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16SENTENTIA. European Journal of Humanities and Social Sciences
Правильная ссылка на статью:
Ozarnov R. — The gravity model of the EAEU, SCO and BRICS countries. // SENTENTIA. European Journal of Humanities and Social Sciences. - 2021. - № 2. DOI: 10.25136/1339-3057.2021.2.33964 URL: https ://nbpublish.com'library_read_article.php?id=33964
The gravity model of the EAEU, SCO and BRICS countries. / Модель международной торговли по типу взаимного притяжения стран ЕАЭС, ШОС и БРИКС
Озарнов Руслан Владиславович
Департамент мировой экономики и мировых финансов, Финансовый университет при Правительстве
РФ, г. Москва, РФ
125993, Россия, г. Москва, Ленинградский проспект, 49, Департамент мировой экономики и мировых
финансов
Статья из рубрики "Economics"
DOI:
10.25136/1339-3057.2021.2.33964
Дата направления статьи в редакцию:
24-09-2020
Аннотация: Статья посвящена исследованию факторов, влияющих на финансово-экономическое сотрудничество стран с развивающимся рынком в рамках трех блоков, а именно ЕАЭС, БРИКС и ШОС, посредством построения модели по типу взаимного притяжения. Отдельно рассматривается принадлежность стран к бывшему СССР. Установлен временной период исследования, который начинается в кризисном для России 2014 году и продолжается до настоящего времени. Предметом анализа являются финансово-экономические отношения, возникающие в процессе сотрудничества стран с развивающимся рынком, входящих в такие объединения как ЕАЭС, ШОС и БРИКС. Автор рассматривает факторы, оказывающие воздействие на финансово-экономическое сотрудничество стран с развивающимся рынком: глубина и масштабы внешнеторгового оборота, валовый внутренний продукт на душу населения, индекс торговой открытости, темп роста курса валюты страны-импортера и страны-экспортера, расстояние между странами, причастность страны с развивающимся рынком к таким объединениям как ЕАЭС, БРИКС, ШОС. Исследование основано на общенаучных методах познания (анализ, синтез, сравнение), представлении табличной и графической интерпретации статистической информации, временных рядов, эконометрическом моделировании с использованием программного продукта EViews. Новизна статьи заключается в определении, с применением эконометрических методов, факторов, влияющих на финансово-экономическое сотрудничество стран с развивающимся рынком в рамках трех блоков, а именно ЕАЭС, БРИКС и ШОС, посредством построения модели по типу
взаимного притяжения. Особенность представленной модели заключается в наличии лаговых обменных курсов. Включение лага относительного изменения курса валюты экспортера привело к тому, что данные за 2014 год оказались исключенными из выборки. Целесообразно учитывать полученные результаты при сотрудничестве России как на двусторонней основе, так и в многостороннем формате в рамках ЕАЭС, БРИКС и ШОС.
Ключевые слова: международные финансы, международная торговля, мировая экономика, международные экономические отношения, гравитационная модель, российско-китайское сотрудничество, эконометрика, ЕАЭС, БРИКС, ШОС
INTRODUCTION
The research of financial and economic cooperation among emerging countries being the members of such blocks as the EAEU, SCO, and BRICS, as well as determining the factors influencing the given cooperation, with the aim of further strengthening and expanding financial and economic relations among the countries in question is paramount in terms of increasing imbalances in the world economy and world finance, along with the protectionist policy of the largest world economies, the growth of geopolitical and economic contradictions, the use of sanctions policy, it is relevant to.
The gravity model demonstrates the way the ties among countries influence the volume and quality of mutual trade. In the literature, this model is found quite frequently as the study of the character and the degree of influence allows to adjust international policy. The directions largely favouring the improvement of mutual trade relationships are distinguished. Thus, the efficiency of the international production system and world GDP growth.
The gravity model allows setting the volume of trade in the lack of restrictions. Comparison of trade volumes predicted by the model with actual trade volumes makes it possible to determine the trade changes which are likely to take place if the restrictions are removed. Gravity models can be used both for analyzing the bilateral trade and the trade of the country in question with all countries at large, which essentially represents the sum of
bilateral trade flows. ^
If it is politically necessary to exert a certain influence on trading partners, for example, a sanctions policy, this model allows one to calculate the distant financial and economic prospects of restrictive measures and find weak points. This makes it possible to increase the effectiveness of restrictive policies.
The connections being included in gravity models can be divided into three main groups:
1) location, geography, boundaries;
2) economic production, raw materials and human potential, economic development rates;
3) features of the management system, political system, block membership.
METHODS
The author has applied econometric data analysis using the EViews software as the main research method. The author uses statistical data of the World Bank Groupé, the
International Trade Center ^^ and the "FINAM" investment company.-8 Besides, the author makes use of general scientific methods of cognition such as analysis, synthesis, comparison, presentation of tabular and graphical interpretation of statistical information and time series.
MAIN PART
The main purpose of the given research is to build up the gravity model for trade partners of the Russian Federation within three blocks, namely the EAEU, BRICS and SCO. The countries' belonging to the former USSR is considered separately. It should be noted that a strategic partnership has been established between Russia and China, and China's economic strategy presupposes a global foreign economic offensive with elements of trade expansion
to foreign markets. The research data starts with the crisis in Russia of 2014 and proceeds up to now. Table 1 provides a list of countries belonging to the EAEU, SCO and BRICS, as well as the ISO two-letter international classification codes for these countries. The countries of associations that were part of the former USSR are enumerated separately.
Table 1. List of countries united in blocks EAEU, SCO and BRICS and their belonging to the former USSR.
Countries CodesISO-2 Countries CodesISO-2
EAEU BRICS
Armenia AM Brazil BR
Belarus BY Russia RU
Kazakhstan KZ India IN
Russia RU China CN
SCO South Africa ZA
India IN USSR
Kazakhstan KZ Armenia AM
Kyrgyzstan KG Belarus BY
China CN Kazakhstan KZ
Pakistan PK Kyrgyzstan KG
Russia RU Russia RU
Tajikistan TJ Tajikistan TJ
Uzbekistan UZ Uzbekistan UZ
Table 2 presents a list of the countries participating in the sample, as well as their main economic indicators in 2018. Despite the eightfold excess of China's GDP over that of the Russian Federation, the trade surplus of the Russian Federation is more by 60 billion dollars than that of China.
Table 2. List of the countries participating in the sample, as well as their main economic indicators in 2018.
Source: Authors' calculations based on the World Bank data
Страны ISO2 GDP, billion USD GDP per capita,USD Export, billion USD Import,billion USD Balance, billion USD
1 Russia RU 1657,6 11729,1 509,6 344,3 165,3
2 Brazil BR 1868,6 11026,2 276,7 266,8 9,9
3 China CN 13608 2 7755 0 2655 6 2 549 0 1 06 6
..... ■ * " ' ■ ^ f — ' f ^ ' -' — f ^ — v v f v
4 India IN 2726,3 2104,2 537,0 638,8 -101,8
5 South Africa ZA 368,3 7439,9 110,1 108,9 1,2
6 Armenia AM 12,4 4406,7 4,7 6,6 -1,9
7 Belarus BY 59,7 6744,5 41,9 41,3 0,6
8 Kazakhstan KZ 170,5 11165,5 56,0* 42,8* 13,2
9 Kyrgyzstan KG 8,1 1087,2 2,6 5,5 -2,9
10 Pakistan PK 312,6 1196,6 26,7 60,8 -34,1
11 Tajikistan TJ 7,5 1073,0 1,1* 2,9* -1,8
12 Uzbekistan UZ 50,5 2026,5 14,7 19,6 -4,9
* Export and import data of 2018 For Kazakhstan and Tajikistan are not available, so the data of 2017 are provided.
Export-import relationship of the Russian Federation
within the listed blocks
Table 3 provides the data on the trade openness of the economies of the countries listed in Table 2 for 2014 - 2018. The trade openness index is calculated using the formula:
= jm^P, ^
GVPi , (1)
where i is the index of the country, the correspondence of the indices and countries is indicated in Table 2;
EXPi - gross export of the country;
IMPi is the country's gross import;
GDPi is the country's gross domestic product.
The high economic openness of Belarus and Kyrgyzstan, where the total value of exports and imports exceeds GDP should be noted. To the greatest extent, the trade openness index decreased among the groups of the SCO and BRICS countries. These groups comprise China, whose trade activity index has decreased by 15% over 5 years. Since China's exports and imports have not declined over the period, this drop in trade activity is explained by increased domestic consumption. In the Russian Federation, the index of trading activity increased by 7.8%. In fact, the trade among the former USSR countries had remained unchanged over the given period.
Table 3. Countries trade openness index of trade partners of the Russian Federation and amalgamation with the participation of the Russian Federation.
Source: Authors' calculations based on the World Bank data
i Countries Trade openness index, %
2014 2015 2016 2017 2018 Growth rate, %
1 Russia 47,78 49,35 46,30 46,76 51,51 7,81
2 Brazil 24,69 26,95 24,54 24,14 29,08 17,78
3 China 45,07 39,63 37,21 38,15 38,25 -15,13
4 India 48,92 41,92 40,16 40,77 43,13 -11,84
5 South Africa 64,43 61,62 60,64 57,97 59,47 -7,70
6 A rm enia 75 61 71 59 75 92 86 83 90 40 1 9 56
7 Byelorussia 110,65 115,91 125,21 133,37 139,34 25,93
8 Kazakhstan 64,97 53,05 60,31 60,62 0,51*
9 Kyrgyzstan 125,13 110,96 105,82 100,62 101,12 -19,19
10 Pakistan 30,90 27,65 25,31 25,79 27,97 -9,48
11 Tajikistan 54,61 52,73 54,97 56,64 3,04*
12 Uzbekistan 35,95 30,44 29,75 45,68 67,85 88,73
EAEU 51,61 52,25 50,36 50,89 49,90 -3,31
SCO 46,02 40,77 38,41 39,42 39,72 -13,69
BRICS 43,35 39,76 37,37 38,08 39,53 -8,81
USSR 51,36 51,43 49,55 50,95 50,38 -1,91
* Export and import data of 2018 For Kazakhstan and Tajikistan are not available, so the data for 2014 - 2017 are provided.
In 2014, the Russian Federation underwent the shock of sanctions. As a result, the process of reorientation of the Russian economy towards eastern markets started. Table 4 illustrates this process as well as demonstrates the export volume of Russian GDP with the listed trade partners and amalgamations in Table 2. The growth leaders are Pakistan, Armenia, and India.
Table 4. Export volume of Russian GDP with the given trade partners and amalgamations.
Source: Authors' calculations based on the World Bank data
Russian trade partners Export as a percent of Russian GDP, %
2014 2015 2016 2017 2018 Growth rate
Brazil 0,111 0,141 0,139 0,129 0,156 40,28
China 1,816 2,078 2,185 2,377 3,381 86,15
India 0,213 0,334 0,414 0,344 0,468 119,18
South Africa 0,014 0,020 0,015 0,013 0,017 24,29
Armenia 0,026 0,037 0,075 0,055 0,081 211,69
Byelorussia 0,803 0,911 1,095 0,984 1,316 63,95
Kazakhstan 0,673 0,755 0,735 0,755 0,780 15,86
Kyrgyzstan 0,084 0,095 0,080 0,088 0,099 16,97
Pakistan 0,007 0,007 0,010 0,016 0,025 263,80
Tajikistan 0,043 0,056 0,052 0,044 0,051 18,51
Uzbekistan 0,151 0,163 0,153 0,166 0,200 32,55
EAEU 1,502 1,704 1,905 1,795 2,177 44,96
SCO 2,988 3,487 3,629 3,790 5,004 67,45
BRICS 2,155 2,573 2,753 2,862 4,022 86,65
USSR 1,781 2,017 2,190 2,092 2,527 41,94
Fig. 1 shows the way the share of exports with amalgamations of countries in the Russian Federation grows. Russia has the largest volume of the share of exports with the SCO countries. as well as the highest growth rates are detected with the BRICS countries.
5.2-, 4 84.44.0 -3.63.22.8 -2.42.0 -1.6-
mi-t-t-t-t-
2014 2015 2016 2017 2018
- EAEU -SCO
- BRICS -USSR
Figure 1. Export as a percent of Russian GDP in trade with given amalgamations, %
The gravity model of the given countries in the post-crisis period
for the Russian Federation
Walter Isard applied the model for the first time in 1954. It may be called a theoretical
model at the primary level for the purposes of trade between a couple of countries. Then
the model was used by Tinbergen in 1962 t18, and Poyhonen in 1963 ^ based on the notion that bilateral trade flows between two countries directly depends on national
incomes and indirectly links with bilateral distance -t^. This model of the relationship between bilateral trade, a country's economic position, and distance can help analyze bilateral or multilateral economic integration between two or more countries.
There are various options for gravity models, in which the variables are used indicators such as the population, the area of countries, the length of the border, as well as dummy variables responsible for social and political, climatic and other differences. Thus, gravity models determine the dependence of a unidirectional foreign trade flow on the parameters
of the internal economic state as a country exporter and importing country. ^
The influence of these factors is estimated on the basis of the data on the actual size of trade among countries using regression analysis. The obtained parameters of the model are elastic and demonstrate to which percentage the trade among countries can increase if the corresponding factor rises by 1%. Usually, this model is represented either in a power-law or in a linear-logarithmic form.
To conduct the research, bilateral models of trade integration between Russia and China both in multilateral and bilateral formats, for the period from 2014 to 2018 should be analyzed by using panel data that make up the basis of the gravity model. In doing this, it should be noted that the contribution of the BRICS countries to world economic growth over the past decade has exceeded almost 50%. According to many forecasts, the economic performance of these countries and the growth rates will be higher than in developed countries and other emerging economies by 2030 - 2050.
The model performed in the given research is as follows:
+ß3 hi{CAPDISTJ + ßgEAESi + ßl0SHOSi + ß^BRICSi ++ßl2 USSRi + ai} + u,
■in, (2)
where EXPORTijt - exports from country i to country j for year t;
GDPit and GDPjt - GDP of the exporting country i and the importing country j for year t;
GDPPCit and GDPPCjt - GDP per capita of the exporting country i and importing country j for year t;
TOit and TOjt - trade openness index of the exporting country i and the importing country j for year t;
DEXRit and DEXRjt - the growth rate of the exchange rate of the importing country i and the exporting country j;
CAPDISTij - distance between the capitals of the importing country j and the exporting country i;
EAESi, SHOSi, BRICSi, USSRi - variables that take on the value 1 if the exporting country is included in the corresponding amalgamation, and 0 if the country is not included in this amalgamation;
(y
- random effects among countries; uijt - random errors of the model.
The second column of Table 5 gives the estimates of the parameters that fully correspond to equation (2). A detailed description of the characteristics of the performed model is in Appendix 1. The coefficient p_1 in equation (2) indicates the elasticity of exports to changes in GDP of both the exporter and the importer. The value of p_1 close to 1 is obtained.
The coefficients p_2 and p_3 show the way the per capita income of the exporting and importing countries influences on the value of exports. The obtained values indicate that with an increase in per capita income of an exporter by a thousand dollars, exports increase by 16.5%. If the per capita income of the importer increases by a thousand dollars, then exports to this country increase by 10.5%.
The trade openness index also positively correlates with the exports value. One additional percentage point of exporter's trade openness provides an average of 1.17% growth in exports. One additional percentage point of importer's trade openness increases exports to the country by 1.87% on average.
The coefficients p_6 and p_7 in equation (2) demonstrate the way the relative change in the exchange rate of the national currency against the dollar affects the export of a given country. One percent gain in the currency of the importing country increases exports to that country by 0.41%. In fact, this means that the weakening of the dollar of a trading partner increases exports to this country. It is likely to be related to the increase of effective demand. As for the exporter, the strengthening of the national currency by 1% causes the growth of exports by 0.51% with a lag of one period.
Increasing the distance among countries predictably reduces the amount of exports among them. The name of the model, i.e. the gravity model is valid. The value of the coefficient P_8 = - 2.27 indicates that an increase in the distance to the capital of a trading partner's country by 1% results in a decrease in exports to this country by 2.27%.
Coefficients p_ (9-12) present the way the belonging to definite amalgamation influences the export value. Membership in the EAEU did not show a considerable impact on the export of the member country of this amalgamation. Belonging to BRICS demonstrates a noticeable increase in exports equal to 167.2%. This is explained by the fact that the countries with significant GDP and with large global export opportunities belong to the BRICS. Belonging to the former USSR shows an increase of exports by 85.3%.
Table 5. Equation (2) parameters estimates.
ependent Variable: OG(EXPORT_IJT) ßklag DEXR_IT lag DEXR_IT without Russia ßklag DEXR_IT without China ßk sample from 2014 k
OG(GDP_IT*GDP_JT) 1.048378*** 1.083114*** 1.051590*** 0.904208*** 1
DPPC_IT 0.000165^ 0.000153* 0.000188** 0.000173** 2
DPPC_JT 0.000105*** 0.000106* 0.000130** 0.0000952* 3
O_IT 0.011684*** 0.010934** 0.010548** 0.013988*** 4
O_JT 0.018690*** 0.019598*** 0.019924*** 0.013197*** 5
EXR_IT -0.001116
EXR_IT(-1) 0.005050*** 0.006290** 0.005477** 6
EXR_JT 0.004144*** 0.004293* 0.003930* 0.004509** 7
OG(CAPDIST_IJ) 2.272103*** 2.317837*** 2.324079*** 2.096904*** 8
AES_I -0.094825 -0.103772 -0.294821 0.184009 9
HOS_I -0.681953* -0.803031 -0.981422* -0.203052 10
RICS_I 1.671805** 1.676277* 1.415229** 1.887951*** 11
SSR_I 0.853088** 0.957536 1.126112* 0.365977 12
29.27515*** 30.65195*** 29.19053*** 23.17843*** 0
2 0.386655 0.360223 0.375565 0.319733
W 1.805328 1.816908 1.819800 1.670782
bs 486 444 444 618
ross 132 121 121 132
eriods 4 4 4 5
*** significance level less than 1%; ** significance level is less than 5%; * the level of significance is less than 10%; ^ one-sided significance level less than 10%.
In order to demonstrate the stability of the obtained parameter estimates to data changes, the third, fourth and fifth columns of Table 5 were calculated. Russia is a member of all amalgamations the trade relationships of which are studied in the given research. In the case of instability of the performed model to a change in the initial data, the exclusion of Russia in estimating the parameters of equation (2) should cause a change in the estimates of the parameters.
In the third column of Table. 5, the parameters of equation (2) in which the Russian
Federation is excluded from the list of countries are calculated. Parameter estimates that assess the impact of the SCO and the USSR, where the Russian Federation is a key player, become insignificant. Estimates of the remaining parameters are changed insignificantly.
The fourth column gives estimates of the parameters of equation (2) in which China is excluded from the list of countries. China is the largest economy in the world, in terms of GDP, it noticeably exceeds the rest of the participants of the research in question. All parameter estimates changed insignificantly compared to estimates obtained for the entire sample.
The inclusion of a lag in the relative change in the exporter's exchange rate resulted in the data of 2014 being excluded from the sample. The fifth column of Table. 5 provides the estimates of the model parameters comprising the present (at time t) value of the relative change in the exchange rate of the exporter's currency. The estimates of the parameters that do not concern the amalgamations have remained practically unchanged. It is the BRICS that has a significant influence among the amalgamations.
CONCLUSION
The author comes to the following conclusions on the basis of the conducted research. In the case of an increase in per capita income of an exporter by a thousand dollars, exports increase by 16.5%. If the per capita income of the importer increases by a thousand dollars, then exports to this country increase by 10.5%. Since at present per capita income in China is increasing, the given fact should be taken into account in order to expand foreign trade cooperation.
The trade openness index presented in the model is positively correlated with the export value. One additional percentage point of exporter's trade openness provides an average of 1.17% growth in exports. One additional percentage point of importer's trade openness increases exports to the country by 1.87% on average. This result confirms the conclusion about the reorientation of the Russian Federation to Asian markets, in particular, to the Chinese market and an increase in Russian exports to China.
The relative change in the exchange rate of the national currency against the dollar influences the export of a given country. One percent gain in the currency of the importing country increases exports to the country by 0.41%. In fact, it means that the weakening of the dollar of a trading partner increases exports to this country. It is likely to be related to an increase of effective demand. As for the exporter, the strengthening of the national currency by 1% causes the growth of exports by 0.51% with a lag of one period.
Increasing the distance among countries predictably reduces the amount of exports among them. Thus, the mutual attraction is weakened. According to the results of the given model, an increase in the distance to the capital of a trading partner country by 1% results in a decrease of exports to the country by 2.27%.
Membership in the EAEU did not show a considerable impact on the export of the member country of this amalgamation. Belonging to BRICS demonstrates a noticeable increase in exports equal to 167.2%. This is explained by the fact that the countries with significant GDP and with large global export opportunities belong to the BRICS.
To create a sustainable strategic financial and economic cooperation, a strategy of soft balancing and the establishment of beneficial bilateral economic ties are desirable. At the same time, soft balancing implies recognition of the geo-economics leadership of a less
dependent state while preventing its hegemony. The given conditions can be implemented through diversification of the partnership, which will promote the benefits of this partnership to its members due to economies of scale; developing institutions for collective bargaining advantages and the creation of multilateral organizations that also include other advanced economies to maintain the internal balance.
Thus, one should take into account the obtained results of the gravity model parameters in Russia's cooperation both on a bilateral basis and in a multilateral format within the EAEU, BRICS, and SCO.
ACKNOWLEDGMENTS
The article was prepared under the scientific supervision of Viktor Y. Pishchik, Doctor of Economics, Professor, Scientific Advisor of the Department of World Finance of Financial University under the Government of the Russian Federation.
Appendix 1.
Detailed description of the performed model characteristics
Dependent Variable: LOG(EXPORT_IJT)
Method: Panel EGLS (Cross-section random effects)
Sample (adjusted): 1915 1918
Periods included: 4
Cross-sections included: 132
Total panel (unbalanced) observations: 486
Swamy and Arora estimator of component variances
White cross-section standard errors & covariance (d.f. corrected)
Variable Coefficient Std. Error t-Statistic Prob.
LOG(GDP_IT*GDP_JT) 1.048378 0.084805 12.36225 0.0000
GDPPC_IT 0.000165 0.000113 1.456157 0.1460
GDPPC_JT 0.000105 2.59E-05 4.045995 0.0001
TO_IT 0.011684 0.004512 2.589344 0.0099
TO_JT 0.018690 0.002785 6.711053 0.0000
DEXR_IT(-1) 0.005050 0.001182 4.272918 0.0000
DEXR_JT 0.004144 0.001115 3.717389 0.0002
LOG(CAPDIST_IJ) -2.272103 0.269394 -8.434137 0.0000
EAES_I -0.094825 0.717991 -0.132071 0.8950
SHOS_I -0.681953 0.407836 -1.672125 0.0952
BRICS_I 1.671805 0.709031 2.357873 0.0188
USSR_I 0.853088 0.377309 2.260981 0.0242
C -29.27515 3.424975 -8.547552 0.0000
Effects Specification
S.D. Rho
Cross-section random 2.162925 0.8961
Idiosyncratic random 0.736510 0.1039
Weighted Statistics
R-squared 0.386655 Mean dependent var 1.891011
Adjusted R-squared 0.371094 S.D. dependent var 0.927611
S.E. of regression 0.738560 Sum squared resid 258.0076
-1/1 O/IO/H Durbin Wat._______ ^ o n c i n o
r-SLdLISLIL 24. 84841 Duibni-vv atson stat 1 .805328
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.681403 Mean dependent var 10.87668
Sum squared resid 2386.813 Durbin-Watson stat 0.195151
Dependent Variable: LOG(EXPORT_IJT)
Method: Panel EGLS (Cross-section random effects)
Sample: 1914 1918 IF I<>"RU"
Periods included: 4
Cross-sections included: 121
Total panel (unbalanced) observations: 444
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
LOG(GDP_IT*GDP_JT) 1.083114 0.110056 9.841514 0.0000
GDPPC_IT 0.000153 8.37E-05 1.822996 0.0690
GDPPC_JT 0.000106 6.28E-05 1.684406 0.0928
TO_IT 0.010934 0.005526 1.978537 0.0485
TO_JT 0.019598 0.005431 3.608552 0.0003
DEXR_IT(-1) 0.006290 0.002960 2.125080 0.0341
DEXR_JT 0.004293 0.002266 1.894671 0.0588
LOG(CAPDIST_IJ) -2.317837 0.278519 -8.322012 0.0000
EAES_I -0.103772 0.807731 -0.128474 0.8978
SHOS_I -0.803031 0.634169 -1.266273 0.2061
BRICS_I 1.676277 0.935376 1.792088 0.0738
USSR_I 0.957536 0.866671 1.104843 0.2698
C -30.65195 5.609907 -5.463897 0.0000
Effects Specification
S.D. Rho
Cross-section random 2.227902 0.8938
Idiosyncratic random 0.768131 0.1062
Weighted Statistics
R-squared 0.360223 Me a n de pe nde nt va r 1.855289
Adjusted R-squared 0.342411 S.D. dependent var 0.947696
S.E. of regression 0.770491 Sum squared resid 255.8659
F-statistic 20.22272 Durbin-Watson stat 1.816908
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.658152 Me a n de pe nde nt va r 10.52446
Sum squared resid 2304.674 Durbin-Watson stat 0.201714
Dependent Variable: LOG(EXPORT_IJT)
Method: Panel EGLS (Cross-section random effects)
Date: 01/08/20 Time: 12:58
Periods included: 4
Cross-sections included: 121
Total panel (unbalanced) observations: 444
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
1 OG(GDP IT*GDP IT) 1 051590 0 106248 9 897533 0 0000
—w w v 1 —" ■ — — ■ ■ J - 1 - ^ " v ~. - ~ v — . w ------
GDPPC_IT 0.000188 8.30E-05 2.268286 0.0238
GDPPC_JT 0.000130 6.06E-05 2.141705 0.0328
TO_IT 0.010548 0.005324 1.981017 0.0482
TO_JT 0.019924 0.005349 3.725101 0.0002
DEXR_IT(-1) 0.005477 0.002513 2.179073 0.0299
DEXR_JT 0.003930 0.002276 1.727061 0.0849
LOG(CAPDIST_IJ) -2.324079 0.264123 -8.799222 0.0000
EAES_I -0.294821 0.718499 -0.410329 0.6818
SHOS_I -0.981422 0.579839 -1.692578 0.0913
BRICS_I 1.415229 0.683943 2.069220 0.0391
USSR_I 1.126112 0.757419 1.486775 0.1378
C -29.19053 5.405518 -5.400136 0.0000
Effects Specification
S.D. Rho
Cross-section random 2.136618 0.8865
Idiosyncratic random 0.764544 0.1135
Weighted Statistics
R-squared 0.375565 Me a n de pe nde nt va r 1.918049
Adjusted R-squared 0.358179 S.D. dependent var 0.954998
S.E. of regression 0.767065 Sum squared resid 253.5958
F-statistic 21.60199 Durbin-Watson stat 1.819800
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.668344 Me a n de pe nde nt va r 10.49597
Sum squared resid 2134.737 Durbin-Watson stat 0.216183
Dependent Variable: LOG(EXPORT_IJT)
Method: Panel EGLS (Cross-section random effects)
Sample: 1914 1918
Periods included: 5
Cross-sections included: 132
Total panel (unbalanced) observations: 618
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
LOG(GDP_IT*GDP_JT) 0.904208 0.092099 9.817766 0.0000
GDPPC_IT 0.000173 7.31E-05 2.370733 0.0181
GDPPC_JT 9.52E-05 5.46E-05 1.744362 0.0816
TO_IT 0.013988 0.004377 3.195693 0.0015
TO_JT 0.013197 0.004268 3.092174 0.0021
DEXR_IT -0.001116 0.002076 -0.537604 0.5910
DEXR_JT 0.004509 0.002065 2.183437 0.0294
LOG(CAPDIST_IJ) -2.096904 0.257550 -8.141725 0.0000
EAES_I 0.184009 0.689100 0.267028 0.7895
SHOS_I -0.203052 0.539728 -0.376212 0.7069
BRICS_I 1.887951 0.658241 2.868175 0.0043
USSR_I 0.365977 0.730228 0.501182 0.6164
C -23.17843 4.728267 -4.902097 0.0000
Effects Specification
S-R- Rh«-
Cross-section random 2.166811 0.8932
Idiosyncratic random 0.749097 0.1068
Weighted Statistics
R-squared 0.319733 Me a n de pe nde nt va r 1.711373
Adjusted R-squared 0.306240 S.D. dependent var 0.907856
S.E. of regression 0.758878 Sum squared resid 348.4174
F-statistic 23.69638 Durbin-Watson stat 1.670782
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.668896 Me a n de pe nde nt va r 10.88485
Sum squared resid 3173.453 Durbin-Watson stat 0.183437
Библиография
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