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Text of Crude Palm Oil Price Forecasting: Box-J .CRUDE PALM OIL PRICE FORECASTING: ~O~-JENKINSAPPROACH...

  • Pertanika 9(3), 359 - 367

    Crude Palm Oil Price Forecasting: Box-Jenkins Approach

    FATIMAH MOHD. ARSHAD and ROSLAN A. GHAFFAR*Department of Agricultural Economics,Faculty of Economics and Management,

    University Pertanian Malaysia,43400 Serdang, Selangor, Malaysia.

    Key words: Crude palm oil price; univariate; identification; estimation; diagnosis.


    Model univariate yang diCpta oleh Boxjenkins telah digunakan untuk meramal hargabulanan minyak kelapa sawit mentah. Model yang telah dikenalpasti sesuai untuk ramalam adalah(0, 2, 1) (0, 1, 1) 6' Model ini menunjukkan bahawa siri data harga minyak kelapa sawit mentah iaitutak pegun dan mengandungi unsur-unsur gandaan, menyarankan kewujudan proses purata bergerakModel ARIMA yang dikenalpasti menjadikan siri data kepada bercorak stokastik, membolehkanmodel ini meramal harga minyak kelapa mentah dalam jangka masa pendek.


    A univariate ARIMA model developed by Boxjenkins was utilised to forecast the short-runmonthly price of crude palm oil. The appropriate model for forecasting was found to be (0, 2, 1)(0, 1, 1) 6' This model indicates that the original crude palm oil series is non-stationary and containssome elements of multipliCty, hence inheriting moving average process. The identified ARIMAmodel induced the data series into a stochastic one, making it a suitable model forforecasting crudepalm oil prices in the short term.


    Forecasting consists basically of using datato predict future values for given variables tofacilitate macro and micro level decision-making. In the case of Malaysia's crude palm oil,price forecasts represent valuable and funda-mental information to direct and indirecttraders in fats and oils market, and to financiers,producers and manufacturers and policy,makers. Over 3 mn MT of palm kernel oil andpalm oil of one form or another are traded in theworld each year, with an Lo.b. value in excess ofUS$2.5 bn.

    There are, of course, many ways to generateprediction, ranging in complexity and datarequirements from intuitive judgements throughtime series analysis to econometric models 1. Thelatter two approaches produce what Theil (1966)referred. to as "scientific forecasts", in that it isformulated as a verifiable prediction from anexplicitly stated method which can be reproduc-ed~. A prime goal of forecasting studies is toassess the factors influencing supply and demandby developing estimates of coefficients and pro-viding elasticity and flexibility of estimates. Toachieve this goal, econometric models are usedalmost invariably.

    Department of Economics, Faculty of Economics and Management, UPM.

    I Other techniques of forecasting (for agriculture) include informal models, indicato~s, balance sheet methods and surveys (see

    Freebairn, 1975).

    2 The terms prediction and forecast are used interchangeably.


    A sophisticated subclass of linear time seriesmodels which have been known to have desirableforecast properties are those of the autoregressiveintegrated moving average (ARIMA) type,developed by Box-Jenkins (1970). To date, theBox-Jenkins technique has only been demonstr-ably successful in non-agricultural forecasting.

    Several attempts have been made to com-

    pare the relative effectiveness of various forecast-ing techniques. For instance, Teigen (1973)compared the performance of econometricmodels, trend models, price difference modelsand the futures price to forecast US cattle pricesusing monthly and quarterly prices for theperiod of 1967 - 70. He concluded that a simplenaive (no change) forecasting method providesbetter results than sophisticated methods. Theanalysis of Teigen indicated that projections ofcurrent cash prices or the corresponding futuresprice provided a good estimate of price to pre-vail in the forecast period. Gellatly (1979)evaluated the performance of several methodsused to forecast New South Wales quarterlybeef production, one quarter ahead. The fore-casting procedures used were a single equationregression model, a Box-Jenkins univariate timeseries model, committee's judgement and a naivemodel. Although' Gellatly's evaluation gavemixed results, it appears that the forecast-ing committee performed better than theother forecasting procedures. Nevertheless, theresults also indicated that the committee's per-formance was little better then that of a naive(no change) model, suggesting there is room forimprovement in general and as regards both thesingle-equation and Box-Jenkins models in parti-cular.

    Other attempts to evaluate forecastingtechniques reveal the supperiority of the Box-Jenkins univariate time-series model for deliver-ing accurate predictions. Helmer and Johansson(1977) compared Box-Jenkins' results with theestablished econometric models and cross-spectral analysis applied by other authors on thesame set of data on advertising-sales relation-ships. The forecasting ability of Box-Jenkinstransfer function models were proven to be far .more accurate than econometric models and

    cross-spectral analysis. Bourke (1979) comparedthe forecasting accuracy of the Box-Jenkins andother econometric techniques for. forecastingmanufacturing-grade beef prices in the U.S.A.Criteria used to measure accuracy were RootMean Squared Error, Theil's Inequality Coeffi-cient and Turning Points. His findings suggestedthat Box-Jenkins models were in generalmarginally superior to the econometric method.Usirig a similar approach as Bourke, Brandt andBessler (1981) found that the ARIMA modelmethod performed substantially better thaneither the econometric or the expert opinionmethods.

    Thus, the application of the Box-Jenkinstechnique can be regarded as a fairly safe one forforecasting in comparison with other methods.With notable exceptions of Mohamad Napi(1982) and Mohamad YusofTalib (1985), whoseuses of the Box-Jenkins technique producedsatisfactory results, little attempt has been madeto utilise the technique to forecast the prices ofMalaysian local commodity prices. In view of itsproven superiority, this article therefore seeks toforecast crude palm oil prices by using the Box-Jenkins model.


    Details of the Box-Jenkins approach may befound in Box and Jenkins (1970) and critical dis-cussions of it by Newbold (1975) and Geurts andIbrahim (1975). A brief account of the generalis-ed Box-Jenkins model is given below.

    Generally speaking time series data can becategorised as stationary and non-stationarydata which are mostly generated by a stochasticprocess. Stationary models are based on theassumption that the process remains in equilib-rium about a constant mean level. Suppose wehave a stationary series having mean and obser-vations Z ,Z , Z ,Z ..... are taken att t-I t-2 t-3equal intervals. We define at' at_I' a t_2..... as"white noise" or random shocks to the system.Then there are two ways to model the series as anautoregressive (AR) model; and, as a movingaverage (MA) model.

    360 PERTANIKA VOL. 9 NO.3. 1986


    p (B) = 1

    Autoregressive processes of order p(ARIMA (p, 0, 0 may be modelled by using plagged observations of the series to predict thecurrent observation, that is,

    + p Zt':-p + at 1 (1)

    Which, using the backward shift operator,may be rewritten as,

    (B) Zt = at

    where (B) = 1 - 1 B - 2 B2

    ..... -p BP, ..... 2

    2at "" NI, (0, aa ) and

    Z =Z -Jlt t

    Sometimes a current deviation from themean period t is made linearly dependent on allprior deviations back .to period (t - q). There-fore, the current deviation can be expressed as alinear function of the "white noise" to thesystem. The above process is called movingaverage process of order q (ARIMA (0, 0, qwhich can be expressed as:

    or Z =8q (B) at' and2

    at"" NI (0, aa )

    Often the pattern of data may be describedbest by a mixed process of AR and MA elements.Almost all the stochastic or deterministic timeseries encountered in practice exhibit somedegrees of non-stationarity which denote theexistence of either AR or MA elements separate-ly. or possibility some combination of them both.

    A preliminary model which incorporatesstochastic and deterministic trend characteris-tics, non-seasonality and seasonality, is necessarybefore identifying a specific and relevant form ofmodel which will describe the process. Theappropriate preliminary model for an auto-regressive integrated moving average model(ARIMA (p, d, q is:-

    p (B) (1 - B)d (1 - BS)DZt =8p '

    + 8q(B) at 4

    where 8 = (1 - Ao. _ Ao. - Ao. )P '#'1 '#'2 . '#'p Jl

    2 ,J". P


    For each differencing pattern specified byd, D and s, Box-Jenkins calculate sample auto-correlation function, r of lag k as:

    If data shows nonstationarity, differencingis needed to transform it to a stationary series. Astationary series is required for identificationbecause the theoretical autocorrelation andpartial autocorrelation of stationary series havedistinct patterns for various models. In general,data should be differenced either regularly and/or seasonally to a degree that is just sufficient toinduce stationarity.

    Where kk is the last coefficient. This willlead to a Yule-Walker Equation which may bewritten in matrix form as:

    As a general rule when the autocorrelationsdrop off exponentially to zero as k increases, thisimplies an AR model whose order is determinedby the number of partial autocorrelation whichare significantly different from zero. If thepartial autocorrelations drop off exponentiallyto zero as k increases, the model is MA, and itsorder is determined by the number of statistical-ly significant autocorrelations. When both auto-correlations and partial autocorrelations drop