using monthly data from January 1992 to December 2000, we estimate the following equation for lightweight vehicle sales: Δln (Salest) = 2.7108 + 0.3987Δln (Salest −1) + εt. table 5 gives sample autocorrelations of the errors from this model. table 5 lag 1 2 3 4 5 6 7 8 9 10 11 12 Different Order autocorrelations of Differences in the logs of vehicle Sales autocorrelation 0.9358 0.8565 0.8083 0.7723 0.7476 0.7326 0.6941 0.6353 0.5867 0.5378 0.4745 0.4217 Standard error 0.0962 0.0962 0.0962 0.0962 0.0962 0.0962 0.0962 0.0962 0.0962 0.0962 0.0962 0.0962 t-Statistic 9.7247 8.9005 8.4001 8.0257 7.7696 7.6137 7.2138 6.6025 6.0968 5.5892 4.9315 4.3827 a. use the information in the table to assess the appropriateness of the specification given by the equation. b. if the residuals from the ar(1) model above violate a regression assumption, how would you modify the ar(1) specification?
using monthly data from January 1992 to December 2000, we estimate the following equation for lightweight vehicle sales: Δln (Salest) = 2.7108 + 0.3987Δln (Salest −1) + εt. table 5 gives sample autocorrelations of the errors from this model. table 5 lag 1 2 3 4 5 6 7 8 9 10 11 12 Different Order autocorrelations of Differences in the logs of vehicle Sales autocorrelation 0.9358 0.8565 0.8083 0.7723 0.7476 0.7326 0.6941 0.6353 0.5867 0.5378 0.4745 0.4217 Standard error 0.0962 0.0962 0.0962 0.0962 0.0962 0.0962 0.0962 0.0962 0.0962 0.0962 0.0962 0.0962 t-Statistic 9.7247 8.9005 8.4001 8.0257 7.7696 7.6137 7.2138 6.6025 6.0968 5.5892 4.9315 4.3827 a. use the information in the table to assess the appropriateness of the specification given by the equation. b. if the residuals from the ar(1) model above violate a regression assumption, how would you modify the ar(1) specification?
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