Question

TABLE 16-4                                                                                                                                                                                  Given below are EXCEL outputs for various estimated autoregressive models for Coca-Cola's real operating revenues (in billions of dollars) from 1975 to 1998. From the data, we also know that the real operating revenues for 1996, 1997, and 1998 are 11.7909, 11.7757 and, 11.5537, respectively.                                                                                                                                                                                                                         AR(1) Model:

	Coefficients	Standard Error	t Stat	P-value
Intercept	0.1802077	0.39797154	0.452815546	0.655325119
XLag1	1.011222533	0.049685158	20.35260757	0.643735615

AR(2) Model:

	Coefficients	Standard Error	t Stat	P-value
Intercept	0.30047473	0.4407641	0.681713257	0.503646149
X Lag 1	1.17322186	0.234737881	4.998008229	7.98541E-05
X Lag 2	-0.183028189	0.030716669	-0.730020026	0.034283347

AR(3) Model:

	Coefficients	Standard Error	t Stat	P-value
Intercept	0.313043288	0.514437257	0.608515972	0.550890271
XLag1	1.173719587	0.246490594	4.761721601	0.000180926
XLag2	-0.069378567	0.373086508	-0.185958391	0.004678245
XLag3	-0.122123515	0.282031297	-0.433014053	0.30448392

Referring to Table 16-4 and using a 5% level of significance, what is the model that uses the most lag variables?

options:

	linear
	AR(3)
	AR(1)
	AR(2)

Answer 1

ANSWER:

In statistics and econometrics, a distributed lag model is a model for time arrangement of series of data in which a regression condition is utilized to predict current estimations of a dependent variable dependent on both the current values of a explantory variable and thelagged (past period) estimations of this explanatory variable.

AR(3) utilizes the most lag factors