ЕCONOMETRIC FORECAST OF AGRICULTURAL SECTOR INVESTING IN LVOV REGION

Purpose of economic processes forecasting in agriculture is more relevant and urgent in recent years with application of applied econometric methods. In represented research paper, these methods are used to forecast investment and the main agricultural industry indicators of Lvov region of Ukraine. The linear trend model, the parabolic trend model and the exponential trend model were elaborated from the period from 2000 to 2009 in this scientific study using applied statistical tool STATGRAFICS and EXCEL spreadsheets. And with assistance of these models forecast for investment on the basis of data of essential indicators of agrarian sector of the region for 2010 and 2011 was made. All models with probability р=0,95 are adequate experimental data for 2000-2009 years, that allow to make the forecast of investments and main agricultural indicators of the researched region by these models for 2010 and 2011 years. Nevertheless, it should be pointed out that, because of small amount of input data analysis of regression equations coefficients have more qualitative than quantitative influence upon resulting variable y6. КEY WORDS Investment; Agrarian sector; Forecasting; Linear trend model; Parabolic trend model; Exponential trend model. Accordingly to statistical data of agricultural sector of Lvov region and Ukraine provided by State Statistical Service of Ukraine for 2000-2009 years, presented in tables (1) in this applied research calculate main dynamics and trend models and calculate predicted values and their estimates for forthcoming two years with application of the applied statistical tool STATGRAFICS and EXCEL spreadsheets. To vital issues of investing processes in agriculture of Ukraine’s economy have dedicated one’s researches many prominent scientists, among them: A. Carita, I. Luyt, N. Santos et al. [1], K. Crane, F.S. Larrabee [2], K. Vitale [3] and others. In this research one’s attention is paid to study investment of agriculture of Ukraine and another developing economies with assistance of econometric methods, that are displayed in scientific works of: V. Yeleyko, О. Yeleyko, І. Кopych, R.. Bodnar, М. Demchyshyn, О. Synytskyy, А. Chemerys [4-7], M.I. Gómez, E.R. Gonzáles, L.F. Melo [8], F. Ruff [9], N. Carnot, V. Koen, B. Tissot [10], R.S. Mariano, Y.K. Tse [11] and R. Gupta, A. Kabundi [12]. Input data of the models: Table 1  Dynamics of agricultural sector main indicators of Lvov region Year y6 bln. UAH y7 mln. UAH y8 mln. USD y9 mln. pers. y10 mln. UAH 2000 5,850 48,3 2,412 0,267 10,5 2001 7,305 -4,2 2,412 0,283 18,8 2002 8,578 -7,0 2,935 0,285 14,2 2003 10,547 45,0 0,041 0,228 30,7 2004 13,992 63,2 0,064 0,216 40,3 2005 17,192 70,8 0,438 0,213 75,1 2006 21,486 166,3 0,612 0,189 84,

Accordingly to statistical data of agricultural sector of Lvov region and Ukraine provided by State Statistical Service of Ukraine for 2000-2009 years, presented in tables (1) in this applied research calculate main dynamics and trend models and calculate predicted values and their estimates for forthcoming two years with application of the applied statistical tool STATGRAFICS and EXCEL spreadsheets.
Relevant predicted values and their estimates based on trends (1) -(15) were calculated, that are displayed in table (2).Here: ME -mean value of the error; MSE -mean square value of the error; MAE -mean absolute value of the error.It is necessary to notice that the closer the values of ME, MSE and MAE to zero, the better will be calculated forecasts value of the appropriate indicators.Forecast with the least gross regional product error (Lvov region) receive on the basis of parabolic trend model ( 2 Value of the multiple determination coefficients R 2 of the simple and multiple linear regression equations ( 16) -( 19) give a reason to assert that all of them have a good probability or credibility, however, except equation ( 16), their value is greater than 0,7, and considerable part is rather close to one.Existence of the linear dependence between the resulting and factor variables also confirmed by obtained F -criteria, that with probability р = 0,95 is more greater than F table = 5,59, that was calculated with assistance of F -Fisher distribution.
Study of the regression model ( 18) shows slight positive influence on gross regional product of Lvov region у 6 of foreign direct investment in agriculture of Lvov region у 8 (в 8 = 0,13442) and investments in capital assets (agriculture of Lvov region) у 10 (в 10 = 0,04638), in particular, while increase foreign direct investment inflow in agriculture of Lvov region at 1 mln.USD and some constant or mean investments in capital assets (agriculture of Lvov region) value у 10 is expected to grow gross regional product of Lvov region у 6 by an average of 0,13442 bln.UAH; moreover increase of investment in fixed assets (agriculture) of Lvov region у 10 at 1 mln.UAH with an average or constant value of foreign direct investment in agriculture of Lvov region у 8 will result in increment of gross regional product of Lvov region у 6 in average at 0,04638 bln.UAH.
Regression coefficients' values of the multiply regression model (20) argue some slight positive impact on gross regional product of Lvov region у 6 of foreign direct investment in agriculture of Lvov region у 8 (в 8 = 0,19455), financial results of general activity (agriculture) of Lvov region у 7 (в 7 = 0,04147) and investments in capital assets (agriculture) of Lvov region у 10 (в 10 = 0,02422), at the same time employees quantity grow in agriculture of Lvov region у 9 will cause decrease of gross regional product of Lvov region (в 9 = -74,3949).
Finally, one should notice, because of small amount of input sample table (2) analysis of regression equations coefficients (19) і (20) have more qualitative than quantitative influence upon resulting variable y 6 .

CONCLUSION
In this research linear, parabolic, and exponential trend models of investments and main agricultural indicators of Lvov region of Ukraine were presented.All the models with probability р=0,95 are adequate experimental data for 2000-2009, that permit to make the prediction of investments and main agricultural indicators of the researched region by these models for 2010 and 2011.However, it should be pointed out, because of small amount of input data analysis of regression equations coefficients have more qualitative than quantitative influence upon resulting variable y 6 .

Table 1 
Dynamics of agricultural sector main indicators of Lvov region

Table 2 
Forecasting values and estimation indicators of Lvov region's agricultural sector