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situated in so-called "gray zone", where is no statistical evidence that the error terms are positively or negatively autocorrelated. Thereby it is impossible to say, whether there is presence of correlation or not. This was the best possible way to construct model, using above mentioned indicators for the last 20 years.
Sweden has an export-oriented mixed economy. Timber, hydropower and iron ore constitute the resource base of an economy with a heavy emphasis on foreign trade. Nevertheless, not all resources are subjects to trade, industrial production is another important sector of economy.
Analysis of the results shows us how complicated it can be to identify "Dutch disease" in country, where no clear signs of it, like in UAE or Saudi Arabia. Possible autocorrelation of both models is not a good sign, so it is difficult to make fair conclusion on this problem. One of the reasons of such result can be changes in structure of economy, where ores and metal export is increasing, or, vice versa, decreasing. Following observations are recommended.
List of literature:
1. De Vylder Stefan. "Den "hollandska sjukan" och bistand" 09.2010, URL:http://nationalekonomi.se/filer/pdf/20-6-sv.pdf
2. Dutch disease. URL:www.thefreedictionary.com/Dutch+disease
3. Munkhammar V. Makroradet: Sverige lider av hollandska sjukan, 14.09.2015 URL:http://www.di.se/artiklar/2015/9/14/makroradet-sverige-lider-av-hollandska-sjukan/
4. Magnusson Par. Varning for hollandska sjukan, 15.09.2015 URL:http://www.di.se/di/artiklar/2015/9/15/varning-for-hollandska-sjukan/?timestamp=1442534400317
5. Трегуб А.В., Трегуб И.В. Методика прогнозирования показателей стохастических экономических систем //Вестник Московского государственного университета леса - Лесной вестник. - 2008. - №2 (59). -С. 144-152.
6. Трегуб И.В., Хацуков к.л. Проверка применимости модели для прогнозирования экономических показателей // Экономика и социум. 2014. № 4-4 (13). С. 1345-1349.
УДК 339.727.2
Nadyrova E.M. master student
1 year, "International Finance" department Financial University under the Government of the Russian Federation
Russia, Moscow
RESEARCH OF FDI INFLOWS DETERMINANTS IN THE RUSSIAN
FEDERATION
Abstract:
The aim of the study was to evaluate the factors affecting the FDI inflow to Russia. The influence of four important parameters was identified. They were:
GDP in the previous period, total reserves (including gold), R&D expenditure and average annual OPEC crude oil price. At the same time export in the previous period, inflation, number of R&D researchers and FDI in the previous period were not essential for investors. The need of further institutional analysis was revealed.
Key words: capital flow, foreign direct investment, investment attraction, econometric model, institutional factors.
Among the diverse forms of economic activity foreign direct investment (FDI) is becoming increasingly important. It is an essential element of the global economy and international finance. FDI are long-term investments in companies and banks when a foreign legal entity or individual acquires control and participates in the enterprise operation. They can have various forms, for example, shares acquisition or a new project set-up.
FDI attraction into the country is one of the key sources of economic growth along with increase in high-tech production output and diversification of the economy. FDI can guarantee the sustainability of the economic growth because of its specific feature: the inflow of technology and know-how.
Many scientists dedicate their theoretical studies to the problem of foreign direct investments and the impact they have on different countries. Among the most modern researchers are W. Thorbecke, M. Blomstrom, H. Gorg, L. Alfaro, R. Lipsey. Some effects of FDI, for example, technological exchanges, are discussed in detail in studies of K. Ramanathan, H. Blalock and others. Among the Russian authors O.G. Golichenko, V.I. Tinyakova, G.B. Kleiner, V.N. Lifshitz work on common investments problems in the economy.
The problem of the research is of high importance and relevance as Russia attracts the least amount of FDI compared to the BRICS countries. In 2014 more than 22 billion dollars was invested in the Russian economy and in 2013 - more than 69.2 billion. The FDI inflow rate is now at its lowest point for the past 10 years. Even in the crisis year 2009 the figure was higher - $ 36.6 billion.
The main barriers for FDI attraction are the low level of economic diversification, low efficiency of public administration, corruption as well as other problems. Companies are not sure in the investment climate stability and are not willing to do business because of high risks.
In these terms the aim of this scientific paper is to define the factors that affect the FDI inflows to the Russian Federation. To analyze their impact annual data of main economic indicators from 1996 to 2015 was collected and econometrical model based on linear regression was built.
The author puts forward a series of hypotheses that are tested statistically.
Hypothesis 1. One of the key determinants of FDI is the gross domestic product. It symbolizes consumer potential of the market and, consequently, perspective profitability. It should be noted that this variable represents the preceding period as in practice investors make judgements based on accurate statistical indicators of the previous period rather than incomplete or inaccurate
current period data.
Hypothesis 2. FDI inflows positively correlate with the reserves of the country (total reserves including gold) because they eliminate the default risk of the loan agreement almost completely and reduce the likelihood of unplanned changes in the national currency. As a consequence, the credibility of the economic policy increases, international relations strengthen and the business activity of the population rises.
Hypothesis 3. Export is shipping of the goods and services out of the jurisdiction of a country. In other words, this figure shows what amount is spent in the international market and, therefore, not consumed by the country. As a result, it is assumed that export determines FDI (negative correlation). As in the H1, this variable is chosen in the previous period.
Hypothesis 4. The main factor that stops foreign investors is inflation. By the way, over the past 5 years the inflation rate was 50%. In 2015 inflation reached 15% that is more than the crisis year level (2008 - 14%).
Hypothesis 5. Investors take into account information on the R&D achievements of Russian companies because innovations and unique production can ensure high return. R&D expenditure and number of researchers in R&D are included as defining parameters.
Hypothesis 6. Current foreign direct investments correlate with the FDI level in the previous period as investors take into consideration historical data.
Hypothesis 7. Oil plays a key role in the economies of countries that export crude oil and petroleum products. Rise in world oil prices has a beneficial effect on the Russian economy allowing increase in revenue in the global market. For this reason, foreign direct investment inflows rise.
_Table 1. Descriptive analysis of the variables_
Hy-poth. Variable Abbr. Measurement Arithm. mean Stand. dev. Source of inf.
1 GDP in the previous period (X1) GDPt-1 Current billion US$ 976 731 Worldbank
2 Total reserves (includes gold) (X2) Reservest Current billion US$ 249 208 Worldbank
3 Export in the previous period (X3) Exportt-1 Current billion US$ 291 197 Worldbank
4 Inflation, consumer prices (X4) Inflationt Annual % 18 19 Worldbank
5 Research and development expenditure (X5) R&D expendituret % of GDP 1 0 Worldbank
Researchers in research and development (X6) Researchers in R&Dt Per million people 3287 195 Worldbank
6 Foreign direct investment inflow in the previous period (X7) FDIt-1 Current billion US$ 25 25 Worldbank
7 Average annual OPEC crude oil price (X8) Oil pricet US$ per barrel 54 34 Statista
X1, X2, X3, X4, X5, X6, X7, X8 are ti he regressors that define t
endogenous variable Y - FDI inflow. Mathematically, the model will have the following form:
y = a0 + atXl + a2X2 + a3X3 + a4X4 + a5X5 + a6X6 + a7X7 + a8X8 + st
e( EJ = 0 a(st) = const
Taking into account given information the author applied the least square method with the help of the econometric program EVIEWS.
The initial estimation of the impact of chosen variables on FDI are presented in Table 2.
Table 2. Initial estimation at EVIEWS
Variable Coefficient Std. Error t-Statistic Prob.
C 3.660341 102.5928 0.035678 0.9722
GDPt-1 -0.025195 0.036446 -0.691306 0.5037
Reservest 0.095319 0.046874 2.033523 0.0668
Exportt-1 0.016229 0.161592 0.100431 0.9218
Inflationt 0.013629 0.180035 0.075703 0.9410
R&D expendituret -30.66241 35.96744 -0.852505 0.4121
Researchers in R&Dt 0.007608 0.023900 0.318316 0.7562
FDIt-1 0.191008 0.236119 0.808949 0.4357
Oil pricet 0.407126 0.220642 1.845187 0.0927
R-squared 0.900577 Mean dependent var. 25.63776
Adjusted R-squared 0.828270 S.D. dependent var. 24.82822
S.E. of regression 10.28889 Akaike info criterion 7.802169
Sum squared resid 1164.473 Schwarz criterion 8.250249
Log likelihood -69.02169 Hannan-Quinn criter. 7.889639
F-statistics 12.45486 Durbin-Watson stat 2.397856
Prob (F-statistic) 0.000163 - -
After the first estimation some variables should be excluded because of their coefficient insignificance, iteration by iteration (X4-X3-X6-X7). Finally, there will be a new model with a new range of significant variables. Table 3. Final estimation at EVIEWS
Variable Coefficient Std. Error t-Statistic Prob.
C 30.59971 27.40552 1.116553 0.2812
GDPt-1 -0.019132 0.006113 -3.129715 0.0069
Reservest 0.100548 0.031466 3.195438 0.0060
R&D expendituret -31.20867 24.47998 -1.274865 0.2218
Oil pricet 0.43052 0.182218 2.362843 0.0321
R-squared 0.892294 Mean dependent var. 25.63776
Adjusted R-squared 0.863572 S.D. dependent var. 24.82822
S.E. of regression 9.170597 Akaike info criterion 7.482200
Sum squared resid 1261.498 Schwarz criterion 7.731133
Log likelihood -69.82200 Hannan-Quinn criter. 7.530794
F-statistics 31.06686 Durbin-Watson stat 1.970464
Prob (F-statistic) 0.000000 - -
Y = 30.6 - 0.02 *X1 + 0.1 *X2 - 31.2 * X5 + 0.4 * X8
or
FDIt = 30.6 - 0.02GDPt-1 + 0.1Reservest - 31.2R&D expendituret +
+0.40il pricet
The influence of four important parameters on foreign direct investment in the Russian Federation has been identified. They are: GDP in the previous period, total reserves (including gold), R&D expenditure and average annual OPEC crude oil price. At the same time export in the previous period, inflation, researchers in R&D and FDI in the previous period are not essential for investors. Interestingly, the calculations do not confirm the hypothesis about positive correlation between FDI and GDP in the previous period and between FDI and R&D expenses while negative correlation takes place.
The R-squared is rather high and gives 89% probability of the true forecast based on this model. It is also clear that the real observed value of FDI inflows fits into the confidence interval. That means that the model is adequate and can be used for forecasting.
To confirm the third Gauss-Markov condition the Durbin Watson test is used. As the DW statistics is between dU and 4-dU, there is no autocorrelation in residuals, the third Gauss-Markov condition is confirmed and the OLS can be used for estimating the coefficients.
Table 4. Durbin Watson statistics
0 dL dU DW 2 4-dU 4-dL 1
0 0.9 1.83 1.97 2 2.17 3.1 1
Homoscedasticity of random disturbances is checked with the help of the White test (Fig. 1). As probability of the null hypothesis is much more than 5%, the test did not reveal the presence of heteroscedasticity.
F-statistic 1.119036 Prob. F(14,5) 0.4885
Obs'R-squared 15.16125 Prob. Chi-Square(14) 0.3672
Scaled explained SS 10.10539 Prob. Chi-Square(14) 0.7544
Variable Coefficient Std. Error t-Statistic Prob.
C -632.0242 7223.759 -0.087492 0.9337
XV2 -0.000425 0.000818 -0.519224 0.6258
X1*X2 0.001793 0.005083 0.352743 0.7387
X1*X5 -2.796317 4.596155 -0.608404 0.5695
X1*X8 0.009472 0.011451 0.827222 0.4458
X1 3.452085 4.895969 0.705087 0.5122
X2A 2 0.008821 0.012988 0.679203 0.5272
X2*X5 1.706250 32.71749 0.052151 0.9604
X2*X8 -0.142438 0.219302 -0.649508 0.5447
X2 -1.151430 35.37248 -0.032552 0.9753
X5A2 915.7133 4487.304 0.204068 0.8463
X5*X8 -25.43589 153.7578 -0.165428 0.8751
X5 -631.9338 11468.16 -0.055103 0.9582
X8A2 0.236878 0.626640 0.378014 0.7209
X8 28.72774 168.7193 0.170269 0 8715
R-squared 0.758063 Mean dependent var 63.07489
Adjusted R-squared 0.080638 S.D dependentvar 99.62248
S.E. of regression 95.52139 Akalke info criterion 12.07028
Sum squared resid 45621.68 Schwarz criterion 12.81708
Log likelihood -105.7028 Hannan-Quinn criter. 12.21607
F-statistic 1.119036 Durbin-Watson stat 2.216326
Prob(F-statistic) 0.488502
Fig. 1 - Homoscedasticity Test: White
To visualize the results, the graph is created (Fig. 2). It shows that the overall trend remains, however, some of predicted FDIs are very different from their statistical estimations.
80 70 60 50 40 30 20 10 0
FDI observed
FDI predicted
ii i i i i i i i i i i i i i i i i i
# ^ # ^ # ^ ^ # # ^ ^ Q<V ^ ^
Fig. 2 - Dynamics of FDI observed and FDI predicted by the model Overall, the relationship between foreign direct investment inflows in Russia and a range of economic factors in the period between 1996 and 2015 was
analyzed. Being trustworthy and adequate, the econometric model passed a number of tests. Calculations confirmed the hypothesis 2 and 7 and proved significance of total reserves and oil price for decision-making of foreign investors. Moreover, gross domestic models in the previous period and R&D expenditure variables were also included in the model showing negative correlation with the endogenous variable - FDI.
But at the same time, the model requires further study as significant role of the institutional factors were not taken into consideration. Firstly, well-developed institutions contribute to the growth of productivity of factors in the country-recipient. Secondly, operation of poor-developed institutions can lead to additional costs for the investors (for example, bribes in case of corruption). Thirdly, the FDI are particularly vulnerable to all the forms of uncertainty in the host country. Thus, the institutional factors can explain the dynamics of FDI flows as well as economic determinants, especially in developing economies, e.g. the Russian Federation.
The list of reference:
1. Average annual OPEC crude oil price from 1960 to 2017 (in U.S. dollars per barrel) // Statista URL: https://www.statista.com/ (дата обращения: 27.02.2017).
2. DataBank. World Development Indicators // The world bank URL: http://databank.worldbank.org/data/home.aspx (дата обращения: 27.02.2017).
3. Foreign direct investments in Russia sharply decrease // BBC URL: http://www.bbc.com/russian/news/2016/05/160527_direct_investments_russia (access date: 25.02.2017).
4. Suslov M.Yu.E., Tregub I.V. Ordinary least squares and currency exchange rate // International Scientific Review. 2015. № 2 (3). С. 33-36.
5. Tregub I.V. The capital market model on the example of Norway // в сборнике: forum for research in empirical international trade сан Рафаэль, 2015.
УДК 330.4
Orlov Ph.P. First-year master student International Finance Faculty Financial University under the Government of Russian Federation
Russia, Moscow 2017
THE USE OF ECONOMETRIC METHODS IN THE CONSTRUCTING OF MATHEMATICAL SYSTEMS FOR DETECTING «DUTCH DISEASE» IN ECONOMY. EVIDENCE FROM CANADA
Key words:
Dutch disease, Canada, GDP, regression, export, crude oil, natural gas, energy, oil prices, natural resources, industry, exchange rates, economics, econometrics.
