001 47.7 1.0 703.8 0.9 2015 139.4 1.0 5945.5 0.97
2002 49.6 1.0 831.0 1.2 2016 130.2 0.9 5749.4 0.97
2003 53.7 1.1 1042.7 1.3
Figure 1 Phase curve of the rate of volume of work executed by kind of economic activity "construction"
in 1998 – 2015
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Figure 2 The phase curve of the index amounts entered in the action of buildings,buildings, separate
production facilities, houses, objects socially-cultural appointment in 1990 – 2016
The obtained results are necessary for the economic and mathematical modeling of ICF
subsequent and effective management [13; 14; 15; 16; 5]. The model should be based on a
differential equation of the second and higher orders to account for the cyclic and oscillatory
processes. The management accounting for the synergetic features and economic and
mathematical modeling results allows shifting the bifurcation diagram at a certain period of time,
bypassing the critical point which leads to an adverse outcome [17; 18].
3.2. Modeling
As ICF order parameter the authors selected a statistical quantitative indicator – "Commissioning
of buildings, structures, individual production facilities, houses, social and cultural facilities"
measured in thousands of square meters, which characterizes to the maximum extent the system
development dynamics. An analysis of the set of ICF statistical indicators made it possible to
distinguish the control variables (tab. 2).
Table 2 Control variables and their designation
Designation Control variable
Х1 Investment in equity, mln. RUB (Before 1998 – RUB bn).
Х2 Volume of real estate loan, RUB bn
Х3 Expenses per RUB 1, works, kop.
Х4 Population of the RSFSR/RF, people
Х5 Average monthly wages of workers in construction organizations, RUB, in thousands
Х6 Overall construction materials production index
Х7 Consolidated price index for the main types of construction materials and works
Х8 Average per capita incomes of the population, RUB per month (before 1998 RUB, in
thousand)
Х9 Provision of own resources of construction organizations
Х10 Share of expenses for the acquisition of real estate in the monetary expenditures of the
population
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Х11 Refinancing rate, %
Х12 Annual inflation in the Russian Federation,%
Х13 Volume of housing construction, thousand square meters.
Х14 Productivity index
Х15 Level of construction profitability, %
Х16 Average prices of 1 square meter of the total area at the primary housing market, RUB.
Х17 Availability of fixed assets, RUB bn
Х18 Number of operating construction organizations, pcs.
The entire modeling interval was divided into three segments corresponding to significant
changes in the social and economic system of the USSR and the Russian Federation: 1) 1990 –
1998; 2) 1999 – 2007; 3) 2008 – 2016 ICF RDM was built according to three time periods. RDM
diagram for the period of 1990 – 1998 (before denomination) is shown in Figure 3.
Benchmark data Post-prognosis for 6 years
Post-prognosis for 7
years Resulting model
Figure 3 RDM period 1990 – 1998
Since a smooth curve is the solution of differential equations of high orders which does not
have discontinuities of lower derivatives, the approximation of the order parameter was
performed by a cubic spline, and control variables between the annual counting were
approximated linearly. The forecast trends for this period were obtained through gradual increase
in the number of years. Factors with low-valued coefficients were discarded until their
elimination began to lead to a sharp increase in the error. The factors remaining in the model
have the coefficients presented in the final model (table 3).
Obviously, a decrease in ICF indicators for 1996 – 1997 is successfully predicted using
RDM, even according to the 1990 – 1994 data, but with a significant error. The addition of the
following years to the model leads to the post-forecast adjustment, and according to the 6-year
data it turns out to be satisfactory.
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Table 3 RDM factors weight
Factor Х1 Х2 Х3 Х4 Х5 Х6 Х7 Х9 Х10 Х11 Х12 Х15 Х16 Х17
Final factors weight for RDM 1990 – 1998.
Ci 16.
86 0
66.2
1
-
20.03 0 -3.6 6.88 2.95
-
57.9
16.1
8 5.44
-
12.33 0
-
48.79
Initial factors weight for RDM 1999 – 2008.
Ci
-
0.2
4
-
0.37 -0.12 0.07 1.37 0
-
0.08 0.04 0.11 -0.05
-
0.04 0.04
-
0.42 0.46
Weights of RDM factors in 1999 – 2008 after the first factors exclusion
Ci
-
0.3
5
-
0.38 -0.13 0 1.21 0
-
0.08 0 0.11 0 0 0
-
0.38 0.30
Final weights of RDM factors in 1999 – 2008 after exclusion of factor Х7
Ci 0.2
6
-
0.19 -0.12 0 1.54 0 0 0 0.11 0 0 0
-
0.40 0.37
Initial factors weight for RDM 2008 – 2016.
Ci
-
0.0
1
-
0.10 0.17 0.03
-
0.11
-
0.02 0.08
-
0.12 0.12 -0.1
-
0.05 -0.75 0.02 0.23
Weights of RDM factors in 2008 – 2016 after the first factors exclusion
Ci 0 -
0.11 0.19 0
-
0.15 0 0.08
-
0.09 0.12 -0.09
-
0.05 -0.85 0 0.26
Final weights of RDM factors in 1999 – 2008 after exclusion of factor Х12
Ci 0 -
0.07 0.25 0
-
0.18 0 0.05
-
0.07 0.11 -0.09 0 -0.83 0 0.30
Neither model is capable of forecasting beyond 1998, which is considered absolutely normal.
Models built according to 6 – 8 years show to varying degrees the growth in the commissioning
of construction projects, which can be referred to as a way out of the crisis of 1994-1996. Based
on the signs and absolute values of the weight coefficients for factors in RDM (table 3) we can
draw the following conclusions: the paramount importance for ICF development in recent years,
covered by the model, can be attributed to factor Х3 (expenses per RUB 1, works, kop.). Its
dynamics according to the years covered by RDM is shown in Figure 4.
Figure 4 Dynamics of factor Х3 RDM 1990 – 1998
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By the time of the financial crisis it became clear to a certain circle of ICF entities that it is
more profitable to increase construction costs and this process began to develop non-linearly.
"Share of expenses for the acquisition of real estate in the monetary expenditures of the
population" factor had the biggest deterrent effect. This is quite logical – the poorer the
population and the greater the proportion of all available funds belongs to the forced purchase of
real estate, the more difficult is to sell the constructed housing on the market.
In this case, it is difficult to estimate the presence of the current dynamics, that by 1998 the
population became more active in purchasing real estate than few years earlier. The lack of funds
ceased to be a deterrent for making a decision on investing in real estate. It is interesting that Х9
factor interpreted as "the force that affects ICF" acquired in around 2006 has a positive and
steadily growing importance. This indicates that ICF by this factor was restrained by 1998, and
its growth (more precisely, its acceleration) has already become positive. Construction
enterprises provided with their own fixed assets, began to accelerate the construction growth
rate. This is a good example illustrating the fundamental difference between the explanatory
properties of LMM and RDM.
Similarly, we have built RDM according to the data of 1999 – 2008 adding 2010 and 2011 to
control changes in the object of research. The post-forecasts obtained are given in Figure 5. The
model, constructed according to the data of 1999 – 2005 qualitatively forecasts the trend
inflection in 2008 – 2009, but not its consequences.
Benchmark data
Post-prognosis for 6 years
Post-prognosis for 7 years
Resulting model
Post-prognosis for 8 years
Figure 5 RDM and post-forecasts of 1999 – 2008
The study results allowed considering that the conditions for 2008 financial crisis had
developed a few years before it and provoked this negative synergetic effect. Post-forecast
fluctuations in the "plus" and "minus" compared to the initial data indicate the fluctuations in
ICF properties and structure during this period.
Table 3 shows that the factors Х4, Х6, Х9, Х11, Х12, Х15 have weight coefficients with an
absolute value of less than 5% of the maximum value according to coefficient module. We
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eliminate them from the model, identify the coefficients and growth of the approximation error.
It increased from 0.01248 to 0.02046, and the qualitative nature of the forecasted trends
remained the same: in 2008 the post-forecast quality reduces, but not as much as it was in 1998
model. The reduction in the approximation error is not significant, it shows that factors exclusion
from the model is justified. The weight of Х7 factor is sufficiently small, which indicates the
possibility of excluding it from the model. All the models confidently forecast ICF reaction drop-
down after 2008 and, of course, do not account for its change as a result of 2008 crisis.
Therefore, its post-forecast is unsatisfactory.
The maximum positive effect on ICF dynamics is associated with Х5 factor X5 (average
monthly wages of workers in construction organizations), which is quite easy to explain. The
following factor, which has a positive effect on ICF trend is Х17 factor (availability of fixed
assets), which changed the sign in comparison with the past period model. These weight
coefficients are stable and do not change significantly in models built up according to the data
before 2005, 2006, etc. The largest deterrent effect on ICF dynamics is caused by Х16 factor
(average prices of 1 square meter of the total area at the primary housing market), which also
does not need comments. Further, we built RDM corresponding to the period of 2008 – 2016.
(Figure 6).
Figure 6 RDM and post-forecasts of 2008-2016
The quality of RDM post-forecasting draws a special attention. They, according to the data of
2008 – 2011 and 2008 – 2012 are almost identical and do not coincide with reality, although
forecast a certain inflection of the trend around 2014 with subsequent sharp drop-down.
Probably, something has changed within these years essentially in the series of factors, which
resulted in delay in the drop-down and further peak shift in time, since the post-forecast for 6
years almost exactly coincides with the original data, and adding two more annual counts does
not change much. The model approximation error is 0.1012.
3.3. Identification of Factor Models
According to the absolute values of Х1, Х4, Х6, Х16 factors weight less than 5% of the maximum,
therefore we excluded them from RDM and found new coefficients. The approximation error has
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decreased two-fold and is 0.06886. The nature of post-forecasts has not changed, the "jump" in
the transition to 6 years has been preserved. It is reasonable to exclude the only Х12 factor from
RDM, since its weight is 5.5% of the maximum. We obtained the final RDM (Figure 7). The
forecast error has slightly increased and is 0.08651. Trends nature has not changed.
Figure 7 RDM post-forecast in 2008-2016 after Х12 factor exclusion
Let us analyze the dynamics of the factors weights with an increase in the number of years
for ICF post-forecast construction (table 4), by dividing them into three groups: 1) the factor
weight is changing smoothly with an increase in the number of years when constructing the post-
forecast; 2) the factor weight is changing step by step, taking into account the 6-year series when
constructing the post-forecast; 3) the factor weight is not changing in any number of the post-
forecast years.
Table 4 RDM factors weights for the period of 2008 – 2016 according to the corresponding post-forecast
years
Post-forecast, years Х2 Х3 Х5 Х7 Х9 Х10 Х11 Х15 Х17
4 (year 2012) -0.0797 0.2248 -0.1681 0.0821 -0.0904 0.1232 -0.0999 -0.955 0.3109
5 (year 2013) -0.0870 0.2248 -0.1691 0.0821 -0.0894 0.1232 -0.0999 -0.9545 0.3110
6 (year 2014) -0.0700 0.2452 -0.1803 0.0539 -0.0657 0.1089 -0.0900 -0.8363 0.2970
7 (year 2015) -0.0690 0.2452 -0.1803 0.0539 -0.0658 0.1089 -0.0901 -0.8327 0.2970
Entire model -0.11 0.2452 -0.1803 0.0539 -0.0659 0.1089 -0.0902 -0.8289 0.2970
Group 1 2 2 2 2 2 3 1 2
% Change - 9.1% 6.6% 34.3% 26.5% 11.6% - - 4.5%
The greatest change between 2013 and 2014 is attributed to ICF driving forces – Х7 and Х9
factors. Both the factors weights have decreased in absolute terms: Х7 – began to accelerate
construction to a less extent, Х9 – slows it down to a less extent. It should be noted that this
change occurred quickly and "stepwise". RDM factors values for the three periods are shown in
Table 5.
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Table 5 RDM factors values for all periods
Period Х1 Х2 Х3 Х4 Х5 Х6 Х7
1990 16.8609 0 66.2121 -20.0299 0 -3.5669 6.8762
1998 0.2597 -0.188 -0.1174 0 1.5403 0 0
2008 0 -0.068 0.2452 0 -0.1803 0 0.0539
Factors values in proportions of the maximum factor weight module
1990 25.5% 100.0% -30.3% -5.4% 10.4%
1998 16.9% -12.2% -7.6% 100.0%
2008 -8.2% 29.6% -21.8% 6.5%
Period Х9 Х10 Х11 Х12 Х15 Х16 Х17
1990 2.9513 -57.9116 16.1808 5.4366 -12.330 0 -48.785
1998 0 0.106 0 0 0 -0.3985 0.3753
2008 -0.0659 0.1089 -0.0902 0 -0.8289 0 0.297
Factors values in proportions of the maximum factor weight module
1990 4.5% -87.5% 24.4% 8.2% -18.6% -73.7%
1998 6.9% -25.9% 24.4%
2008 -8.0% 13.1% -10.9% -100.0% 35.8%
4. DISCUSSION
It is interesting to note the following patterns of changes in ICF RDM factors weights that turned
out to be significant in the models of all the periods:
1. Х10 factor impeded the construction growth during the Soviet period, and began to accelerate
somewhat during the post-crisis period;
2. The same is true for Х17. As soon as the Soviet economic model collapsed, this factor became
one of the main factors in ICF development [19; 20; 21].
3. Х1 factor accelerated the construction significantly during the Soviet period and before 2008
crisis, and then its significance decreased.
4. Х3 and Х5factors, although they do not correlate with each other in a linear term, have a clear
negative correlation as construction driving forces. They are probably interchangeable to some
extent in RDM (with different signs), but, unfortunately, no RDM well-developed analytical
theory exists.
5. During the Soviet era, perhaps because of the deep comprehensive ties of the planned
economy, large number of construction driving forces was significant. In a market economy, the
fewer driving forces are of importance, while the rest are negligibly small.
In order to forecast ICF development we should consider the change in the normalized values
of the factors in recent years (Figure 8).
Modeling of the Investment and Construction Trend in Russia
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Figure 8 The factors normalized values dynamics in 2012-2016
It can be seen from the diagrams that the factors values have no pronounced linear or
quadratic trends, so, their values forecasting for the next years is difficult. However, their series
values can not be independent, therefore, it is reasonable to assume that the series can be
approximated with some self-similarity model (a model in the state space) when the next state of
the external environment, that is, the value of the factor vector at the next time reference is not
random, but depends on the previous state (formula 9):
1k kx t a Bx t , (9)
where В – transition matrix, а – initial state vector, tk - k-th count of the series in time.
Having determined the parameters а and В by the least squares method, we will obtain a fairly
good reproduction of factors (sum of quadratic deviations for 2012 – 2016. S = 1.42*10-12
. Through
re-calculations in the following years we have obtained RDM factors forecast (table 6).
Table 6 ICF RDM factors forecast values 2017-2019
Factor 2012 2013 2014 2015 2016 2017 2018 2019
Х2 0.5398 0.7400 0.9917 0.6128 1.0000 0.8567 0.2371 0.7400
Х3 0.0000 1.0000 1.0000 0.8000 1.0000 0.9041 0.3621 1.0000
Х5 0.8656 1.0000 0.9892 0.6344 0.6398 0.5274 0.4134 1.0000
Х7 0.1143 0.0000 0.0857 0.2229 0.0857 0.1655 -0.0211 0.000
Х9 0.5649 0.6183 0.6565 1.0000 0.7328 0.8915 0.1849 0.6183
Х10 0.4787 0.4681 0.1596 0.9255 0.8830 1.0787 0.0906 0.4681
Х11 0.0952 0.0952 0.0952 0.0952 0.4286 0.3716 -0.0063 0.0952
Х15 0.1750 1.0000 0.3500 0.1250 0.0500 -0.1107 0.5063 1.0000
Х17 0.6554 0.7124 0.9164 0.9663 1.0000 1.0426 0.2046 0.7124
Y 0.3946 0.5491 0.9833 1.0000 0.8079 0.5237 0.4591 -0.0021
Inna Nikolaevna Geraskina, Andrey Vladimirovich Zatonskiy and Alexander Alekseevich Petrov
http://www.iaeme.com/IJCIET/index.asp 1446 [email protected]
5. CONCLUSIONS
Thus, the research showed that currently ICF dynamics can be described with a few statistical
parameters, since the system moves near the attractor and dimensionality of its space is reduced,
and the selected parameters subspaces in the phase space quite fully represent everything that
occurs in an enormous set of variables.
ICF development cyclical nature predetermines the inexpediency of using LMM to forecast
the dynamics and consequences of control impacts due to: system complexity, stochasticity,
inertness, consistency, presence of oscillation transient processes, nonlinear links between
factors and reaction dynamics, high values of the mean square errors.
Forecast ICF RDM factors values indicate that if the control variables dynamics will be
subject to the same patterns as before 2016, then ICF in 2017 will face a decrease in the resulting
indicators. This trend will not change practically in 2018, and in 2019 the moment will come of
ICF stability loss when it reaches the values of 2010 resulting indicators.
In order to maintain ICF stable trend and avoid an undesirable forecast zone, a complex of
management decisions is required, aimed at: immanent factors activation of the economic system
and combined effect of control variables that have a certain degree of impact. ICF trend
withdrawal from the negative forecasts zone by influencing the values dynamics of the control
variables will be identified as a type of synergistic effect "sustainable development".
REFERENCES
[1] Geraskina, I. N. The investment and construction complex of Russia is a synergistic system.
Economic analysis: theory and practice, 2, 2017, pp. 328-339.
[2] Mirolyubova, A.A. Modeling methodology the investment process of real sector of economy
of the region. Ivanovo: Ivanovo State University of Chemistry and Technology, 2012, 301 p.
[3] Dzyuba, S.A. Corporate reporting: in the data source. Management in Russia and Abroad
Journal, 2, 2014, pp. 66-75.
[4] Micek, E.B. (2011). Econometric modeling of investment in fixed capital of economy of
Russia. Ekaterinburg: NOU VPO “Gumanitarnyj universitet”, 2011, 394 p.
[5] Forrester ,J. W. World Dynamics (2 ed.). Portland, OR: Productivity Press, 1973, 144 p.
[6] Zatonskij, A.V. The advantages of differential models in ecological-economic modeling.
Bulletin of the Tomsk Polytechnic University, 5, 2012, pp. 134-139.
[7] Kovalevskij, D.V. Modeling of the system "the world economy – global climate" within the
framework of optimization and system-dynamic approaches. St. Petersburg polytechnic
university journal of engineering sciences and technology, 256, 2016, pp. 197-203.
[8] Yanchenko, T. V. and Zatonskij, A.V. Model of regional social resource based on the
regression-differential equation of second order. A new University. The scientific journal, 5-6,
2014, pp. 23-34.
[9] Belonogov, D. S. Prediction of the development of machine tool industry in the Russian
Federation on the basis of mathematical multi-factor models. The first step in science, 5-6
(17-19), 2016, pp. 12-17.
[10] Rakaeva, T.G. Regression-differential model for the dynamics of the mining industry of the
Perm region. A new University. Series: Technical Sciences, 7-8 (41-42), 2015, pp. 47-53.
Modeling of the Investment and Construction Trend in Russia
http://www.iaeme.com/IJCIET/index.asp 1447 [email protected]
[11] Constructing in Russia. Federal state statistics service. Rosstat. Moscow: Federal State
Statistics Service, 2016, 111 p.
[12] Telichenko, V.I. Status and problems of sustainable development of construction activities.
Proceedings of Moscow State University of Civil Engineering, 12, 2015, pp. 5-12.
[13] Malineckij, G.G. Mathematical foundations of synergetics: chaos, structures, computational
experiment. Moskow: Publishing house "LCI", 2007, 308 p.
[14] Akaev, A.A., Korotaev, A.V., Malineckij, G.G. and Malkov, S.Ju. Modeling and forecasting
the global, regional and national development. Moscow: Librokom, 2012, 488 p.
[15] Akaev, A.A., Sarygulov, A.I. and Sokolov, V.N. Structural dynamics of modern economic
systems. Saint Petersburg, SPb: Publishing house of Polytechnical Institute. University press,
2014, 170 p.
[16] Akaev, A.A., Rumjanceva, S.Ju., Sarygulov, A.I. and Sokolov, V.N. Structural cyclic
processes of economic dynamics. Saint Petersburg, SPb: Publishing house of Polytechnical
Institute. University press, 2016, 391 p.
[17] Petrov, A. A. and Geraskina I. N. Analysis of the functioning and development of
investment-construction complex of Russia. Proceedings of Moscow State University of Civil
Engineering, 12, 2016, 131-144.
[18] Malkov, S.Ju. Hierarchical modeling of world dynamics, in Akaev, A.A. (Ed.) Projects and
risks of the future. Concepts, models, instruments, forecasts. Moscow: Krasand, 2011, pp.
208-231.
[19] Klejner, G .B. The structural model of general equilibrium in the interior of the system of
economy, in Mathematical methods in contemporary economic research: collection of
articles. Moscow: Jekonomicheskij fakul'tet MGU, Prospekt Moskva, 2014, pp.65-88.
[20] Klejner, G. B. System a strategic resource of a sustainable economy. St.Petersburg State
Polytechnical University Journal. Economics, 4(223), 2015, pp. 10-24.
[21] Klejner, G.B. The stability of the Russian economy in the mirror of the system of economic
theory. Part II. Economic issues Journal, 1, 2016, pp. 117-138.
[22] Sashikala V. and Dr.P.Chitramani A Review on Emoti onal Intelligence and Investment
Behavior. International Journal of Management , 8 (3), 2017, pp. 32–41.
[23] Dr. T. Unnamalai. A Study on Awareness of Investors about the Mutual Fund Investments in
Musiri Taluk. International Journal of Management , 7 (2), 2016, pp 115 - 122 .
[24] A Study on Financial Performance of Amana Investment, Dr. S. Poongavanam, Dr.
Mohammed Ismail Sait, Dr. Srinivas an and Dr. Rengamani, International Journal of
Mechanical Engineering and Technology , 8(7), 2017, pp. 976– 984, 8(7),
[25] Amana Investment and Amana Bank – A Comparative Study between Pre-Public Issue and
Post-Public Issue, Dr. S. Poon gavanam, Dr. Mohammed Ismail Sait, Dr. Srinivasan and Dr.
Rengamani, International Journal of Mechanical Engineering and Technology , 8(7), 2017,
pp. 985–991, 8(7),