Zvyagintseva O. S., Chernikova L. I.

Stavropol state agrarian University

Prediction of the regional economic growth

The development potential of the region is realized in the future in the form of investment activity and the overall regional growth, but this realization occurs with a certain time lag. Thus, considering the potential development of the region in the current period, expected changes in regional development are possible only after a certain time.

Therefore, there is need to establish a causal relationship between the development potential and key indicators of regional development (investments in main capital per capita and gross regional product per capita). The potential development is the cause of the inflows of investments into the region and the growth of gross regional product. The article proposes a method of establishing the relationship between the development potential of the region and the main indicators of regional development, the main stages of which are shown in Figure 1.

time lag

Figure 1 - Diagram analysis of the relationship changes in the development potential of the region and regional development factors

Correctly pick function connecting two arrays of data, allows regression analysis, in which the determined nature of the relationship between the levels of development potential of the region and investment in fixed capital (gross regional product).

But total investment per capita in main capital and gross regional product by themselves insufficiently characterize the level of investment activity and the overall regional growth. In contrast, volume indicators, tempo indicator is subject to little inter-regional differences in the specialization of the regional economy. Therefore, the integration of volume and growth rates of per capita indicators supplement each other, allowing you to get a comprehensive assessment of regional development. As a result, the studied parameters are normalized (standardized) that enables regional comparisons further by the formula:

, (1)

where Xi - measure of the level of socio-economic development of the i-th region; - The average value of the index, which characterizes the level of socio-economic development of the region (in Russian or macro-region); TH p - growth rate of indicators characterizing the level of social and economic development of the region; - The average growth rate of indicators characterizing the level of social and economic development of the region (in Russian or macro-region).

The critical level of Crr is a unit. If the value of the index <1, the level of socio-economic development of the region below the average level (taken as a basis for calculation). If the value of Crr> 1 opposite the level of economic development in the region above the average, and its growth rate is significant.

The results of calculation of the indicators of regional development are presented in Figures 2 and 3. In this way, you can see a clear distinction between regions in terms of socio-economic development on the basis of the computed criteria.

Figure 2 - The dynamics of the development of the regions of Southern Russia (based on investments in fixed capital per capita)

In considering the first criterion clear leaders are Krasnodar and Astrakhan region. Other regions have a level of coefficient within the unit (with some exceptions). According to the second criterion the spread of the regions is several other - are clearly distinguished leading regions (Krasnodar, Volgograd region, Rostov region, Astrakhan and Stavropol Territory)

Figure 3 - The dynamics of socio-economic development of the regions of Southern Russia (based on GDP per capita)

The value is calculated growth rate is greater than one. The lowest (but not critical) levels have a criterion Republic of Ingushetia and the Chechen Republic. This circumstance is due to a significant backlog level of GDP per capita compared to the average level of the test macro.

Selection of a theoretical equation of the relationship-building factor in the region and regional development was carried out taking into account the time lag (1 to 3). The most accurate and adequate models for each time lag selected based on three indicators: the correlation coefficient, the approximation error and the F-test.

A detailed analysis of the regression (based on investments in fixed capital) led to the following conclusions:

1. The maximum correlation coefficients,allowing talk about the close relationship between the factor x (potential for the development of regional socio-economic systems) and the function y (a factor of regional development (based on investments in fixed capital per capita)) allow to opt for models with built a lag of one year - for the Volgograd region (0.655), the Krasnodar Territory (0.779), the Republic of Ingushetia (0.574) and the Republic of Dagestan (0,809); with a lag of two years - for the Rostov region (0.785), the Chechen Republic (0.734), the Republic of North Ossetia-Alania (0,399); with a lag of three years - for the Republic of Kalmykia (0.760), the Astrakhan region (0.690), Stavropol Territory (0.660), Kabardino-Balkaria Republic (0.860), the Karachai-Cherkess Republic (0,777) and the Republic of Adygea (0.833).

2. The error of approximation is acceptable to all constructed models on temporary lags.

3. F-criterion leads to the conclusion that all the constructed models with a lag of three years can not be used to predict, because the quality of these models has not been confirmed by comparison with the critical levels of the index = 6.61 significance level f1 = 1 and f2 = 4. The exception is one region - Kabardino-Balkar Republic. In other regression models the actual levels of the indicator matches to critical levels (with two-year time lag = 5.99 and a time lag of one year = 5.59). In such a situation there is a need for four of the studied regions to look for features that best describe investigated the relationship of the development potential of regional socio-economic systems by a factor of regional development (based on investments in fixed capital per capita). Such models are selected by the correlation coefficient. As a result, they lag impact of the development potential of two years.

The final selected models the relationship between the development potential of regional socio-economic systems and the coefficient of regional development taking into account the time lag c satisfy all the conditions of their accuracy, relevance and adequacy.

Thus, as a result of the analysis of the relationship and potential regional growth factors, the following theoretical equation (tab. 1).

Table 1 - Results of construction of single-factor regression model, the relationship of the development potential of the region and factors of regional growth in Southern Russia

Name of region	based GDP	based investments in the fixed capital
VO	Yt = 1,284 + 0,008 ∙ xt-1	Yt = 1,373 – 0,201 ∙ xt-1
RO	Yt = 1,018 + 0,059 ∙ xt-1	Yt = 1,019 + 0,060 ∙ xt-1
KK	Yt = 1,143 + 0,515 / xt-1	Yt = 2,248 – 0,323 ∙ xt-1
CHR	Yt = 0,855 – 0,057 / xt-1	Yt = 0,819 - 0,030 / xt-1
RSOA	Yt = 0,839 + 0,072 ∙ xt-1	Yt = 0.,839 + 0.073 ∙ xt-1
KCHR	Yt = 1,108 ∙ 0,867 ^x^t-1	Yt = 0,898 + 0,112 / xt-1
RK	Yt = 0,835 + 0,060 / xt-1	Yt = 0,835 + 0,060 / xt-1
KBR	Yt = 1,222 – 0,225 ∙ xt-1	Yt = 1,223 – 0,225 ∙ xt-1
RI	Yt = 0,919 – 0,301 ∙ xt-1	Yt = -0,992 + 1,30 / xt-1
RD	Yt = 0,722 + 0,076 ∙ xt-1	Yt = 0,139 + 0.313 ∙ xt-1
AO	Yt = 1,463 – 0,352 ∙ xt-1	Yt = 0,224 + 3,866 ∙ xt-1
SK	Yt = 1,276 – 0,102 ∙ xt-1	Yt = 1,277 - 0,102 ∙ xt-1
RA	Yt = 0,722 + 0,076 ∙ xt-1	Yt = 0,829 + 0,11 / xt-1

As a result of the constructing of the presented models in the Table the relationship of development potential of the region and the most important indicators of regional development confirmed their relationship. Moreover, this provision is valid for both current and for future periods.

The use of this technique makes it possible to measure the speed of perception of the existing potential of the regional economy as a whole, which is defined as the reciprocal of the period of performance of the local index of regional development. The grouping of regions of the South of Russia on the development potential of this relationship, and the speed of its perception of the Territory's economy is presented in the table number 2.

Table 2 - The grouping of regions of Southern Russia at the speed of perception of the development potential of the regional economy as a whole

The grouping of regions by the current development potential	for investments in fixed capital per capita			GDP per capita
	speed of perception			speed of perception
	1	0,5	0,33	1	0,5	0,33
High	VO, КК,	RO, SK	-	VO, КК,	RO, SK	-
Average	RD	RSOA, AO, RA	KBR	RD, AO, RA	RSOA	KBR
Low	RI	CHR, KCHR, RK	-	CHR, RI, KCHR	RK	-

The resulting regression models are built taking into account the time lag, allow to determine on the basis of pre-existing forecasts, strategic development potential as the level of regional development in the long term (Table. 3).

As a result, the authors determined that in five regions of the South of Russia in the forecast period, the growth rate of investment in fixed capital slowed down, due to insufficient investment.

In turn, this fact is explained by a low level of capabilities in the region and significant restrictions on their development. As a result of those regions in the short term will not be able to provide high levels of socio-economic development. In other regions the growth rate of investment in fixed capital in the forecast period are increased (with the exception of the Volgograd Region, Rostov and Astrakhan region).

Table 3 - Results of the forecast factors of regional growth

Name of of the region	The coefficient the regional growth
	based on investments in the fixed capital		based on GRP
	prognosis		prognosis
	2011	2012	2011	2012
VO	0,933	0,932	1,301	1,302
rate of growth, %	109,92	109,80	106,08	106,08
RO	1,154	1,153	1,151	1,150
rate of growth, %	117,87	117,80	99,45	99,39
КК	1,372	1,375	1,333	1,334
rate of growth, %	100,94	101,20	100,05	100,11
CHR	0,773	0,775	0,768	0,772
rate of growth, %	90,58	90,82	101,06	101,57
RSOA	0,744	0,748	0,936	0,933
rate of growth, %	95,30	95,80	97,26	96,89
KCHR	0,724	0,723	0,946	0,947
rate of growth, %	92,30	92,10	99,04	99,18
RK	0,910	0,914	0,910	0,914
rate of growth, %	103,01	103,54	98,32	98,82
KBR	0,603	0,605	0,919	0,922
rate of growth, %	91,97	92,26	104,89	105,23
RI	0,831	0,855	0,704	0,707
rate of growth, %	119,64	123,00	101,96	102,36
RD	0,797	0,800	0,882	0,883
rate of growth, %	82,67	82,99	89,25	89,33
AO	1,295	1,291	0,989	1,002
rate of growth, %	103,20	102,86	85,56	86,66
SK	0,918	0,919	1,088	1,089
rate of growth, %	107,80	107,90	107,41	107,51
RA	0,937	0,940	0,799	0,797
rate of growth, %	107,47	107,81	82,46	82,25

Forecast of Regional Development of GDP per capita also allows you to split the region into two groups: developing in accordance with the high levels of existing capacity and not able to normal development, due to the dominance of the limitations in the development of the existing opportunities.

Thus, in the process of research was proved the presence of the dependence between the development potential of the region and regional development indicators such as fixed investment and GDP per capita. The resulting production functions fully approximated the original data of the entire range of changes.

Literature:

1. Nazarenko A., Zaporozhets D., Sergienko E. Features of innovative activity motivation in enterprise structure / A. Nazarenko, D. Zaporozhets, E. Sergienko // In the collection:Social and economic innovations: trends, forecasts and perspectives conference proceedings of the Ist International Conference. Russian State Social University (Stavropol branch); under the editorship of PhD, associate professor O. Yu. Kolosova, PhD, senior lecturer K. V. Bagmet, assistent K. A. Andikaeva. 2015. С. 160-164.

2. Eremenko N.V., Sidorova D.V., Orel J.V. Historical and geographical region investment potential Stavropol RF / N.V. Eremenko, D.V. Sidorova., J.V. Orel // Scientific works SWorld. 2015. Т. 18. № 1 (38). С. 69-71.

3. Bannikova N.V., Baydakov A.N., Vaytsekhovskaya S. S. Identification of strategic alternatives in agribusiness / N.V. Bannikova, A.N Baydakov., S.S. Vaytsekhovskaya Modern Applied Science. 2015. Т. 9. № 4. С. 344-353.