残差项et为何不加？

问题如下：

Angela Martinez, an energy sector analyst at an investment bank, is concerned about the future level of oil prices and how it might affect portfolio values. She is considering whether to recommend a hedge for the bank portfolio’s exposure to changes in oil prices. Martinez examines West Texas Intermediate (WTI) monthly crude oil price data, expressed in US dollars per barrel, for the 181-month period from August 2000 through August 2015. The end-of-month WTI oil price was $51.16 in July 2015 and $42.86 in August 2015 (Month 181). After reviewing the time-series data, Martinez determines that the mean and variance of the time series of oil prices are not constant over time. She then runs the following four regressions using the WTI time-series data.

Ÿ Linear trend model: Oil price_t = b₀ + b₁t+ e_t

Ÿ Log-linear trend model: ln Oil price_t = b₀ + b₁t+ e_t

Ÿ AR(1) model: Oil price_t = b₀ + b₁Oil price_t–1 + e_t

Ÿ AR(2) model: Oil price_t = b₀ + b₁Oil price_t–1 + b₂Oil price_t–2 + e_t

Exhibit 1 presents selected data from all four regressions, and Exhibit 2 presents selected autocorrelation data from the AR(1) models.

In Exhibit 1, at the 5% significance level, the lower critical value for the Durbin–Watson test statistic is 1.75 for both the linear and log-linear regressions.

After reviewing the data and regression results, Martinez draws the following conclusions.

Conclusion 1: The time series for WTI oil prices is covariance stationary.

Conclusion 2: Out-of-sample forecasting using the AR(1) model appears to be more accurate than that of the AR(2) model.

5. Based on Exhibit 1, the forecasted oil price in September 2015 based on the AR(2) model is closest to:

选项：

A.

$38.03.