| dc.description |
This study is concerned with the impact that inaccuracy in the measurement of the dependent variable and one or more of the independent variables has on the price elasticity of demand estimate. Two diagnostics - the regression coefficient bounds and the bias correction factor - are introduced to assess the effect that such measurement errors have on the estimated coefficients of demand relationships. In considering the demand for diesel fuel per acre by farmers in the United States, both the quantity demanded and the price data available contain measurement error. The regression coefficient bounds diagnostic is used to indicate a range over which the true price responsiveness of farmers to changes in energy prices lies. The results suggest that each 1 percent increase (decrease) in the price of energy will result in between a 1.15 and 0.26 percent decrease (increase) in the quantity of diesel fuel demanded. The bias correction factor is computed to evaluate the magnitude of the underestimation of the responsiveness of the diesel fuel quantity demanded per acre to a change in the price of output and precipitation. For the price of output, the underestimation is approximately 61 percent while for precipitation, it is 45 percent. Finally, when considered in the context of forecasting the quantity of diesel fuel demanded over a known horizon, the forecasts based on a model corrected for the presence of measurement error have a smaller forecast error. |
