Научные записки молодых исследователей, 2017, № 5
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слово редактора Меня зовут Павел Викторович Строев, и я новый главный редактор нашего журнала «Научные записки молодых исследователей». Немного о себе: кандидат экономических наук, директор Центра региональной экономики и межбюджетных отношений и доцент Департамента общественных финансов Финансового университета. Наука — это моя страсть. В Финансовом университете проводится большое количество научных исследований как профессорско-преподавательским составом и научными сотрудниками, так и студентами разных курсов и факультетов. Студенты, магистранты и аспиранты принимают активное участие в серьезных работах для органов законодательной и исполнительной власти, общественных и коммерческих организаций. Мы обязательно будем продолжать с ними знакомиться в наших будущих номерах. «Научные записки молодых исследователей» — отличная возможность для апробации своих идей и публикации результатов проведенных научных исследований. Журнал дает возможности как в личном развитии, так и в профессиональной, и научной сфере. Нашему журналу предстоят большие свершения. Многое уже сделано и достигнуто, спасибо за это председателю редакционного совета М.А. Эскиндарову, руководству нашего вуза, моим предшественникам Л.И. Гончаренко и О.В. Карамовой, редакции и членам редколлегии, и, конечно же, вам, дорогие читатели! Многое еще предстоит сделать. Я с оптимизмом смотрю вперед и уверен, что «Научные записки молодых исследователей» ждут новые высоты и горизонты! Но это невозможно без вас! Талантливым и творческим людям свойственно желание постоянно расти и находить новые области применения своих знаний и способностей. Приглашаю всех заинтересованных активных молодых людей к организационному и научному сотрудничеству. Редакция открыта для новых идей и предложений, наш e-mail: vestnikfinu@mail.ru. Записывайте свои мысли, не бойтесь и пробуйте, ведь только так рождаются качественные научные работы и новые идеи. Удачи! Уважаемые читатели! П.В. Строев, кандидат экономических наук, директор Центра региональной экономики и межбюджетных отношений, главный редактор журнала
Научные записки молодых исследователей № 5/2017 2 содержание Учредитель ФГоБУ «Финансовый университет при Правительстве российской Федерации» свидетельство о регистрации Пи № Фс77-67073 от 15 сентября 2016 г. Главный редактор П.в. строев, канд. экон. наук Заведующий редакцией научных журналов в.а. Шадрин Выпускающий редактор и. с. довгаль Корректор с.Ф. Михайлова Верстка с.М. ветров Мнение редакции и членов редколлегии может не совпадать с мнением авторов. Письменное согласие редакции при перепечатке, ссылки при цитировании статей журнала «Научные записки молодых исследователей» обязательны. Подписной индекс в объединенном каталоге «Пресса России» — 42136. По вопросам подписки и приобретения журнала в редакции звонить (499) 943-94-31 e-mail: NAPuntus@fa.ru Пунтус н.а. Почтовый адрес редакции: 125993, Москва, ГСП-3, Ленинградский проспект, д. 53, 5-й этаж, комн. 5.4. Тел.: (499) 943-94-53 http://www.fa.ru/dep/ scinotes/journal/Pages/ Default.aspx E-mail: vestnikfinu@mail.ru Формат 60 × 84 1/8 Заказ № 1168 от 28.11.2017 Отпечатано в Отделе полиграфии Финуниверситета (Ленинградский пр-т, д. 49) 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. ЭконоМика
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. ЭконоМика
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]. ЭконоМика