Consider the model, Yt = B0 + B1 X1,t + B1 X2,t + Ut, and this is estimated using OLS with 350 observations. However, it is suspected autocorrelation is present. You compute the DW statistic as 0.891. The critical values for this model are: dL = 1.741 and dU = 1.764. What is your decision?
Reject the null.
Fail to reject the null.
C. Undetermined or inconclusive.
the null and alternative hypothesis is
Ho: there is no autocorrelation present
Ha: there is autocorrelation
the DW statistic (d) = 0.891
The critical values for this model are: dL = 1.741 and dU = 1.764
since d < dL
so we reject Ho
Option A is true
Consider the model, Yt = BO+B1 X1,t + B1 X2,t + Ut, and this is estimated using OLS with 250 observations. However, it is suspected autocorrelation is present. You compute the DW statistic as 1.997. The critical values for this model are: dL = 1.692 and du = 1.724. What is your decision? A. Reject the null. B. Fail to reject the null. C. Undetermined or inconclusive.
Consider the model, Yt = Bo + B1 Xt + Ut, and this is estimated using OLS with 65 observations. However, it is suspected autocorrelation is present. You compute the DW statistic as 1.64. The critical values for this model are: du = 1.407 and du = 1.467. What is your decision? O A. Reject the null O B. Fail to reject the null. O C. Undetermined or inconclusive.
Consider the model, Yt = Bo + B1 X1,1+B1 X2,t + Ut, and this is estimated using OLS with 350 observations. You run some tests with the following results: DWH fails to reject, BG fails to reject, and White test rejects. Select the combination of approaches for the most appropriate estimation of the coefficients. A. Use 2 SLS B. Heteroskedastic correction using X2,t only. c. Use Newey-West HAC. D. Use OLS. Heteroskedastic correction using X1,t only. F. Use White's heteroskedastic-consistent...
Consider the model, Yt = BO+B1 Xt + Ut, and this is estimated using OLS with 65 observations. However, it is suspected autocorrelation is present. You estimate the residuals (Uhatt) on the lag of residuals (Uhatt-1), Xt, and a constant. These estimation results are presented in the table below. Coefficient Std. Error Intercept Uhatt-1 Xt 0.006 0.052 0.004 0.051 0.002 0.001 0.004 R2 Adjusted-R2 0.003 Make your decision on autocorrelation and choose the most appropriate action from the responses. A....
Consider the model, Yt = BO+B1 Xt + Ut, and this is estimated using OLS with 65 observations. However, it is suspected autocorrelation is present. You estimate the residuals (Uhatt) on the lag of residuals (Uhatt-1), Xt, and a constant. These estimation results are presented in the table below. Std. Error Coefficient 0.006 0.004 Intercept Uhatt-1 Xt 0.052 0.051 0.001 0.002 0.004 R2 Adjusted-R2 0.003 Make your decision on autocorrelation and choose the most appropriate action from the responses. O...
Consider the model, Yt = BO+B1 Xt + Ut, and this is estimated using OLS with 65 observations. However, it is suspected autocorrelation is present. You estimate the residuals (Uhatt) on the lag of residuals (Uhatt-1), Xt, and a constant. These estimation results are presented in the table below. Coefficient Std. Error 0.824 Intercept Uhatt-1 Xt 10.412 0.198 0.325 0.052 0.064 Adjusted-R2 0.122 0.107 Make your decision on autocorrelation and choose the most appropriate action from the responses. A. Not...
Consider two models for the variable Yt: MA: Yt = Bo + B1 Yt-1 + Ut MB: Yt = A + A1 Yt-1 + A2 Xt + Vt While examining the prediction errors you obtain a Diebold-Mariano statistic of 1.78, what is your decision at the 10% significance level? A. Reject the null B. Fail to reject the null O C. Not enough information
Consider the model, Yt = 0.8 + 0.1 Yt-1 +0.5 X1,t + 1.7 X2,t + Ut. Complete the following table for the predicted values (2 decimals): 2018 2019 2020 2021 Year Yt 7.1 7.63 1.25 1.35 X1,5 KX2,0 1.30 1.9 1.40 2.4 2.5 3.20
Consider the model, Yi = Bo + B1 X1,1 + B2 X2,1 + Uj, where sorting the residuals based on the X1, and X2,i gives: X1 X2 SSE-F 13.7 67.2 SSE-L 85.2 52.0 Compute the Goldfeld-Quandt statistic and decide if there is heteroskedasticity present for either regressor at the 5% critical-F value of 1.313 A. Not enough information. B. Reject the null for X1, and fail to reject for X2. C. Reject the null for X1, and reject the null...
Table 4 Regression Model Y = α X1 + β X2 Parameter Estimates Coefficient Standard Error Constant 12.924 4.425 X1 -3.682 2.630 X2 45.216 12.560 Analysis of Variance Source of Degrees Sum of Mean Variation of Freedom Squares Square F Regression XXX 4,853 2,426.5 XXX Error XXX 485.3 Find above partial statistical output...