1. jpsuzuki is the monthly production of cars at a Suzuki plant. A researcher runs the following regression on D. jpsuzukit = (jpsuzuki- jpsuzuki t_1). z(t) D.jpsuzuki Test Statistic jpsuzuki L1. _trend _cons -11.368 1% Critical Value Mackinnon approximate p-value for Z(t) = 0.0000 -3.990 Coef. Std. Err. -.6799044 .059806 157.0193 14.79308 3636.04 840.3144 5% Critical Value t P>|t| -3.430 10% Critic. Value -3.13 [95% Conf. Interval -11.37 0.000 -.7976899 -.56211: 10.61 0.000 186.15) 127.8849 4.33 0.000 1981.074 5291.0 jpsuzuki L1 represents the first lag of jpsuzuki. What is the above regression testing? State the model being estimated and show the null and alternative hypothesis being tested. Interpret whether the software output provides evidence for or against your null hypothesis. Given your conclusion above, what would be needed to make the series stationary? Are there any potential issues with the model used to test your hypothesis above that could invalidate your conclusions? If so, what should be done to obtain reliable inferences? Discuss with reference to alternative tests.

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1. jpsuzuki is the monthly production of cars at a Suzuki plant.
A researcher runs the following regression on D. jpsuzukit
(jpsuzuki- jpsuzuki t_1).
Z(t)
D.jpsuzuki
Test
Statistic
jpsuzuki
L1.
_trend
_cons
-11.368
1% Critical
Value
MacKinnon approximate p-value for Z(t) = 0.0000
-3.990
Coef. Std. Err.
-.6799044
.059806
157.0193
14.79308
3636.04 840.3144
5% Critical
Value
t P>|t|
-11.37 0.000
10.61 0.000
4.33 0.000
-3.430
=
10% Critica
Value
-3.13
[95% Conf. Interval
-.7976899 -.562118
127.8849
186.153
1981.074
5291.00
jpsuzuki L1 represents the first lag of jpsuzuki.
What is the above regression testing? State the model being
estimated and show the null and alternative hypothesis being tested.
Interpret whether the software output provides evidence for or
against your null hypothesis.
Given your conclusion above, what would be needed to make the
series stationary?
Are there any potential issues with the model used to test your
hypothesis above that could invalidate your conclusions? If so, what
should be done to obtain reliable inferences? Discuss with reference
to alternative tests.
Transcribed Image Text:1. jpsuzuki is the monthly production of cars at a Suzuki plant. A researcher runs the following regression on D. jpsuzukit (jpsuzuki- jpsuzuki t_1). Z(t) D.jpsuzuki Test Statistic jpsuzuki L1. _trend _cons -11.368 1% Critical Value MacKinnon approximate p-value for Z(t) = 0.0000 -3.990 Coef. Std. Err. -.6799044 .059806 157.0193 14.79308 3636.04 840.3144 5% Critical Value t P>|t| -11.37 0.000 10.61 0.000 4.33 0.000 -3.430 = 10% Critica Value -3.13 [95% Conf. Interval -.7976899 -.562118 127.8849 186.153 1981.074 5291.00 jpsuzuki L1 represents the first lag of jpsuzuki. What is the above regression testing? State the model being estimated and show the null and alternative hypothesis being tested. Interpret whether the software output provides evidence for or against your null hypothesis. Given your conclusion above, what would be needed to make the series stationary? Are there any potential issues with the model used to test your hypothesis above that could invalidate your conclusions? If so, what should be done to obtain reliable inferences? Discuss with reference to alternative tests.
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