Palm Oil Case Study

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Objective (v) Seeks to estimates a supply response model for palm oil in Nigeria inclusive of price and non-price factors. In other to estimate the supply response for Nigeria palm oil production, the model of Ayanwale et al, (2011) was used, with slight modification. The supply equation is specified as a function of the production of palm oil, price of palm oil, price of competitive crop (soyabean), harvested area, rainfall in year t (mm) as climate element, interest rate, exchange rate, palm Oil import in year t (tons) as a proxy for importation policy and Time trend as proxy for technological change or producers’ preference
OUTPUT=f(LPPO,LPSO,PHA,INTRA,EXR,IMPO,RANFAL,POLY, ECT, St).................(3.13)
Where;
OUTPUT = Nigerian palm oil Supply in year t, Proxied by palm oil Output (tons)
LPPO = Local Price of palm Oil in years t.
LPSO = Local Price of Soyabean in years t.
PHAt = palm oil harvested area at time t.
INTRA = Interest rate in years t.
EXR = Exchange Rate in years t.
RANFAL = Amount of Rainfall in year t (mm) as climate element
IMPO = Palm Oil Import in year t (tons) as a proxy for importation policy
Poly = Policy Variable (1-Policy intervention regime, 0-Non- policy intervention regime )
ECT is the error
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Based on the above estimate, the residual term et= are generated; and iii). The residual term is included in the short-term equation correction as an error correction ………………………………………………………… (3.16) and obtained in this process are then interpreted and used as earlier illustrated with the simplified ECM representation in (3.15). The Engle and Granger (1987) approach to testing for co-integration is to test for stationarity of the stochastic residuals generated in the second stage of the three stage ECM procedure (Gujarati and Porter , 2009; Ogundele, 2007,Ayanwale et al 2011). Equation (3.16) will be estimated using the least square regression. This will lead us to a parsimonious error correction model which will equally be

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