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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

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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).

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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

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These are shown in Figure 1 ( a to d).

(a)

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(b)

is determined on the assumption that the

farmer knows what and are.

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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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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