=Paper= {{Paper |id=Vol-2018/paper-14 |storemode=property |title=Econometric Modeling of Integration Activity in the Russian Economy |pdfUrl=https://ceur-ws.org/Vol-2018/paper-14.pdf |volume=Vol-2018 |authors=Vladimir Mkhitarian,Mariia Karelina,Natalia Reent }} ==Econometric Modeling of Integration Activity in the Russian Economy== https://ceur-ws.org/Vol-2018/paper-14.pdf

Econometric Modeling of Integration Activity
in the Russian Economy

Vladimir S. Mkhitarian1[0000−0002−3116−3342] ,
Mariia G. Karelina2[0000−0001−7477−3194]? , and Natalia A. Reent3
1
National Research University Higher School of Economics, Moscow, Russia,
http://www.hse.ru
2 3
Nosov Magnitogorsk State Technical University, Magnitogorsk, Russia,
http://www.magtu.ru,
2
marjyshka@mail.ru

Abstract. A dynamic process of forming complex structured economic
entities at the sectoral, interindustry, and interregional levels based on
the integration policy takes place in the modern economy. A structural
change happens in mergers and acquisitions transactions; they involve an
increasing number of participants; the scale of cross-border transactions
is expanding, the state becomes an active participant. At the same time,
the nature and complexity of integration processes require qualitative
tools for a comprehensive analysis of the integration activity of Russian
companies. The aim of the research presented was the development of an
econometric approach to the analysis of integration policy in the Rus-
sian economy. Multidimensional statistical and econometric methods for
analysing the dependencies, reducing the dimension and classification, as
well as the system of econometric equations were used as a research tool.
The econometric approach is based on a recursive system of simultaneous
equations describing the integration activity of Russian companies and
the impact of various macroeconomic indicators on it. The analysis of the
model obtained proves a unidirectional change in the volume of foreign
investments and the value of mergers and acquisitions transactions, as
well as the inverse relationship between the volatility of the stock market
and the value of integration transactions. The results of the research are
of practical importance, since they can be used in order to identify fa-
vorable conditions for doing business, improve the investment climate, as
well as to increase the validity of decision-making on the socioeconomic
development of Russian territories.

Keywords: integration activity, simultaneous equations systems, econo-
metric approach

1 Introduction
The role of integration policy in the system of economic relations of any state
has significantly increased in recent decades. The scale and level of economic in-
?
The work is performed under the grant of the President of the Russian Federation
for the state support of young Russian scientists — PhDs (МК–5339.2016.6).
tegration are largely macroeconomic indicators of the effective functioning of the
national economy and its institutions. Integration processes under modern Rus-
sian conditions restore the structural integrity of the national economy, equalise
the spatial characteristics of the country’s industrial potential, activate the inno-
vative business activity, and increase the competitiveness of domestic products
that is the key to the reindustrialisation of the domestic economy.
The processes of integration policy are the objects of close attention and nu-
merous studies. However, the issues of economic content and quantitative mea-
surement of the integration activity in the Russian economy remain insufficiently
developed. Moreover, it is impossible to reindustrialise the Russian economy suc-
cessfully, as well as to create an effective economic system with a fundamentally
new nature of corporate relationships without statistical consideration of the
factors that determine the integration activity of Russian companies. In this
regard, there is a need to model the processes of mergers and acquisitions of
Russian companies based on modern econometric approaches.

2 Econometric analysis of mergers and acquisitions
processes in the Russian economy

Integration processes in business aimed at its scale and market share increasing
become an important factor in raising competitiveness under the conditions of
Russia’s imposing economic sanctions and increasing international isolation. The
research of the mergers and acquisitions (M&A) processes and the integration
activity in various states with an advanced institutional environment is largely
based on econometric methods and models [7, 10, 16].
Thus, Choi S. H. and Jeon B. N. studied the dynamic impact of macroeco-
nomic factors on the integration activity in the USA economy for the period
1980–2004 in their work [8]. Firstly, the long-term equilibrium relationship be-
tween the frequency volume of the integration activity and the main macroe-
conomic variables including real GDP, monetary aggregates, interest rates, and
stock indices were analysed in the framework of this approach. Secondly, the
short-term dynamics between M&A activity and macroeconomic variables was
investigated based on the vector autoregressive model (VAR).
In the 1990’s, the importance of the macroeconomic environment influence
on the location of international production in the host country (M&A export
transactions) against the intensification of globalisation processes was widely
discussed in foreign scientific literature. Thus, Hughes H., Uddin M., and Not-
tingham M. in the article [5] investigated the economic impact of shocks for the
specific country economy on mergers and acquisitions transactions over the pe-
riod 1987–2008 on the example of Great Britain, which was the leader among
European countries in the international market for corporate control. The vec-
tor autoregressive model and the error correction model (VAR/VECM) served
as a tool for the study. The author drew a conclusion that the number of exter-
nal mergers and acquisitions transactions has a significant connection with the
country’s GDP, with the money supply and an effective exchange rate in the
long-term outlook.
A relatively short time series of domestic databases on M&A transactions,
which can give a very diffuse assessment of the Russian market of external merg-
ers and acquisitions, is one of the serious limitations for the use of this kind of
model in Russia. Insufficient development of tools of the Russian stock mar-
ket for transaction payment may be another restriction apart from the sample
size [2]. Unfortunately, despite the increased degree of information openness in
Russia, many Russian M&A transactions happen under non-transparent condi-
tions; business owners often prefer the confidentiality of the information about
the amount of transactions.
Bhattacharjee A. and Higson S. in their work [3] investigated the influence
of the macroeconomic environment on the probable business exit of enterprises
in a model where mergers and bankruptcies are jointly defined and are mutually
exclusive processes.
Thus, a large number of foreign authors devoted their works to the dynamics
of macroeconomic indicators and its impact on the intensity of mergers and ac-
quisitions processes. At the same time, this research direction is relevant also for
the Russian economy. Although many models of this direction have not yet been
realised under Russian conditions, nevertheless it has recently become possible
to analyse the relationship between Russian corporate integration processes and
a number of macro variables [13].
Ignatishin Yu. V. was the first who used econometric methods for the study
of the market for corporate control [9]. He calculated correlation and regression
relationships between the data on mergers and acquisitions and a number of
macroeconomic variables such as inflation indicators, oil prices, the exchange
rate for Ruble to Euro and to US dollar, the RTS index, etc.
Musatova M. M. proposed a regression model of relationship between macro
variables and the dynamics of M&A transactions over 2001–2004 [15]. The author
modelled a number of integration transactions using Poisson regression with
added seasonal components. The growth rate of real industrial production, the
balanced financial result of the main branches of the economy, the statistical
volatility of the RTS index and the proportion of loss-making organisations in
the total number of industrial organisations were taken as exogenous variables
in the work.
At the same time, an assessment of the close relationship between individ-
ual variables and the creation of regression equations is not enough in order to
describe the mechanism of the functioning of the market for mergers and acquisi-
tions. To characterise the true impact of individual characteristics on the change
in the system of the resulting indicators of the M&A market it is not reasonable
to consider the multiple regression equation taken separately. In this case, these
processes can be described with systems of interrelated (simultaneous) equations.
For a complex analysis of the market of mergers and acquisitions it seems
necessary to use 3 resulting variables with a causal and consecutive relationship
based on information collected monthly from January 2003 to December 2015
(156 observations): y1 — the cost of conflict assets; y2 — the number of integration
transactions; y3 — the cost of integration transactions.
Various factors that determine the specificity and efficiency of the use of
integration strategies for growth and development by business entities influence
the integration processes. The internal prerequisites for mergers and acquisitions
may include: the achievement of certain financial indicators that are necessary
for the further development of the company and its transition to a qualitatively
new level; the correction of a company’s financial position, when integration with
other market participants will allow to solve a number of unsolvable problems.
The ongoing macroeconomic processes can be attributed to the external fac-
tor that influences the development of mergers and acquisitions processes. The
impact of the stock market and its capitalisation on the M&A market is that
one of the main integration schemes is based on the acquisition of shares of the
target company [14]. In this regard, most researchers agree that the correlation
between the indicators of the market volume for the integration transactions and
the development of the stock market is clearly pronounced.
According to the experts of the committee on corporate finance and financial
management of the audit and consulting firm FAC, investments are one of the
indicators of the mergers and acquisitions market movement. Currently, Russia
lags behind most post-Soviet countries in terms of the level of investment for
infrastructure development that is reflected in the fact that the share of for-
eign buyers in the total value of mergers and acquisitions transactions has been
recently decreasing.
According to the research of the CMS consulting company and the Merger-
market analytical agency, which was based on the survey of top managers among
32 large companies and 100 participants of the Russian market of mergers and
acquisitions in 2015, the institutional reforms in the economy and, particularly,
the state privatisation program have impact on the market of mergers and ac-
quisitions in the Russian Federation. The government intends to sell minority
block of shares in the leading state-owned enterprises in order to reduce the bud-
get deficit, increase the investment attractiveness of assets, and attract serious
private investors in privatised companies.
According to the press service of the government, in February 2017, the head
of the government approved the forecast privatisation plan for 2017–2019. The
budget with revenues from privatisation without taking into account the sale
of shares in the largest companies should reach 5.6 billion rubles annually. It
is planned to privatise 477 JSCs, 298 FSUEs, the Russian Federation’s stake in
10 PLCs, as well as 1041 property of the state treasury for the period 2017–
2019 in total. In particular, it is envisaged that Russia will cease to participate
in Novorossiysk Commercial Sea Port PJSC, United Grain Company, Prioksky
Non-Ferrous Metals Plant, and Crystal PA.
When characterising the Russian market, a special attention should be paid
to crimes and offenses in the sphere of economic activity. Clever abuse of pro-
cedural rights during corporate conflicts entails the difficulty and frequently the
complete paralysed activities of economic entities of the Russian market that
causes significant material losses [17]. According to the experts of the depart-
ment for especially dangerous crimes in the sphere of economic activity of the
Investigative Committee attached to the Ministry of Internal Affairs of Russia,
raiding is because the cost of capturing assets in most cases is significantly lower
than the price of their loyal acquisition.
Thus, the factors that directly influence the intensity of mergers and acquisi-
tions processes (see Fig. 1) for the period from January 2003 to December 2015
were divided into 6 functional blocks: macroeconomic indicators (11 variables);
corporate finance (12 variables); the Russian stock market (12 variables); invest-
ments (8 variables); institutional changes in the economy (7 variables); crimes
and offenses in the economic sphere (4 variables). At the same time, it should
be noted that in reality the causal relationship between the integration activ-
ities and selected regressors is multiple-valued [6]: the integration transactions
can affect both the volume of industrial production and the financial results of
companies and sectors of the economy.

Fig. 1. Factors affecting the intensity of mergers and acquisitions processes

One should pay attention to the fact that there is a causal relationship in
the effective variables. The key characteristic of the M&A market is the M&A
market value which directly depends on the number of transactions. At the same
time, due to the specifics of the Russian market for corporate control, the cost
of conflict assets has a direct impact on the market value and the number of
integration transactions.
Further, the Granger causality test was used to test these assumptions for
causal dependency; this test includes the application of the Fisher test which is
used to check whether the lag information about the variable x has a statistically
significant effect in explaining yt at accounting explanatory variables and lagged
values of y [12]:
Xm m
X
yt = α0 + αi yt−i + βi xt−i + εi . (1)
i=1 i=1

If lagged x in the presence of lagged y does not make a statistically significant
contribution to the explanation of yt , then it is considered that “x does not
Granger cause y”. Alike, if lagged y does not contribute a statistically significant
input in explaining xt in the presence of lagged x:
k
X k
X
xt = µ0 + µj xt−j + ηj yt−j + εj , (2)
j=1 j=1

then it is considered that “y does not Granger cause x”.
The hypotheses presented in Table 1 were put forward to clarify the causal
relationship between the resulting factors.
As a result of the analysis, it was found that the value of conflict assets is a
Granger cause for the number of mergers and acquisitions transactions and the
monetary value of the M&A market, but the number of integration transactions
is a Granger cause for the monetary value of the M&A market. All abovemen-
tioned shows that the model of the integration activity in the Russian Federation
can be represented in the form of a recursive system of simultaneous equations.
Based on the method of eliminating quasinemeasurable variables at a criti-
cal value of the coefficient of variation ν ∗ = 0.1, the following 8 characteristics
with no significant information were recognised as quasi-unchangeable and ex-
cluded from the set of potential explanatory variables: the consumer price index;
producer price index of manufactured goods; index of industrial production; the
producer price index of industrial goods; official exchange rate of the Ruble to US
dollars; the number of shares traded on the Russian stock market; the number
of issuers traded on the Russian stock market; the number of crimes committed
in the economic sphere.
Functional and weakly bound regressors were identified with respect to en-
dogenous variables based on the method of analysing the matrix of correlation
coefficients (Table 2).

2.1 The creation of the econometric model of the indicator y1 —
the value of the conflict assets
With the help of cross-correlation function, lagged variables were created based
on the exogenous variables. Lag value τ for variable xj was defined from r(y1t ,
xjt−τ ) = max(r(y1t , xjt−τ )), varying τ from 0 to 12. For instance, the volume
GDP for lag x1 was 8 months. Similarly, lags were defined for the remaining
exogenous variables xj analysed.
Table 1. The results of the study of the Russian M&A market based on the Granger
causality test

General
No Hypothesis Fobserv. Conclusion
conclusion
1 H1: “The cost of the conflict 8.1523 Hypothesis H1 is y1 is the cause of
assets y1 is not a factor that rejected y2 (y2 is not the
determines the change in the cause of y1 )
number of mergers and
acquisitions transactions y2 ”
H2: “The number of M&A 1.1512 Hypothesis H2 is
translations y2 is not a factor accepted
that determines the value of
conflict assets in Russia y1 ”
2 H1: “The value of the conflict 8.7567 Hypothesis H1 is y1 is the cause of
assets y1 is not a factor that rejected y3 (y3 is not the
determines the change in the cause of y1 )
cost volume y3 of the M&A
market”
H2: “The monetary volume y3 1.8796 Hypothesis H2 is
of the M&A market is not a accepted
factor that determines the
change in the value of the
conflict assets y1 ”
3 H1: “The number of mergers 6.4612 Hypothesis H1 is y2 is the cause of
and acquisitions transactions rejected y3 (y3 is not the
y2 is not a factor that cause of y2 )
determines the change in the
monetary volume y3 of the
M&A market”
H2: “The monetary volume y3 1.361 Hypothesis H2 is
of the M&A market is not a accepted
factor that determines the
change in the number of
integration transactions y2 ”

Table 2. Exogenous variables excluded from the further analysis of the integration
activity

y2 — the number of y3 — the monetary value
y1 — the value of conflict
mergers and acquisitions of the mergers and
assets
transactions acquisitions market
x6 , x14 , x22 , x25 , x32 , x33 , x1 , x12−15 , x17 , x19 , x22 , x6 , x12 , x14 , x17 , x19−20 ,
x38 , x46 , x48 , x53 x24−25 , x32−33 , x36−45 , x22 , x25 , x32−33 , x38 ,
x47−50 , x53 x41−50 , x53
At the same time, a repeated analysis of the matrix of paired correlation
coefficients of the 15 remaining characteristics showed the presence of multi-
collinearity. In order to preserve the number of endogenous variables for further
economic interpretation, the transition to an orthogonal coordinate system was
used [4].
The implementation of the principal component method with the subsequent
orthogonal rotation resulted in the five generalised factors that explain 83.04% of
the total variance. The regression equation for y1 — the value of conflict assets,
created based on the individual values of the generalised factors f1 − f5 , has the
form:
ŷ1t = 0.51f1 − 0.62f2 + 0.29f3 + 0.29f4 + 0.31f5 .
(12.17) (−10.58) (7.14) (7.29) (6.03)

All regression coefficients in the equation are significant at the level α =
0.05. The parameters of the equation obtained show its statistical adequacy:
Fobserv > Fcrit with α = 0.05, found under the table of the F -distribution. The
standard error was 0.28.
Based on the factor load matrix that characterises the close relationship
between the characteristics and the main components, as well as the matrix of
eigenvectors with respect to the original variables, the regression equation takes
the form:

ŷ1,t = 0.12 + 0.23y1,t−1 + 0.07x1,t−8 − 0.02x4,t−9 + 0.32x24,t−5 +
+ 0.19x26,t−2 + 0.21x29,t−3 + 0.05x36,t−8 + 0.14x37,t−7 − 0.03x44,t−5 −
− 0.07x45,t−5 + 0.12x47,t−8 − 0.12x50,t−6 + 0.07x54,t ,

where R2 = 0.8415, Fobserv = 53.03, ŝ = 0.39.
To investigate the presence of autocorrelation in the remainder, an asymp-
totic criterion of the Breusch–Godfrey serial correlation was used, which is based
on the idea that if there is a correlation between neighboring observations, then
it is natural to expect that in equation (3) the coefficient ρ turns out to be
significantly different from zero [9].

et = νt + ρet−1 , (3)

where et is the remainder of the regression equation.
According to available data, the coefficient ρ = 0.019. It is not significantly
different from 0, therefore there is no autocorrelation in the remainder.

2.2 The creation of the econometric model of y2 — the number of
integration transactions.
The analysis carried out made it possible to reveal that since the instant in time
t∗ = 69 (September 2008) a structural change takes place in the character of the
dynamics of the indicator under study. This instant in time is characterised by
the beginning of the financial and economic crisis in Russia. Buyers and sellers
have different reasons for mergers and acquisitions during the periods of economic
growth from reasons for integration during the financial and economic crisis
[11] that cannot influence the integration activity of Russian companies. Under
the conditions of the financial and economic crisis the imbalance of supply and
demand in the M&A market affects the formation of the reasons for integration.
In econometrics, several formalised tests have been developed that allow one
to determine the presence of a structural shift in the available data. In this paper,
the Chow test was applied, which showed that a structural shift was observed
in September 2008. Therefore, the parent population was divided into two parts
in terms of improving the model quality relative to the instant in time t∗ = 69.
Further, with the use of the cross-correlation function, the lag variables
were created based on the exogenous variables. Thus, the lag for x7 — the
deficit/proficit of the consolidated federal budget is 7 months. The lags for other
exogenous variables xj were created similarly.
The analysis of the matrix of paired correlation coefficients showed a high
multicollinearity in the independent variables. The approach to maximise the
predictive power of regression models [1] has revealed that a reduced set of
indicators can contain 6 endogenous variables: x10 , x16 , x18 , x20 , x31 , x54 .
To use the entire set of observations in the quantitative volume model of the
mergers and acquisitions market, a dummy variable ut was used that takes the
values 1 for all t < t∗ and the value 0 for t ≥ t∗ , i. e.
(
1, t < t∗
u=
0, t ≥ t∗ , t∗ = 69.

The discrete nature of the dependent variable affords ground to assume that
linear models that connect the number of mergers and acquisitions transactions
with the levels of the factors accompanying them will not be entirely adequate to
actual data because the calculated values ŷ2t can take both integer and fractional
values. Count data models, in particular the Poisson regression model are more
acceptable in such situations:
00
Yi = eβ xt +εt ,

i. e. it is assumed that the number of events yt is distributed according to Pois-
son’s law with the parameter λt = eβxt .
The maximum likelihood method was chosen to create Poisson multiple re-
gression (calculations were carried out using the Matrixer package). As a result,
the following model count data was obtained:

ln ŷ2,t = 2.03 + 0.11 ln y2,t−1 + 0.05 ŷ1,t−1 −
(4.02) (6.02) (4.47)

− 0.08 x16,t + 0.003x31,t + 0.12 x54,t + 0.01 ut .
(−8.05) (5.27) (4.01) (3.53)

The likelihood ratio criterion was used to test the hypothesis of the signif-
icance of Poisson regression. Since χ2observ = 62.37 > χ2crit (0.05; ν = 1) = 3.84,
then the created Poisson regression equation is significant as a whole.
According to the available data, the pseudo-coefficient of determination
2
Rpseudo = 0.8423, which shows that 84.23% of the variation in y2 is due to
the factors included in the model. The Akaike information criterion, which takes
into account the requirement of the increased model accuracy and the reduced
number of model parameters, was AIC = 8.03.
The asymptotic criterion of Breusch–Godfrey serial correlation showed the
absence of autocorrelation in the remainder. To check whether the remainders of
the created model count data belong to the Poisson distribution, the following
property was used: the variance of a random variable distributed according to a
Poisson law is equal to its mathematical expectation:
M [y/xt ] = D[y/xt ] = λ. (4)
At that time if the values M (εt ) and D(εt ) are equivalent, then this may
serve as an argument in favor of the hypothesis of a Poisson distribution of
remainder; the sharp difference in these characteristics, on the contrary, testifies
against the hypothesis. Since M (εt ) ≈ 2 and D(εt ) ≈ 1.43 then the hypothesis
of the Poisson distribution of the remainder is accepted that testifies the model
adequacy.
The calculation of the average marginal effects (Table 3) showed that the
value of the conflict assets and the number of mergers and acquisitions trans-
actions related to the previous instant in time has the maximum impact on y2 .
Moreover, the number of criminal cases related to the crimes committed during
illegal seizures of property complexes of legal entities, as well as property and
non-property rights of enterprises (raiding) also has a direct impact on y2 .
The calculation of average marginal effects also showed a unidirectional
change in the number of integration transactions and overdue creditor indebt-
edness, as well as overdue indebtedness for loans and borrowings among large
and medium-sized enter-prises. This can be explained by the fact that the man-
agement of companies that have fallen into a crisis situation has two options:
bankruptcy or business sale, and in most cases they prefer the second one.

Table 3. Average marginal effects for the Poisson regression model

∂M [y2,t /y2,t−1 ] ∂M [y2,t /x18,t ]
0.23054 0.00653
∂y2,t−1 ∂x18,t
∂M [y2,t /y1,t−1 ] ∂M [y2,t /x20,t ]
0.06042 0.18579
∂y1,t−1 ∂x20,t
∂M [y2,t /x16,t ] ∂M [y2,t /x54,t ]
-0.1503 0.27193
∂x16,t ∂x54,t
∂M [y2,t /ut ]
0.0321
∂u1

At the same time, it is interesting that the nature of the dependence of the
number of mergers and acquisitions transactions on the share of unprofitable
enterprises is the opposite. This may be because a very popular way of seizure
and acquisitions via the bankruptcy procedure was not used so often in the period
under study. This results from the fact that a potential bankrupt company ceases
to function in a normal mode and attention to it weakens.

2.3 The creation of the econometric model of y3 index number —
the cost of integration transactions.

The analysis revealed that, starting from the instant in time t∗ = 65 (May 2010),
a structural change occurs in the character of the dynamics of the indicator
under study resulting in a change in the trend that describes this dynamics.
This instant in time is characterised by the starting of changes in the global
general economic situation and global factors.
The Chow test was used to test the hypothesis and it showed that it is
advisable to divide the initial population into two parts for the further analysis
from the point of view of the improved quality of the model relative to the instant
in time t∗ = 65 (January 2003–May 2008 and June 2008–December 2015).
As in the case of y1 and y2 , the analysis of the matrix of paired correlation
coefficients showed the presence of multicollinearity between independent vari-
ables. The use of the method to maximise the predictive power of the regression
models allowed us to identify that the reduced set of indicators for y3 contains
10 endogenous variables: x1 , x15 , x16 , x18 , x24 , x26 , x31 , x36 , x37 , x54 .
A dummy variable zt , which takes the values 1 for all t < t∗ and the values
0 for t ≥ t∗ , was included to use the entire set of observations in the model of
the monetary value for the mergers and acquisitions market, i. e.
(
1, t < t∗
z=
0, t ≥ t∗ , t∗ = 65.

The resulting regression equation, which was created by the method of in-
cremental inclusion of variables , can be represented as:

ŷ3,t = − 8.03 + 1.53 ŷ1,t + 0.12 ŷ2,t − 0.03 x16,t −
(−2.97) (6.03) (2.78) (−2.92)

− 0.023 x24,t−9 + 0.04 x37,t−7 − 0.04 x54,t + 2.05 zt .
(−3.37) (3.82) (−2.42) (2.49)

R2 = 0.8907, Fobserv = 19.02, ŝ = 0.37.
The equation created indicates that the following indicators such as the pro-
portion of unprofitable organisations in the total number of organisations in
the industry, the volatility of the stock market, the volume of investments from
foreign investors, and the number of criminal cases related to raiding influence
y3 the monetary value for the mergers and acquisitions market. At the same
time, the dummy variable zt , which characterises the structural instability of
the resulting indicator y3t , is the most direct influence.
The asymptotic criterion of the Breusch–Godfrey serial correlation showed
the absence of autocorrelation in the remainder. It can be seen from the his-
togram of the remainders that the remainders obey the normal distribution law.
Thus, the model of the Russian market of mergers and acquisitions can be
represented in the form of a system of equations:



ŷ1,t = 0.12 + 0.23y1,t−1 + 0.07x1,t−8 − 0.02x4,t−9 + 0.32x24,t−5 +




 + 0.19x26,t−2 + 0.21x29,t−3 + 0.05x36,t−8 + 0.14x37,t−7 − 0.03x44,t−5 −
− 0.07x45,t−5 + 0.12x47,t−8 − 0.12x50,t−6 + 0.07x54,t ,





where R2 = 0.8415, Fobserv = 53.03, ŝ = 0.39;




ln ŷ2,t = 2.03 + 0.11 ln y2,t−1 + 0.05ŷ1,t−1 − 0.08x16,t + 0.003x31,t +

+ 0.12x54,t + 0.01ut ,




2




R pseudo = 0.8423, LR = 63.25, ŝ = 0.25;
ŷ3,t = −8.03 + 1.53ŷ1,t + 0.12ŷ2,t − 0.03x16,t − 0.023x24,t−9 + 0.04x37,t−7 −




− 0.04x54,t + 2.05zt , where R2 = 0.8907, Fobserv = 19.02, ŝ = 0.37.


The resulting system of interdependent econometric equations expresses their
new content through their structural form the interrelationship between the
phenomena; this content is characterised by the mutual influence of dependent
and independent variables on each other, as well as allows for a deeper study of
the causes of the relationship underlying the variation of the resulting variables.

3 Conclusion
Each of the equations of the system obtained includes the number of criminal
cases related to unfriendly and illegal acquisitions. These processes have an ob-
jective basis. Tendencies to increase the size of the money supply, which is the
accumulation of both individuals and organisations, are characteristic for the
modern Russian economy. The free money supply overhangs the mark, seeking
an effective application for itself including via the new businesses acquisition
that are often associated with criminal risks.
The application of the results of the calculations performed for the period
from January 2003 to December 2015 allows one to see the opposite relation-
ship between the volatility of the stock market and the value of the market for
mergers and acquisitions. Thus, the increased uncertainty in the capital market
reduces incentives for Russian holdings to implement investment projects, in-
cluding integration projects. At the same time, the increased uncertainty in the
stock market increases the value of conflict assets.

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