RJOAS, 7(31), July 2014





                ECONOMETRIC FORECAST OF AGRICULTURAL SECTOR INVESTING IN LVOV REGION




Rostyslav Lytvyn, Research assistant
Lvov national university of veterinary medicine and biotechnologies named after S.Z. Gzhytskyj, Lvov, Ukraine E-mail: lytvynr@ukr.net




                ABSTRACT




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.




                KEY 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, I. Kopych, R.. Bodnar, М. Demchyshyn, О. Synytskyy, А. Chemerys [4-7], M.I. Gomez, E.R. Gonzales, 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       y7       ys        y9       y10   
     bln. UAH mln. UAH mln. USD mln. pers. 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      84,0  
2007  27,987   231,9    1,653     0,188     211,0  
2008  35,534    77,7    31,783    0,177     549,8  
2009  39,893    17,9    91,482    0,175     321,9  

Source: State Statistic Service of Lvov region and Ukraine [13,14].

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RJOAS, 7(31), July 2014

       Here comes:
       y₆- gross regional product (Lvov region) in actual prices, in bln. UAH;
       y₇ - financial result of general activity (agriculture) of Lvov region before taxation, in mln. UAH;
       y8 - foreign direct investment in agriculture (Lvov region), in mln. USD;
       y9- quantity of the employed persons in agriculture of Lvov region, in mln. pers.;
       y10- investment in capital assets (agriculture) of Lvov region, in mln. UAH.
       Econometric forecast models. Thus linear (ylⁱⁿ), parabolic (ypar) and exponential (yexp) trend models will look like:

УбП= -2,39867+3,860924                 (1)  
ypar= 6,16267-0,41974 4+0,3891542      (2)  
y*xp= exp {1,51972+0,221854}           (3)  
y17ln= 15,3333+10,52674                (4)  
ypar= -55,3167+45,85174-3,2113642      (5)  
y7xp= exp {2,62598+0,200174}           (6)  
yJln= -20,0469+6,078214                (7)  
ypar= 31, 8863-19,8884 • t+2,3606 • t2 (8)  
y®xp= exp {-1,52552+0,351114}          (9)  
yjn= 0,2946-0,013184                   (10) 
ypar= 0,3056-0,018684+0,00050 • t2     (11) 
y;xp= exp {-1,19592-0,059064}          (12) 
ypn= -120,98+46,65644                  (13) 
ypoar= 54,0783-40,8728 • t+7,9572 • t2 (14) 
y^ exp {1,73874+0,433764}              (15),

where yᵢ (i=1,2,..., 10) - regulatory or averaged values of the researched indicators; t - time.
        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.


Table 2 - Forecasting values and estimation indicators of Lvov region’s agricultural sector

Indicator Indicator forecast    ME      MSE     MAE  
           2010      2011                            
    1        2         3         4       5       6   
                    bln. UAH                          
y         40,071    43,932       0    8,4881  2,6473 
    r     48,633    57,163       0    0,4921  0,5402 
  yexp    52,459    65,489    0,0132  0,9590  0,6575 
                    mln. UAH                          
  y7ln    131,127   141,653      0    3845,42 46,4147
  y7or    60,477    32,467       0    3300,90 48,9838
  yexp    124,939   152,626   24,4689 4905,24 143,899
                    mln. USD                          
ylln                                                 
yR        46,813    52,891       0    456,364 16,4355
yfor                                                 
yR        98,747    133,152      0    162,139 10,8384
yexp                                                 
yR        10,347    14,699    10,9969 782,519 12,4516
                   mln. Pers.                         
ylln                                                 
y9         0450      0436        0    0,00020 0,0116 

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