Given: WHY_t = 2.00 - 0.15 EX_t + 0.70 WHY_t-1 + e_t
(0.10) (0.35) (0.10) $\leftarrow$ standard errors
R²= .9 DW = 2.10 n = 26
A) What is the short run impact of EX on WHY
B) What is the long run impact of EX on WHY?
C) Does this regression suffer from serial correlation according to Durbin's h-test? Show the 5-step procedure.
$h = \left( 1 - \frac { d } { 2 } \right) \sqrt { \frac { n } { 1 - n \left( \operatorname { var } \left( \hat { \beta } ^ { } \right) \right) } }$

Question

Given: WHY_t = 2.00 - 0.15 EX_t + 0.70 WHY_t-1 + e_t
(0.10) (0.35) (0.10)

\leftarrow

standard errors
R²= .9 DW = 2.10 n = 26
A) What is the short run impact of EX on WHY
B) What is the long run impact of EX on WHY?
C) Does this regression suffer from serial correlation according to Durbin's h-test? Show the 5-step procedure.

h = \left( 1 - \frac { d } { 2 } \right) \sqrt { \frac { n } { 1 - n \left( \operatorname { var } \left( \hat { \beta } ^ { } \right) \right) } }

Quizplus · Accepted Answer

The Answer is A) -0.15
B) -.15/(1-.70) = -.5
C) 1)Ho: no autocorrelation Ha: auto present
2)5%
3)If

ext{ extbar}

h

ext{ extbar}

> t^c, then reject Ho (d.f. = n-k)
4)h = -0.3 ; t^c = 2.069 (d.f. = 26 - 3 = 23)
5)Do not reject Ho; no auto present

h = \left( 1 - \frac { d } { 2 } ight) \sqrt { \frac { n } { 1 - n ( \operatorname { var } ( \hat { \beta } ) ) } } \quad h = \left( 1 - \frac { 2.10 } { 2 } ight) \sqrt { \frac { 26 } { 1 - 26 \left( 0.1 ^ { 2 } ight) } } = - .05 ( 5.93 ) = - 0.3

Given: WHYt = 2 ←\leftarrow← Standard Errors R2 = h=(1−d2)n1−n(var⁡(β^))h = \left( 1 - \frac { d } { 2 } \right) \sqrt { \frac { n } { 1 - n \left( \operatorname { var } \left( \hat { \beta } ^ { } \right) \right) } }h=(1−2d​)1−n(var(β^​))n​​

Given: WHY_t = 2 $\leftarrow$ Standard Errors
R²= $h = \left( 1 - \frac { d } { 2 } \right) \sqrt { \frac { n } { 1 - n \left( \operatorname { var } \left( \hat { \beta } ^ { } \right) \right) } }$