View Proc Object Print Name [Freeze Estimate Stats impu Vector Autoregression Es
ID: 3375902 • Letter: V
Question
View Proc Object Print Name [Freeze Estimate Stats impu Vector Autoregression Estimates Vector Autoregression Estimates Date: 04/27/18 Time: 09:10 Sample (adjusted) 1908 1960 Included observations: 53 after adjustments Standard errors in ()&t-statistics; in [l LA LS LA(-1) 0.315866 -0.202275 (0.14791) (0.08497) 12.13548) 2.38050] LS(-1) 0.639970 .117439 (0.16519) (0.09489) 3.87421 11.7755) 0.140907 0.498610 (0.61022) (0.35055) -0.23091 1.422351 VAR Lag Order Selection Criteria Endogenous variables: LA LS Exogenous variables: C Date: 04/27/18 Time: 09.11 Sample: 1907 1960 Included observations: 49 LogL LR AIC SC NA 0.003791 0.100595 0.177812 0.129891 -0.464583 54.60820 103.4020 0000472 1.984008 -1.752357 -1.896120 60.70597 10.95109 0.000433 -20696311.683546 -1.923151 64 40162 6.335403 0.000440 2.057209 1.518689 -1852136 69.07067 7.622947 0.000430 2.084517 1.389563 -1.820853 71.45977 3705535 0.000462 2.018766 -1.169377 Response to Cholesky One S.D. Innovations +2 S.E Response of LA to LA Response of LA to LS 20- 15 10 05 00 - 9 10 1 2 34 66 789 10 Response of LS to LA Response of LS to LS 1 23 456T 89 10Explanation / Answer
1. As we can see from the above data that lag order of 1 is used in the chart and it significantly tells that the respond of LA to LS is much lower than the responds of LS to LA which is 3.87241. So a lag order of 2 or more than that could be use to define the change in value of correlation .
2.
From the above data chart we can say that advertising is positively correlated to the sales over time as the time lag over the period shows an increase in coefficient factor over the time.
3.
From the graph we can state that sales response is decreasing in manner with respect to the advertising over the time period, as the line in the graph decreasing significantly.
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