ESTIMATION OF EFFICIENT REGRESSION MODELS FOR APPLIED AGRICULTURAL ECONOMICS RESEARCH

Texas cotton basis is defined as the difference between the West Texas cash price and the U.S. futures
cotton price for the September contract.

Several factors have been hypothesized to affect this basis, including the monthly Texas, U.S., and
foreign production (TXP, USP, and FP) and beginning stocks (TXBS, USP, and FBS); monthly foreign
mill use (FMU); the U.S. price of rayon (PR); the opportunity cost of storage (STRC), as measured by
Seamon and Kahl; transportation costs measured by the monthly U.S. railroad index for farm products
(RRI); seasonal effects measured by dummy variables representing the planting (SD=0 for March to July)
versus the harvesting and marketing seasons (SD= 1 for August to February); and agricultural policy
effects measured by dummy variables for the pre 1985 farm bill years (PD=1 from 1980 to 1985 and zero
otherwise), the 1985 farm bill period (PD1=1 from 1986 to 1995 and zero otherwise), and the post 1996
farm bill era (PD2=1 from 1996 to 2001 and zero otherwise).

All of the variables discussed above are stationary according to the augmented Dickey-Fuller unit
root test, with the exception of the price of rayon. The first difference in the price of rayon (FDPR), which
is stationary, is therefore used instead of PR. A Durbin-Watson test based on the OLS residuals reveals
first-order positive autocorrelation. The autocorrelation function of the OLS residuals is steadily
decreasing, while the partial autocorrelations become statistically insignificant at lag five. When a tenth-
order autoregressive model is estimated using Gauss 386i autoreg procedure, the autocorrelation parameter
estimates decrease in size and the fifth and higher-order coefficients are not statistically different from zero at the
10% level. Thus, it is concluded that a fourth-order autoregressive [AR(4)] error term specification is sufficient to
correct for autocorrelation.

The standard maximum likelihood procedures outlined above [equation (4)] are then used to estimate an
AR(4) model under the assumption of error term normality and heteroskedasticity [NHAR(4)], shifting the
variance of the error term (σ²) by σ2SD, σ2PD1, and σ2PD2, according to the seasonal and policy dummies (Table 5).
As in previous econometric analysis of regional basis, most of the regression parameters are insignificant. With
the exception of SD, the relatively large standard errors of the slope parameter estimators make it impossible to

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