> NPM"\Ebjbj4F93
bb$$$8\h$W,ttttt[[[+++++++$9.0B+i[W[[[+tt,CCC[$tt+C[+CCV)h^*ttO$)n+',0W,)1@1 *1*[[C[[[[[++C[[[W,[[[[1[[[[[[[[[b(:"METHODOLOGICAL FOUNDATIONS OF DESIGNGrain production in UKRAINEO. Symonenko, PhD, Associate ProfessorThe analysis modeling techniques grain production in Ukraine, which allowed to characterize the basic methods of modeling and forecasting key indicators of development of grain production. System grain production inherent non- stationarity, reversibility, and cyclical uncertainty that creates risk and the need for new science-based approaches of modeling and forecasting.Grain production, productivity, simulation, dynamics, forecasting, time series, trend.One of the main problems of the economy is to solve the problem of food security of the population and food inde pendence, Ukraine. As stated in the Law of Ukraine "On Government agricultural policy until 2015" [1], the purpose of state agricultural policy is to guarantee food security and dis-round agricultural production in order to increase its efficiency and competitiveness in domestic and foreign markets. Grain production is the basis of agriculture, determines the volume of supply and the cost of basic food types, forms to exchange-go state by exports.Analysis of recent research and publications. In recent years, there are sharp fluctuations in yield and gross grain harvest in Ukraine. Gross grain harvest in 2003 was lean 20.2 million tonnes (3.6 million tonnes of wheat). In recent years the gross grain harvest slightly increased, but remains very unstable. In 2008 it was 53.3 million tonnes (25.9 million tonnes of wheat) in 2009 -46.0 million tonnes (20.9 million tonnes of wheat) in 2010 - 39.3 million tons (16.2 million tonnes of wheat).The profitability of grain production is critically dependent on the amount of crop and also undergoes similar fluctuations. At the same time the level has dropped significantly in recent years. In 2008, they accounted for 16.2%, in 2009 - 7.0% in 2010 - 13.9%. According to experts, for effective grain production is minimal profitability of grain must be 20%, and rational (in the case of Ukraine) - at least 40%. The reasons for decline in profitability of grain production and the decrease in production is the lack of effective economic incentives for the production of grain and systematic approach to solving complex management issues grain.Of Ukrainian scientists play a significant role in the current development of methods and models predicting socio-economic processes. Especially should be made of VS Mikhalevich, IV Sergienko, VM Heytsya, A. Cherniak, Y. Lebedev, VV Vitlinskoho, IG Lukyanenko, V. M. Wolf, VI Yeleyko, VM Porokhnya, M. Ivanov, LN Sergeev, NK Maksyshko, KF Kovalchuk.Studying the works of these authors showed that they are not considered models of medium and long-term forecasting, zernovyro of production and model the impact of the gross yield and the price of exports of grain and grain production profitability model to assess the risk of grain production forecasts based on yield and gross yield. The dynamics of grain production is often seen at modeling trends and included its fractal nature, little attention is paid to modeling stochastic component of this dynamic. Sound analysis work on various aspects of the analysis, modeling and prediction of grain production, found many unsolved problems and fragmented picture in simulation of grain production in Ukraine. This fact led to the choice of research topic.The aim - to analyze the modeling methodology ma-themed models for forecasting key indicators of se-rnovyrobnytstva that will improve the accuracy of forecasts as a basis for decision-making weighted aimed at improving the efficiency of grain production in Ukraine.Achieving this goal necessitated analyzing trends of modern economic systems modeling methodology to select the basic research methods.Object is a system of grain production in Ukraine. Pre-DMetI - methodology and appropriate tools of economic and mathematical modeling, analyzing and forecasting the development of key indicators grain production.The methodological basis is a systematic approach, analysis methods, the forecast dosing grain production in the economic, administrative and information-traditional dimensions. The basis for the formalization and quantitative description of the dynamics of grain production mechanisms are the methods of synergetics, nonlinear dynamics, the theory of fractals, economic statistics and econometrics.The main material. The main parameters that descriptive-suyut dynamics of grain production are sown area, gross and Stage zhaynist, economic performance is the price of grain and grain-novyrobnytstva profitability. During the growing season is changing dis-miriv acreage occupied by particular culture, which primarily related to crop losses due to adverse weather and climatic conditions. Methods of planning and decision-making based on mathematical modeling and forecasting with the latest computer technology.The modern approach to modeling the economy involves the use of methods of nonlinear dynamics, fractal analysis and applied stochastic processes [2]. Most of the publications of the last decades in this area is devoted to analysis of the dynamics of financial instruments. Almost no work that demonstrate the use of these methods for the analysis of agricultural production, due to both objective and subjective reasons. The former include the fact that nonlinear dynamics and fractal analysis usually operate large data order 103 and above, and such amount of information is hard to find in the statistics of agricultural production, which is typical for an annual cross-section data. Subjective causes a small amount of work, using modern methods of analysis of agro-production is the result of outdated stereotype of lesser importance compared with agricultural or industrial fi nancial-activity.Without solving the problem of small sample size and short time series study the dynamics of grain production in modern methods mentioned above are not possible. Scientists suggest the following solution to this problem: ob'yed Nata-group areas with homogeneous nature of the dynamics of grain production; sliding window method to use when performing R / S-series analysis yields. Using this method extends the statistical basis of calculations and provides a reliable Hurst coefficient for short time series yield and gross yield.One of the first on the need for time series prediction Zvery-nuv attention outstanding Ukrainian economist, mathematician EE Slutsky [3]. Based on the theory related series he built forecasting method of random processes. This technique is designed for long-term forecasts of the specified error limits. Reliable methods of forecasting grain-duction can be built only on the basis of modern paradigms but other fields of economic and mathematical methods. The linear paradigm was not able to describe, such complex systems, both economic and was replaced nonlinear, resulting in the widespread use of nonlinear dynamics methods for analysis of economic processes and phenomena based on the rapid progress of new computer technologies that implement these methods.It should be noted that the choice of method of forecasting largely, is the volume and structure of the original data. If the available data de-couple system parameters for a specific time period using correlation analysis. If you set a noticeable correlation between different parameters, it is the basis for predictive regression model of the form. (1)If the impact of known factors on the studied characteristics is weak (or non-linear) for the forecasting method using de-composition time series of subsequent extrapolation of trend and cyclical component.The economy often cases when the current state of the process value factor affects yesterday. In such cases, use-tovuyut autoregression model that combines the ideas of both these approaches. As in predicting factor is determined by a number of factors explanatory, but it is the role of past observations. If we are using some procedures define what a significant impact on the next exercise factor was previous observations, we obtain regression model of the process from its previous values, ....,. This model is called autoregressive model order or the AR model order. Autoregressive model of order would look like this:. (2)More sophisticated statistical forecasting model is discontinued, binovani model autoregression - moving average ARMA (AutoRegression - Moving Average) - and their generalization for unsteady cases - model ARIMA (AutoRegression Integrated Moving Average).There are many approaches to forecasting and predictive models of agricultural production, productivity. All methods of yield prediction can be reduced to four groups - kosmostatystychni, geostatistical,abstract statistical and system-statistics.Kosmostatystychni methods of exploring hypothetical dependence Stage zhaynosti of cosmic processes and the dynamics of solar activity. The data on the dynamics of solar activity traditionally displayed in the value of Wolf W. Wolf number for a particular day is calculated based on astronomical observations ratio(3)where - Wolf number - the number of observed spots - the number of observed groups of spots, k - normalizing factor. For the dynamics of co-nyachnoyi activity is typical 11-year cycle.Lack of effectiveness and validity kosmostatystychnyh example because the influence of space factors on the vegetative process is carried out indirectly. Therefore, predicting yields a combination kosmostatystychnyh and other projections, including Geo-statistical.eostatystychni weather forecasts take into account the impact of geophysical, cli-matic and weather factors on the dynamics of productivity. The first approach is often reduced to correlation and regression modeling depending on meteorological yield and agronomic factors. A common drawback of these models is a small bias forecast period. The second approach is based on the assertion that sufficiently long time series of one parameter contains all information about a dynamic system that spawned it. So, with proper identification system and a successful filtering noise can construct an adequate predictive models.The common disadvantage eostatystychnyh forecasting methods is their small premature. It should be closer to behave to an abstract statistical methods yield prediction, which involve the study of the dynamics of the process features without considering the reasons that cause this dynamic. One of the most interesting scientific hypothesis is the hypothesis of the cyclical nature of the changes in yield on some limited area. Research cyclical phenomenon yields paid attention to many species-IIR scientists. Thus, the Russian statistics as a measure of similarity-ness fluctuations harvest crops suggested the use of co-correlation coefficients between the rows of deviations from the trend of time series of one-cle length. In this regard, the conclusions based on the research trend remains, always a controversial choice because of different variants trend.Emphasizing the practical importance of studying kolyvnosti yield, AI Manellya considers appropriate statistical study of its characteristics, such as power fluctuations and fluctuations type. Abso lute main-performance power fluctuations AI Manellya says co-amplitude oscillations, the mean absolute deviation from the linear trend and standard deviation from the trend. Based on these characteristics, [4] introduces the concept kolyvnosti factor, which can be determined by the formula(4)where - the standard deviation from trend yields - the average yield.In our opinion, the weak point of approach to AI Manellya analysis kolyvnosti there is uncertainty about the trend model. Complex dynamics yields may prevent some trendy models that can provide different conclusions about the type kolyvnosti, so the study kolyvnosti number of first differences will yield more productive. This approach eliminates the uncertainty associated with the choice of trend.Conclusions and prospects for further research. Climate change led to an increase in yield fluctuations. This requires new approaches to the study of the dynamics of grain production. In addition, predicting pro-processes that have high variability, to avoid unjustified conclusions should apply multivariate modeling technique you-duction of grain. The most reliable method is to mu;@0CD,E.E0E4E6E:EE@EDEFEHEJELENEPERETEVEXEZE\E dgdWugd~ltivariate forecasting by using multiple predictive models and in the structure of the combined forecasts based on them. This can provide on-hnozni options that provide optimistic and pessimistic scenarios, some limits Forecasted.References1. The Law of Ukraine "On State Forecasting and programming eco-nomic and social development of Ukraine" // Bulletin of the Verkhovna Rada of Ukraine. - 2000. - !25. - P. 195.2. Ayvazyan SA Applied Statistics. Fundamentals of Econometrics / SA Ayvazyan, V. Mkhitaryan. - Moscow: UNITY, 2001. - 1002 p.3. EE Slutsky Slozhenye sluchaynKh reasons Source How tsyklycheskyh protsesov / EE Slutsky // Questions konJyuktury. - 1927 - Vol. 1, v.3. - P. 34-64.
21h:pxt. A!"R#n$n%j666666666vvvvvvvvv66666>66666666666666666666666666666666666666666666666hH6666666666666666666666666666666666666666666666666666666666666666662 0@P`p2( 0@P`p 0@P`p 0@P`p 0@P`p 0@P`p 0@P`p8XV~ OJPJQJ_HmH"nH"sH"tH"L`LWu1KG=K9dCJ_HaJmH sH tH BA B
A=>2=>9 H@8DB 0170F0XiX01KG=0O B01;8F04
l4a.k .
0
5B A?8A:06& 6Wu=0: A=>A:8H*^JTT
Wu0
"5:AB 2K=>A:8dCJOJQJ^JaJ\\Wu0"5:AB 2K=>A:8 =0: CJOJPJQJ^JaJmH sH R"R7R"5:AB A=>A:8dCJPJaJmH"sH"B1B7R"5:AB A=>A:8 =0:PJtH >U A>P_8?5@AAK;:0>*B*^JphPK![Content_Types].xmlj0Eжr(Iw},-j4 wP-t#bΙ{UTU^hd}㨫)*1P' ^W0)T9<l#$yi};~@(Hu*Dנz/0ǰ$X3aZ,D0j~3߶b~i>3\`?/[G\!-Rk.sԻ..a濭?PK!֧6_rels/.relsj0}Q%v/C/}(h"O
= C?hv=Ʌ%[xp{۵_Pѣ<1H0ORBdJE4b$q_6LR7`0̞O,En7Lib/SeеPK!kytheme/theme/themeManager.xmlM
@}w7c(EbˮCAǠҟ7՛K
Y,
e.|,H,lxɴIsQ}#Ր ֵ+!,^$j=GW)E+&
8PK!Ƶtheme/theme/theme1.xmlYnE#;8U
i-qN3'JH DA\8 R>CW]{'ސ{g?x݈]"$q+_,y>8{7K
}xLވHe 2{Rܜaˋ??<ߟǆSr$L].<İT̟72S"ѵ_JwL&BJ05kZVyVy|xC OO|x>+<>@(:??/>5{yxFDdm3^q5'=q6nib-$RB}}YGq=xK@;)^qbha79g
.
pUʹ;bbmc[$c'a}5KKfH5HL!ݦ&|mK{N6M6hqv|y58+zH
*~
HHy\K*t@G>{sA}"))y
sGfۡqǾ'w E1
[8>ܤ4Aбt~ls14_>,Ȭ7TT GqME=w-i>mo[oiBszbP:j5if F
9Pɉ* k\ A
;!N`.{I SցD p3˅5veU}AbvyA/g 氚 ZN+lR~aeԩj9&&CLgMŉ7a A0ƀHEA3~{qq1vƨl効9)>൜fN1NFIrdq8Y^:_73.|3 f%Jش?MOYs3;} Rc0`d[鯠$ÿ
-έhǴ"P
6T{TUٴ+9E28Y*MOt0O9Bݍqg7Ŕ9O)z?7#]urBIHAאTpl>՟,SpT4@~BA%}'0+{eRF&rĪ#uu\{BHuM6`pG}N+!'_oN콶3a3dXZzCyCUɪ嶂ZZZ۱f,fAg-@`gv6V?Dhf6t=M&ʺ6ײ'݉#֚>d8s9xN=ڮjAv11;u2%M2oN1uo%տPK!
ѐ'theme/theme/_rels/themeManager.xml.relsM
0wooӺ&݈Э5
6?$Q
,.aic21h:qm@RN;d`o7gK(M&$R(.1r'JЊT8V"AȻHu}|$b{P8g/]QAsم(#L[PK-![Content_Types].xmlPK-!֧6+_rels/.relsPK-!kytheme/theme/themeManager.xmlPK-!Ƶtheme/theme/theme1.xmlPK-!
ѐ' theme/theme/_rels/themeManager.xml.relsPK]
P3P3F \E\E8@0(
B
S ?P3ENMY[^(.<BNT
"
)
.
7
<
E
kp09KR$+ovw K!Y!"0"2"@"n""""9$B$$$+%=%p%%%%'(****m+w++++,.,6,,,----W.a.p.z.M0T0
11111111&2.2h2p2u22222222222222
3393;3<3>3?3A3B3D3F3Q305m9
;
3EF 4FWA-.\m q t u @"A"''T((&)0)+;,(-)-/0/h1u122F222293;3<3>3?3A3B3D3F3Q333333333333333333333333333393;3<3>3?3A3B3D3F3Q3j2;)64h#N5N+n6nHuH6h
^`hH.h
^`hH.h
pLp^p`LhH.h
@@^@`hH.h
^`hH.h
L^`LhH.h
^`hH.h
^`hH.h
PLP^P`LhH.h^`OJQJo(hHh^`OJQJ^Jo(hHoh
^
`OJQJo(hHhw
^w
`OJQJo(hHhG^G`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hH[^`OJPJQJ^Jo(-^`OJQJ^Jo(hHox ^x `OJQJo(hHH^H`OJQJo(hH^`OJQJ^Jo(hHo^`OJQJo(hH^`OJQJo(hH^`OJQJ^Jo(hHoX^X`OJQJo(hHh
^`hH.h
^`hH.h
pL^p`LhH.h
@^@`hH.h
^`hH.h
L^`LhH.h
^`hH.h
^`hH.h
PL^P`LhH.^`o(.
^`hH.
L^
`LhH.
\
^\
`hH.
,^,`hH.
L^`LhH.
^`hH.
^`hH.
lL^l`LhH.uh#N5n6n;)"""""""""6"""""""""Ƅ10P
+4_o(tL1S4~8c=DaJ4:LN!SP7RT7ZS[V]teXgJj"5lKttxtWu`yw,tQ=$SeJxP_9\gMsM;.~dmAMGQ1
N:93;3@8383Ȯ8383dp%p&'(0
22P3@.`@2h@@BDUnknownG* Times New Roman5Symbol3.* Arial7.{ @Calibri5.*aTahoma?= * Courier New;WingdingsA BCambria Math"qc2'c2' ' '!n203'3 3qHX $PWu2!xxEugeneEugene Oh+'0l
(4
@LT\dEugeneNormal.dotmEugene2Microsoft Office Word@@ϟO@ϟO ՜.+,0hp
diakov.net'3
!"#%&'()*+,-./0123456789:;<>?@ABCDFGHIJKLORoot Entry FOQ1Table$=1WordDocument4FSummaryInformation(=DocumentSummaryInformation8ECompObjy
F' Microsoft Office Word 97-2003
MSWordDocWord.Document.89q