Consider the time series data for the real consumptian of hausehalds and government from year 1950 to year 2019, Five autoregressive models, AR(1) to AR(5), are estimated as follows, where the t statistics for the corresponding estimated coefficients are in the parentheses, and the size of the samples, n, used for each estimation is also shown in the table. Accarding to the significant lag approach, which model is the optimal under the 5% significance level? AR(1) 5854.66 AR(2) 5041.07 (2.05) 0.92 AR(3) AR(4) 5689.50 AR(5) 7681.51 5365.55 intercept t stat (2.44) (2.13) 0.92 (2.13) (2.82) rcont-1 0.82 0.92 0.92 t stat (315.53) (9.49) (9.41) (9.33) (9.57)
Consider the time series data for the real consumptian of hausehalds and government from year 1950 to year 2019, Five autoregressive models, AR(1) to AR(5), are estimated as follows, where the t statistics for the corresponding estimated coefficients are in the parentheses, and the size of the samples, n, used for each estimation is also shown in the table. Accarding to the significant lag approach, which model is the optimal under the 5% significance level? AR(1) 5854.66 AR(2) 5041.07 (2.05) 0.92 AR(3) AR(4) 5689.50 AR(5) 7681.51 5365.55 intercept t stat (2.44) (2.13) 0.92 (2.13) (2.82) rcont-1 0.82 0.92 0.92 t stat (315.53) (9.49) (9.41) (9.33) (9.57)
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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Problem 1P
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Question
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![Consider the time series data for the real consumption of households and government from year 1950 to year 2019. Five autoregressive models, AR(1) to AR(5), are estimated as follows,
where the t statistics for the corresponding estimated coefficients are in the parentheses, and the size of the samples, n, used for each estimation is also shown in the table.
According to the significant lag approach, which model is the optimal under the 5% significance level?
AR(1)
AR(2)
AR(3)
AR(4)
AR(5)
5365.55
7681.51
intercept
t stat
5854.66
5041.07
5689.50
(2.44)
(2.05)
(2.13)
0.92
(2.13)
(2.82)
rcont-1
0.82
0.92
0.92
0.92
t stat
(315.53)
(9.49)
(9.41)
(9.33)
(9.57)
-0.26
rcont-2
-0.18
-0.28
-0.27
t stat
(-1.38)
(-1.38)
(-1.31)
(-1.32)
rcon-s
0.08
0.02
0.07
t stat
(0.64)
(0.09)
(0.32)
rcont-4
0.06
-0.32
t stat
(0.38)
(-1.47)
rcont-s
0.34
t stat
(2.32)
65
65
65
65
65
AIC
18.30
18.30
18.33
18.36
18.29
BIC
18.37
18.40
18.46
18.52
18.50
O A. AR(2).
O B. AR(1).
O C. none.
D. AR(5).](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F945c3d61-62c1-43a6-abf3-40f8a98724de%2Fb7ebd317-39e5-4925-b69c-24c8f3ecf024%2For6f8dl_processed.png&w=3840&q=75)
Transcribed Image Text:Consider the time series data for the real consumption of households and government from year 1950 to year 2019. Five autoregressive models, AR(1) to AR(5), are estimated as follows,
where the t statistics for the corresponding estimated coefficients are in the parentheses, and the size of the samples, n, used for each estimation is also shown in the table.
According to the significant lag approach, which model is the optimal under the 5% significance level?
AR(1)
AR(2)
AR(3)
AR(4)
AR(5)
5365.55
7681.51
intercept
t stat
5854.66
5041.07
5689.50
(2.44)
(2.05)
(2.13)
0.92
(2.13)
(2.82)
rcont-1
0.82
0.92
0.92
0.92
t stat
(315.53)
(9.49)
(9.41)
(9.33)
(9.57)
-0.26
rcont-2
-0.18
-0.28
-0.27
t stat
(-1.38)
(-1.38)
(-1.31)
(-1.32)
rcon-s
0.08
0.02
0.07
t stat
(0.64)
(0.09)
(0.32)
rcont-4
0.06
-0.32
t stat
(0.38)
(-1.47)
rcont-s
0.34
t stat
(2.32)
65
65
65
65
65
AIC
18.30
18.30
18.33
18.36
18.29
BIC
18.37
18.40
18.46
18.52
18.50
O A. AR(2).
O B. AR(1).
O C. none.
D. AR(5).
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