Price forecasting of high value commercial crops (HVCC) for the GMA-HVCC Programme
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Transcript of Price forecasting of high value commercial crops (HVCC) for the GMA-HVCC Programme
8/7/2019 Price forecasting of high value commercial crops (HVCC) for the GMA-HVCC Programme
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PRICE FORECASTING OF HIGH VALUE
COMMERCIAL CROPS ( HVCC ) FOR
THE GMA-HVCC PROGRAMMEDanilo L. Evangelista
Project Leader
Alicia O. Evangelista
Research Associate
Dr Rene Rafael Espino
Antonio Jesus Quilloy
Consultants
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TERMINAL REPORT PRESENTATION
• OUTLINE OF REPORT
• Significance of the study
• Objectives of the study
• Conceptual framework
• Research methodology
• ARIMA modeling results
• Price forecasting
• Output
8/7/2019 Price forecasting of high value commercial crops (HVCC) for the GMA-HVCC Programme
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SIGNIFICANCE OF THE STUDY
• Provide price forecast to farmers, marketing
agents, government planners and
policymakers
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OBJECTIVES OF THE STUDY
• 1. To obtain the best price forecasting model
• 2. To provide farmers and marketing agents
forecast prices to assist them in their
production and marketing decisions
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Conceptual Framework
In developing the framework for this study, we
assume that the farmer seeks to maximize his
profits. Moreover, his production function is
and the prices of the input and output are
and respectively. Mathematically, his
production function is:
Where: x is the single variable input. Marginal
product is given by f’(x).
8/7/2019 Price forecasting of high value commercial crops (HVCC) for the GMA-HVCC Programme
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8/7/2019 Price forecasting of high value commercial crops (HVCC) for the GMA-HVCC Programme
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The profit maximizing equilibrium condition
equates the marginal product to theInput-output price ratio:
Or equate marginal value product to :
Therefore, the farmer will employ units of a
variable input until the point is reached where
the value of the marginal product of the input
exactly equal to the input price. The optimal
level of input is denoted by
8/7/2019 Price forecasting of high value commercial crops (HVCC) for the GMA-HVCC Programme
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These are shown in Figure 1 ( a to d).
(a)
8/7/2019 Price forecasting of high value commercial crops (HVCC) for the GMA-HVCC Programme
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(b)
is determined on the assumption that the
farmer knows what and are.
8/7/2019 Price forecasting of high value commercial crops (HVCC) for the GMA-HVCC Programme
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Let us examine what happens to profits if the
farmer believes the price of the output is greater
or less than the actual price. The farmer is
optimistic about . That is holding
constant. The farmer will use more than theoptimal level and incurs losses. The net effect
will be reduction in profits.
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(c)
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Pessimistic Scenario :
(b)
Figure 1. Determination of profit maximizing input level.
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ARIMA MODELING
Figure 2 . Stages in the Box-Jenkins iterative approach to model building. (Pankratz).
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COFFEE MODELING ILLUSTRATION
Figure 24. Farmgate prices of coffee (robusta), Davao City, 2001-2009.
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Figure 25. Correlogram of the original data series.
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Figure 26. First difference or transformed series.
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Figure 27. Correlogram of the 1st difference.
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ARIMA MODELSEstimates ARIMA ( 2,1,1)
X(t) = .5449 X(t-1) -
.2357 X(t-2) + Z(t) -
.4630 Z(t-1)
ARIMA (2,1,2)
X(t) = .2547 X(t-1) -
.9631 X(t-2) + Z(t) -
.1587 Z(t-1) + .9975
Z(t-2)
ARIMA (1,1,1)
X(t) = - .1568 X(t-1)
+ Z(t) + .2815 Z(t-1)
WN Variance 10.24 9.23 10.69
AR Coefficient 1 .5649(.2764) .2547(.0265) -0.1569(0.367)
t computed 2.044 9.611 - 0.427
AR Coefficient 2 -.2357(.0936) - .9631(.0264)
t computed -2.518 -36.481
Table 6. Parameter estimates and various diagnostic statistics for candidate
models, coffee (robusta), Davao City.
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MA Coefficient 1 -.4630(.2778) -.1587(.0070) 0.2815(0.3461)
t computed -1.67 -22.671 0.813
MA Coefficient 2 .9975(.0070)
t computed 142.5
AIC 561 556 563
BIC 557 551 558
Ljung Box Q stat for
theresiduals
20.03 X (20)
p-value = .4560
13.24 X (20)
p-value=.8667
28.49 X (20)
p-value= 0.983
2 2 2
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Stationarity
This implies that the AR coefficients must satisfycertain conditions.
AR (1) = 0.2547
AR (2) = -0.9631
To check for stationarity: we must apply the 3
conditions to the estimated AR coefficients;
stationarity conditions apply only to the AR
coefficients.
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We find that all three conditions are met, and
hence the model is stationary. Moving averagemodels are always invertible.
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Invertibility
To check the model for invertibility, we mustapply certain condition to the estimated MA
coefficient ; invertibility conditions apply only to
MA coefficients.
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8/7/2019 Price forecasting of high value commercial crops (HVCC) for the GMA-HVCC Programme
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Figure 28. Residual plot.
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Figure 29. Correlogram of the residuals.
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Table 12. Forecasted prices of coffee (robusta), 10-month period.
Approximate 95 Percent
Prediction Bounds
Step Prediction sqrt(MSE) Lower Upper
1 52.69314 3.03882 46.73716 58.64911
2 52.93209 4.50863 44.09533 61.76885
3 52.25034 5.71349 41.05211 63.44857
4 52.28194 6.58501 39.37556 65.18831
5 53.38199 7.25358 39.16525 67.59874
6 54.06717 7.93189 38.52096 69.61338
7 53.61762 8.66001 36.64432 70.59093
8 53.27861 9.29700 35.05682 71.50040
9 54.06061 9.80076 34.85149 73.26974
10 55.02172 10.28554 34.86242 75.18102
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OUTPUT• Forecast models were obtained using UBJ modeling
technique for selected vegetables and fruits- Phase I
Tomato – Davao del Sur
Misamis Oriental
Ampalaya – PampangaNueva Ecija
Tarlac
Marinduque
Ilocos SurEggplant – Nueva Vizcaya
Pampanga
Squash – Cavite
Onion – Ilocos Norte
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OUTPUT
- Phase IIBanana – Mindoro Oriental
Batangas
Isabela
Coffee Robusta – Davao City
Compostela Valley
Carabao Mango - Cebu
• Forecast prices were made for the selectedcommodities