Научные записки молодых исследователей, 2017, № 5

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главный редактор журнала

Научные записки молодых исследователей № 5/2017
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ЭКОНОМИКА

Usoltsev M. K., Dvoichenkov V. O.

Macroeconomic Indicators of the Factors Influencing GDP  

(on the еxample of Russian еconomy) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

Паламаренко Е. В.

к вопросу об инновационной активности национальной экономики . . . . . . . . . . .19

Тимонина А. Е., Клеванец В. С.

Финансовая устойчивость домохозяйств 

как фактор экономического развития . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25

Zbarskaya D., Yashina A.

The Role of Tourism in Development of Regional Economy . . . . . . . . . . . . . . . . . . . . .33

Трушникова А. Д.

инвестиционная привлекательность корпорации  

и подходы к ее оценке . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .36

НАЛОГИ, КРЕДИТЫ, ФИНАНСЫ

Барская П. В.

особенности расчета налоговой базы по налогу  

на прибыль в коммерческих банках  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .47

Комельков М. В.

P2P кредитование: альтернативный подход к долговому рынку . . . . . . . . . . . . . .52

Курныкина Е. Е., Полужевцев В. Г.

обоснованность получения налоговой выгоды при применении режима

налогообложения в виде единого налога на вмененный доход . . . . . . . . . . . . . . .58

НОВЫЕ ТЕХНОЛОГИИ

Денисова А. Н.

развитие возобновляемых источников энергии в россии:  

миф или реальность? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61

Никитин Н.А.

трансформация энергетического комплекса:  

опыт Швеции и Германии . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67

ИНСТРУМЕНТЫ ФИНАНСОВОГО МОДЕЛИРОВАНИЯ

Perevalov D. V.

A New Way to Identify High-Frequency Trading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .72

Ратников А. А.

Построение торговой стратегии  

на основе методов нечеткой логики  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .79

наУЧнЫе ЗаПиски
МолодЫх исследователей

1919

1919

Научные записки молодых исследователей № 5/2017
3

CoNTENTs

редакционнЫй 
совет

Председатель  
совета —  
М.а. Эскиндаров, 
ректор 
Финансового 
университета

а.Г. аксаков, 
заведующий 
кафедрой 
финансового 
просвещения 
и корпоративной 
социальной 
ответственности

а.Г. Мишустин,
научный 
руководитель 
факультета налогов 
и налогообложения

в.и. соловьев, 
руководитель 
Департамента 
анализа данных, 
принятия решений 
и финансовых 
технологий

Г.а. тосунян, 
президент 
Ассоциации 
российских банков

а.в. трачук, 
руководитель 
Департамента 
менеджмента

в.в. Федоров, 
доцент 
Департамента 
социологии

л.З. Шнейдман, 
профессор 
Департамента учета, 
анализа и аудита

ECONOMY

Usoltsev M. K., Dvoichenkov V. O.

Macroeconomic Indicators of the Factors Influencing GDP  

(on the еxample of Russian еconomy) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

Palamarenko E. V.

on the Issue of Innovation Activity of the National Economy. . . . . . . . . . . . . . . . . . . .19

Timonina A.E., Klevanets V.S.

Financial sustainability of Households  

as a Factor of Economic Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25

Zbarskaya D., Yashina A.

The Role of Tourism in Development of Regional Economy . . . . . . . . . . . . . . . . . . . . .33

Trushnikova A. D.

Investment Attractiveness of the Corporation  

and Approaches to its Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .36

TAXES, CREDITS, FINANCES

Barskaya P. V.

Features of Calculation Tax Base for Income Tax in Commercial Banks  . . . . . . . . . . .47

Komel’kov M.V.

P2P Lending: An Alternative Approach to the Debt Market . . . . . . . . . . . . . . . . . . . . .52

Kurnykina E. E., Poluzhevtsev V. G.

Validity of obtaining Tax Benefit at Application of the Mode of the Taxation  

in the Form of the single Tax on Imputed Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . .58

NEw TEChNologIES

Denisova A. N.

Development of Renewable sources of Energy in Russia:  

Myth or Reality? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61

Nikitin. N.A.

Transformation of the Energy Complex:  

the Experience of sweden and Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67

INSTRUMENTS oF FINANCIAl MoDElINg

Perevalov D. V.

A New Way to Identify High-Frequency Trading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .72

Ratnikov A. A.

Creating a Trading strategy Based on Fuzzy Logic  . . . . . . . . . . . . . . . . . . . . . . . . . . . .79

Научные записки молодых исследователей № 5/2017
4

редакционная коллеГия

вЫ Можете оФорМить ПодПискУ на жУрнал 
«наУЧнЫе ЗаПиски МолодЫх исследователей»

•  В любом отделении связи «Почта России». 
Подписной индекс по объединенному каталогу 
«Пресса России» 42136

•  В редакции по адресу: 
Москва, Ленинградский проспект, 53, комн. 5.3
Тел.: (499) 943-9431
Менеджер Пунтус Нинель Артуровна

в.н. Засько, 
декан факультета налогов 
и налогообложения

а.н. Зубец, 
проректор по стратегическому 
развитию и практикоориентированному образованию

а.и. ильинский, 
декан Международного 
финансового факультета

и.и. климова, 
руководитель Департамента 
языковой подготовки

р.М. нуреев,
научный руководитель 
Департамента экономической 
теории

М.р. Пинская, 
профессор Департамента 
налоговой политики и таможеннотарифного регулирования

в.ю. Попов, 
профессор Департамента анализа 
данных, принятия решений 
и финансовых технологий

с.а. Посашков, 
декан факультета прикладной 
математики и информационных 
технологий

с.н. сильвестров, 
директор Института 
экономической политики 
и проблем экономической 
безопасности

л.в. клепикова, 
декан факультета учета и аудита

к.в. симонов, 
первый проректор 
по внешним связям

в.н. сумароков, 
советник при ректорате

р.в. Фаттахов, 
профессор Департамента 
общественных финансов

М.а. Федотова, 
руководитель Департамента 
корпоративных финансов 
и корпоративного управления

а.н. Чумаков, 
профессор Департамента 
социологии

в.Ф. Шаров, 
доцент кафедры философии, 
истории и права

а.Б. Шатилов, 
декан факультета социологии 
и политологии

н.т. Шестаев, 
начальник Управления 
внеаудиторной работы

П.в. строев,

кандидат экономических наук, 

директор Центра региональной 

экономики и межбюджетных 

отношений, главный редактор 

журнала

л.и. Гончаренко, 
руководитель Департамента 
налоговой политики 
и таможенно-тарифного 
регулирования

н.и. Пушкарская, 
начальник Управления 
регионального развития

М.а. абрамова, 
прфессор Департамента 
финансовых рынков и банков

в.и. авдийский, 
декан факультета анализа 
рисков и экономической 
безопасности

е.в. арсенова, 
декан факультета менеджмента

е.р. Безсмертная, 
декан кредитно-экономического 
факультета

в.а. дмитриев, 
заведующий кафедрой 
государственно-частного 
партнерства

Научные записки молодых исследователей № 5/2017
5

УДК 330.341.13

MACRoECoNoMIC INDICAToRs oF THE 
FACToRs INFLuENCING GDP (oN THE 
еxAMPLE oF RussIAN еCoNoMy)

Usoltsev M. K., Dvoichenkov V. O.,
students, Financial University, Moscow, Russia
Maxdonrumata@mail.ru
Dvovad@bk.ru

Abstract. The article examines macroeconomic indicators of the effect of innovation on GDP in terms 
of economy of Russia. Innovation and R&D indicators were chosen from all possible macroeconomic 
indicators to be used in this research. The authors conducted correlation analysis, basing on which they 
have constructed a regression model. This model was tested by means of a number of tools, such as F-test, 
t-test, Goldfeld-Quandt test and others. The model was initially used to “predict” the value of Russian GDP 
for the year of 2016, and that “test-drive” was fairly successful. The authors also used the model to predict 
future values of Russian GDP basing on pessimistic and optimistic forecasts. Further development of the 
model consists of inclusion of other countries’ data so that the model would be applicable for any economy.
Keywords: GDP; regression; model; prognosis; innovation

МакроЭконоМиЧеские индикаторЫ Факторов, 
влияющих на ввП (на ПриМере ЭконоМики 
россии)

Усольцев М. К., Двойченков В. О.,
студенты, Финансовый университет, Москва, Россия
Maxdonrumata@mail.ru
Dvovad@bk.ru

Аннотация. В статье рассматривается влияние факторов инноваций на ВВП страны на примере 
экономики России. Проведен анализ различных макроэкономических показателей, определяющих ВВП 
государства, из которых были выбраны индикаторы инновационного развития. Проведенный авторами корреляционный анализ позволил построить регрессионную модель, адекватность которой 
в дальнейшем была протестирована с применением F-тестов, t-тестов, тестов Голдфелда-Квандта 
и некоторых других. С помощью модели в качестве показательного «тест-драйва» было проведено 
прогнозирование ВВП за 2016 г., а также построен прогноз ВВП России на ближайшее будущее на 
основе оптимистических и пессимистических прогнозов экономистов. В дальнейшем авторы планируют расширить и улучшить модель путем включения в анализ данных других стран.
Ключевые слова: ВВП; регрессия; модель; прогнозирование; инновации

Supervisor: Pyrkina O.E., Cand. Sci. (Physico-math.), associate professor, Department of data analysis, decision-making and 
financial technology, Financial University.
Научный руководитель: Пыркина О.Е., кандидат физико-математических наук, доцент Департамента анализа 
данных, принятия решений и финансовых технологий, Финансовый университет.

ЭконоМика

Научные записки молодых исследователей № 5/2017
6

Introduction
All economists know the macroeconomical equation of GDP 1 of a country. This is GDP = C + I + G + 
(X —  M), where C is consumption, I —  investments, 
G —  governmental expenditures and X —  M states 
for net exports. However, a lot more macroeconomical indicators also show viable status of an 
economy. To be precise, Federal State Statistics 
Service of Russia (Rosstat) gives more than a dozen 
of most commonly used. That is why we decided 
to check, whether there is a connection between 
such indicators and the GDP of a country, just like 
the equation stated above.
First, the chosen indicators need to be specified, 
in other words, future variables of the regression 
equation. Out of all possible ones, those were 
chosen which show the implementation or use 
of innovations and modern technologies. Truly, it 
is nowadays commonly known that Russia is not 
a top-ten country when it comes for innovations. 
Moreover, some people think that extensive growth 
could still be better than intensive one. That is 
why the topic of the investigation becomes more 
urgent —  by showing possible relation between 
innovations and GDP it is good to state that government should pay more attention to what is 
important.
The indicators under investigation would be:
1. State financing of scientific development
2. The number of patented innovative technologies used during obscured period
Those two are declared as “innovations and R&D 
indicators” in Rosstat 2, because of that we chose 
them for our research.
We need to determine precisely the indicators 
to understand fully their meaning.
First, the state financing of scientific development —  all figures are given in million rubles. That is, 
from our point of view, simple to comprehend —  this 
is the amount of money that is spent by government 
on various scientific researches and the implementations of scientific breakthroughs.

1 Wikipedia page concerning GDP of Russia. URL: https://
ru.wikipedia.org/wiki/ВВП_России (аccessed: 18.04.2017). 
(In Russ.).

2 Official web-page of Rosstat. Innovations and R&D section. URL: http://www.gks.ru/wps/wcm/connect /rosstat_main/rosstat/ru/statistics/science_and_innovations/
science/# (аccessed: 18.04.2017). (In Russ.).

The number of patented innovative technologies also gives us a simple amount of new patents 
that were given to the population of the Russian 
Federation at a given period.
Overall, above-mentioned indices show various 
aspects of innovative policies in Russia. It was suggested that there is a connection between those 
and the GDP of our Motherland, which later would 
be checked later in this work.

Economic Review
As it was already mentioned, many factors could 
influence the GDP of a country.
First, let us mention factors that are mainly considered as GDP-forming. Those are Consumption, Investments, Government expenditures and Net Exports.
Consumptions states personal consumption expenditures of the citizens of a country. They are typically broken down into Durable goods, Non-durable 
goods and Services. Investments are gross private 
investments, broken into changes in business inventory. Government expenditures include spending on 
items that were consumed in the given period. Net 
Exports explain the amount of exports subtracted 
the amount of imports in the given period. However, 
not only those microeconomical coefficients could 
be used in estimating the GDP 3.
In 2010, professor Grishel of Grodno University in 
Belarus declared in her article “Brand of a country as 
an economical factor” [1, p. 3–4] that deterioration 
of capital assets could influence GDP. However, she 
stressed that there is practically no statistical data 
available concerning the data on deterioration since 
its calculation is very time-consuming.
“Analysis of primary income”, written by Lozovski 
and Raizberg [2, 2012, p. 231], states that primary 
income could be a crucial indicator that could be 
used in estimating the value of GDP in a country. 
They claimed that primary income could solely give 
good values of GDP.
In addition, in 2006 a group of American scientists 
invented so-called “International Happiness Index” 4. 
Using this index and the values of GDP of 178 coun
3 Official web-page of the Financial University, research on 
initial macroeconomic index. URL: http://www.fa.ru/institutes/
efo/science/Pages/index.aspx (аccessed: 19.04.2017). (In Russ.).

4 Official web-page of the World Happiness index Group 
with index data. URL: http://worldhappiness.report/download (аccessed: 13.05.2017).

ЭконоМика

Научные записки молодых исследователей № 5/2017
7

tries, they told that countries with high levels of 
happiness among citizens had higher values of GDP.

Analytical Part
statistical Data
First, the endogenous and exogenous variables 
in the model need to be determined. The only 
endogenous variable would be the GDP of Russian Federation 5, later denoted by Y [Appendix 1]. 

5 Official web-page of Rosstat. GDP section URL: http://
www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/
statistics/accounts/# (аccessed: 18.04.2017). (In Russ.).

Exogenous variables, hence, would be the macroeconomical indicators discussed in the introductive 
part. State scientific financing is X1 [Appendix 2], 
and Number of Innovative technologies is X2 [Appendix 3].
All the statistical data is presented from the first 
quartile of 2005 to the last quartile of 2014 with 
the quartile steps, namely there are 40 measurements. All data is given in respective Appendices 
for this work.
For better understanding of the relations between variables and the GDP, the scatter diagrams 
were plotted for each one of them.

Macroeconomic Indicators of the Factors Influencing GDP (on the еxample of Russian еconomy)

Diagram 1. Correlation Field of GDP and scientific Financing

Source: Rosstat [Appendix 1, Appendix 2].

Diagram 2. сorrelation Field Between GDP and Number of Innovative Technologies

Source: Rosstat [Appendix 1, Appendix 3].

Научные записки молодых исследователей № 5/2017
8

Diagram 1 represents the relation between GDP 
and State Scientific Financing. Here we can clearly 
see that the relationship exists and it is quite nice. 
The trend line shows small deviations from it —  this 
means high correlation between GDP and State 
Scientific Financing.
Diagram 2 shows existence of correlation between GDP and number of Innovative Technologies. 
Despite the fact that the image is not that good as 
the first one, it is still fine. To prove the existence 
of possible correlation, examined in Diagram 1 and 
Diagram 2, one need to construct the correlation 
matrix, which is shown below.

Y
X2
X3
Y
1
X2
0,971
1
X3
0,738 0,7197
1

Diagram 3. сorrelation Matrix  
of GDP (y), scientific Financing (x1)  
and Number of Innovative 
Technologies (x2)

Source: Rosstat [Appendix 1, Appendix 2, Appendix 3].

From the Diagram 3, it can be seen that all coefficients have strong correlation with each other and 
Y. That is why we can state that the variables are 
good for exploitation and prognosis.

Econometrical 
Model
First, one need to ensure the regression equation 
in its initial form exists.
Assuming Gauss-Markov conditions hold, it 
should look as following:

( )
( )

1
2
1
3
2
0
.

Y
X
X
E
Var
const
ε


= β +β ×
+β ×
+ ε

ε


=


=


As Y array, Y [Appendix 1] data would be used, 
as X array —  X1 [Appendix 2] and X2 [Appendix 3]. 
Using the initial form of the regression equation, 
we can proceed to estimating coefficients using 
the “regression” service in MS Excel.

 

Coefficients
Y-intercept
1508,9437
Variable X 1
0,0305
Variable X 2
0,0163

Diagram 4. Estimated coefficients of the equation

Source: RosStat [Appendix 1, Appendix 2, Appendix 3].

After finding the estimated coefficients one could 
use them to construct the estimated regression 
equation, which would be:



( )
( )

1
2
1508,94
0,031
0,016
0
963970,91.Y
X
X
E
Var


=
+
×
+
×
+ ε

ε
=
ε



=


This model needs to be specified and tested before accepting.
The coefficients of the model state that for every 
unit increase in X2 Y would increase by 3% and for 
every increase in X2 Y would roughly increase by 1,6%.
Then R² test was performed. The results are:

 

R-squared
0,9456
Adjusted R-squared
0,9427

Diagram 5. R² Data for the Regression Equation

Source: Rosstat [Appendix 1, Appendix 2, Appendix 3].

For the initial equation, determination coefficient 
(R²) is 0.946, which means that almost 95% of data 
under consideration is covered by the regression 
equation. Since the estimated coefficients were 
used in second equation, we need adjusted R² value, 
which is 0.943. That means that 94.3% of the data 
could be explained by the estimated equation, which 
is a nice result. The determination test gives good 
results and we could proceed to other tests.
After assessment of the determination coefficient, 
one need to value the significance of the model. For 
this, Fischer’s F-test would be applicable.

 

F crit.
F emp.
4,0982
321,81

Diagram 6. F-values for F-test

Source: RosStat [Appendix 1, Appendix 2, Appendix 3], F-distri
bution table.

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Научные записки молодых исследователей № 5/2017
9

It can be obviously seen that the F value that was 
empirically got from the data is much greater than 
the critical F value from the table of F-distribution 
(321.81 > 4.098). That means that the regression 
equation is statistically significant. Significance of 
the model leads us to other tests.
Since the model is statistically significant, one 
should also assess the significance of the estimated 
coefficients of the equation. For this, Student’s ttest is applied.

 

Coefficients
t-statistics
Variable X 1
0,031
16,517
Variable X 2
0,016
1,482

Diagram 7. T-statistics Values for Coefficients

Source: Rosstat [Appendix 1, Appendix 2, Appendix 3], t-distri
bution table.

Both t-values of coefficients gave good results 
(16.517 for b1 and 1.482 for b2) which leads to the 
statistical significance of the coefficients of the 
regression equation. While the coefficients are significant, we can be sure that we do not need to get 
rid of any variable. However, the heteroscedasticity 
should also be checked.
The Goldfeld-Quandt test was performed to find 
or reject the heteroscedasticity of data. The sum of 
squared errors in the first third was 2894689.921 
[Appendix 4], and the same sum for the last third 
was 15802589.68 [Appenndix 5].

 

F
5,4592
Fcrit
2,2

Diagram 8. F-values for GQ Test

Source: Rosstat [Appendix 4, Appendix 5], F-distribution table.

From the table of F-distribution it was seen that 
the critical value of F for our data would be 2.2 
while GQ test gave us 5.459, which means that we 
reject the hypothesis of heteroscedasticity, hence, 
our data appears to be homoscedastic. This means 
that data is uniformly dispersed around the trend 
line. However, autocorrelation needs to be tested 
before approval of the model.
Durbin-Watson test should prove the absence 
of autocorrelation in the data. Using the tables of 
Durbin-Watson coefficient and the knowledge of 
DW-test, such data showed out:

 

Dl
1,39
Du
1,6
DW
1,636

Diagram 9. Data for the DW Test

Source: Rosstat [Appendix 2, Appendix 3], DW-distribution table.

This means that our DW-value is greater than the 
upper critical value for the DW-statistics (1.636 > 1.6). 
Following assumption would be the absence of autocorrelation in the data. This is a very good result, 

Macroeconomic Indicators of the Factors Influencing GDP (on the еxample of Russian еconomy)

Diagram 10. Time series Graph of True and Estimated Values of GDP

Source: Rosstat [Appendix 1], Estimated GDP values.

Научные записки молодых исследователей № 5/2017
10

meaning that our model is not needed to be reconstructed. All necessary tests were done by this point, 
so the rule-of-thumb analysis is coming.
Overall, from the tests performed it turned out 
that the model is statistically significant —  it covers almost 95% of the data. The coefficients of the 
regression equation are statistically significant too. 
The data is homoscedastic and there exists no autocorrelation. All those tests have proven the adequacy 
and applicability of the model.
After that it was interesting to see, how well 
the approximated values correspond to the true 

values of GDP. For that a time series graph was 
plotted.
As it turns out from the Diagram 10, estimated values of GDP lie very close to true ones without significant dispersion. However, despite the fact that overall 
quality of the model is good, there is a possibility to 
make the approximation even better. For that one 
should analyze the residuals and see, what can be done.
From the “regression” tool in Excel the residuals were found and used to plot the above graph. 
From the Diagram 11 possible trend could be seen. 
It seems that every year the first two quartiles give 

Diagram 12. Time series Graph of GDP and Predicted GDP

Source: Rosstat [Appendix 1], Estimated GDP values.

Diagram 11. Residuals Histogram

Source: Rosstat [Appendix 1], Estimated GDP values.

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Научные записки молодых исследователей № 5/2017
11

smaller residuals than last two. Moreover, the first 
quartile value is the highest negative value and the 
fourth quartile possesses highest positive values.
Using this knowledge, one could decide to introduce another lag variable with “-1” in the first 
quartiles, “0” in the second and third and “1” in every 
fourth quartiles for better approximation. Here the 
new time series graph is presented.
After carrying out a regression analysis one more 
time with the values for quartiles it was found out 
that the model became much better. Here it is:

( )
( )

1
2
1
3
2
4
3

0
.

Y
X
X
X
E
Var
const


= β +β ×
+β ×
+β ×
+ ε

ε
=
ε


=


Now one needs to perform the estimation of 
coefficients again.

Diagram 13. R2 Values for the New Model

Source: Rosstat [Appendix 7].

Diagram 13 means that introduction of new fiction variable let us cover almost 3% more of data!

 

Coefficients
Y-intercept
1901,1927
Variable X 1
946,2325
Variable X 2
0,0303
Variable X 3
0,0145

Diagram 14. Coefficients of New 
Regression Equation

Source: Rosstat [Appendix 7].

Other coefficients of the model did not change 
significantly and they still possess all qualities of 
the previous model. Overall, the best possible regression equation was capable to find, is:

( )
( )

1
2
3
1901,193
946,233
0,03
0,015
0
509102,3.

Y
X
X
X
E
Var


=
+
×
+
×
+
×
+ ε

ε
=
ε


=


where X1 is a fictional introduced variable.

Economic Analysis  
of Model Results
First, the definition of what a nominal GDP is. 
“Gross domestic product (GDP) is a monetary 
measure of the market value of all final goods 
and services produced in a period (quarterly or 
yearly). Nominal GDP estimates are commonly 
used to determine the economic performance of 
a whole country or region, and to make international comparisons.” This is why it was decided 
to choose nominal GDP instead of real GDP —  to 
make it possible to draw connections to other 
countries in the future.
All X variables are taken from the official 
state statistics, which may serve as a proof of 
their significance. Our country nowadays suffers 
from a severe lack of Innovations in economics. 
However, government decided to avoid spending 
too much on innovations, research, and development because we have many natural resources 
to export. However, there is a historical cause 
not to think so.
In the 1980th USSR also followed the road of 
extensive growth and exported a lot of oil and 
gas for a sustainable economy. However, this 
policy led to an economic crisis of late 1980th 
and, hence, derived the decay of the Soviet Union itself.
Our investigation showed a clear dependence 
of GDP and innovation policy of the country. Since 
GDP is a most common way to assess country’s 
economics, We would like to say that it is unwise 
to ignore innovations that could affect our main 
competitive index.
Using our model one could easily find the possible value of GDP at a given point in time knowing, 
of course, the indices and the quartile of the year.

Model Forecasting
Since all the indices are calculated every month and 
GDP values —  only in quartiles, there is a possibility 
to assess the GDP values in shorter periods. That 
gives wider abilities for economists to compare and 
contrast intra and intercountry performance.
Let us perform a forecasting for 1 year, namely, 
for the end of 2016.
The value of GDP at the 31.12.2016 was 
24076.8751 billion rubles. Corresponding values 
of coefficients are 527161.3 and 254733.29. Using the regression equation it is possible to find 

Macroeconomic Indicators of the Factors Influencing GDP (on the еxample of Russian еconomy)

Научные записки молодых исследователей № 5/2017
12

the estimated value of GDP for the end of 2016. 
This gave us 22461,3291, while the true value was 
24076,8752. The graph shows that the deviation is 
relatively small:
According to this investigation, we can see that 
the model could be used for forecasting future values of GDP.
Using this knowledge, it turned out to be necessary to forecast positive and negative future values 
for GDP using the model.
First, official documents were checked to find 
the information concerning any data that would 
be used in the forecasting.

On 3.03.2017 the government of the Russian 
Federation published 6 its strategy for the economic 
development in the sphere of innovations.
From this document, it comes to mind that innovations come to focus of our economic strategy. 
Real figures are not stated but there are words “we 
expect doubled outcome from the implementation 
of the strategies discussed” lead us to understand
6 Official web-page of the Government of Russian Federation concerning its plans on innovative development. 
URL: http://government.ru/govworks/28/events, Accessed 
13.05.2017. (In Russ.).

Diagram 15. Forecasted GDP for 2016

Source: [Appendix 1].

Diagram 16. Positive Forecast for GDP up to 2018

Source: [Appendix 1], [Appendix 6].

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