Consider the time series data for the real consumption of households and government from year 1950 to year 2019. Five autoregressive m estimated as follows, where the t statistics for the corresponding estimated coefficients are in the parentheses, and the size of the samples also shown in the table. According to the AIC approach, is the optimal, and according to the BIC approach,_ is the optimal. AR(1) AR(2) AR(3) AR(4) AR(5) intercept 5854.66 5041.07 5365.55 5689.50 7681.51 (2.44) (2.05) (2.13) (2.13) (2.82) t stat 0.82 0.92 0.92 0.92 0.92 rcon;-1 t stat (315.53) (9.49) (9.41) (9.33) (9.57) -0.18 -0.28 -0.27 -0.26 rcon;-2 t stat (-1.38) (-1.38) (-1.31) (-1.32) rcon-3 0.08 0.02 0.07 t stat (0.64) (0.09) (0.32)

Linear Algebra: A Modern Introduction
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Chapter4: Eigenvalues And Eigenvectors
Section4.6: Applications And The Perron-frobenius Theorem
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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 AIC approach,
is the optimal, and according to the BIC approach,_ is the optimal.
AR(1)
AR(2)
AR(3)
AR(4)
AR(5)
intercept
5854.66
5041.07
5365.55
5689.50
7681.51
t stat
(2.44)
(2.05)
(2.13)
(2.13)
(2.82)
rcon-1
0.82
0.92
0.92
0.92
0.92
t stat
(315.53)
(9.49)
(9.41)
(9.33)
(9.57)
rcont-2
-0.18
-0.28
-0.27
-0.26
t stat
(-1.38)
(-1.38)
(-1.31)
(-1.32)
rcont-3
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)
rconţ-5
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(4); AR(1).
O B. AR(4); AR(4).
O C. AR(5); AR(1).
O D. AR(5); AR(4).
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 AIC approach, is the optimal, and according to the BIC approach,_ is the optimal. AR(1) AR(2) AR(3) AR(4) AR(5) intercept 5854.66 5041.07 5365.55 5689.50 7681.51 t stat (2.44) (2.05) (2.13) (2.13) (2.82) rcon-1 0.82 0.92 0.92 0.92 0.92 t stat (315.53) (9.49) (9.41) (9.33) (9.57) rcont-2 -0.18 -0.28 -0.27 -0.26 t stat (-1.38) (-1.38) (-1.31) (-1.32) rcont-3 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) rconţ-5 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(4); AR(1). O B. AR(4); AR(4). O C. AR(5); AR(1). O D. AR(5); AR(4).
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