(c) Consider the following change in the time series model: Ye = P1Yt-1+ U; where ut follows a white noise process. What is the condition we need to impose on øl in order for the series yt to be weakly stationary? Why? P.T.O (d) Consider the following change in the time series model: y, = Bo+ B1*t-1+ B2*t-2 + uz where y, is some outcome variable of interest, and x.-1 and x,-2 are strictly exogenous explanatory variables. How would you test for the presence of serial correlation in the residual u,? (e) Briefly explain how you would carry out econometric analysis of the model in (d) if u̟ is found to be stationary, but positively serially correlated.
(c) Consider the following change in the time series model: Ye = P1Yt-1+ U; where ut follows a white noise process. What is the condition we need to impose on øl in order for the series yt to be weakly stationary? Why? P.T.O (d) Consider the following change in the time series model: y, = Bo+ B1*t-1+ B2*t-2 + uz where y, is some outcome variable of interest, and x.-1 and x,-2 are strictly exogenous explanatory variables. How would you test for the presence of serial correlation in the residual u,? (e) Briefly explain how you would carry out econometric analysis of the model in (d) if u̟ is found to be stationary, but positively serially correlated.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
aSSIST WITH PART D
![Question 2 [3 marks each]
You are an econometrician working in the Ministry of Finance in Trinidad and Tobago and using
a database consisting of 108 monthly observations on automobile accidents for Trinidad and
Tobago between January 2011 and December 2019, you estimate the following model:
log( totacc,) = Bo + Bit+ B2feb, + Bzmar,+ B12dec, + µ.
where totacc is the total number of accidents, t is time (measured in months), and feb, mar, dec,
are dummy variables indicating whether time period t corresponds to the appropriate month.
You obtain the following OLS results:
Source I
df
MS
Number of obs =
108
F( 12,
95)
31.06
.083536726
.00268944
Model I
1.00244071
12
Prob > F
0.0000
Residual
.255496765
95
R-squared
Adj R-squared
0.7969
0.7712
Total |
1.25793748
107
.011756425
Root MSE
.05186
ltotacc I
Coef.
Std. Err.
P>|t|
[95% Conf. Interval]
.0027471
.0001611
17.06
0.000
.0024274
.0030669
.0058479
.1283621
feb I
.0244475
.0244491
-1.75
3.26
0.084
0.002
-.0426865
-.0912208
.031287
-.030058
-.0164521
-.0283678
mar
.0798245
apr I
.0184849
.0244517
0.76
0.452
.0670277
.0320981
.0201918
.0244554
.0806483
.0687515
1.31
0.193
may
jun I
jul
.0244602
0.83
0.411
0.128
0.030
-.0109886
.0053981
-.0062397
.0334949
.02264
.0375826
.024466
1.54
.0861538
.053983
.0244729
2.21
.1025679
aug I
sep I
oct |
nov |
.042361
.0821135
.0244809
.0244899
0.087
0.001
.0909617
.130732
1.73
3.35
.0712785
.0244999
2.91
0.005
.1199171
dec I
cons I
.0245111
.0190028
.0961572
3.92
0.000
.0474966
.1448178
10.46857
550.89
0.000
10.43084
10.50629
The team meeting will be held in 3 days from the date of the assignment and because of the
limitation of time the Chief economist has given vou the following guidelines:](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F4ad7f403-3734-417f-a950-2374ed67ff45%2Fbc403866-7768-4cd3-aa2b-481fffdecc3e%2F1fs0sq6_processed.png&w=3840&q=75)
Transcribed Image Text:Question 2 [3 marks each]
You are an econometrician working in the Ministry of Finance in Trinidad and Tobago and using
a database consisting of 108 monthly observations on automobile accidents for Trinidad and
Tobago between January 2011 and December 2019, you estimate the following model:
log( totacc,) = Bo + Bit+ B2feb, + Bzmar,+ B12dec, + µ.
where totacc is the total number of accidents, t is time (measured in months), and feb, mar, dec,
are dummy variables indicating whether time period t corresponds to the appropriate month.
You obtain the following OLS results:
Source I
df
MS
Number of obs =
108
F( 12,
95)
31.06
.083536726
.00268944
Model I
1.00244071
12
Prob > F
0.0000
Residual
.255496765
95
R-squared
Adj R-squared
0.7969
0.7712
Total |
1.25793748
107
.011756425
Root MSE
.05186
ltotacc I
Coef.
Std. Err.
P>|t|
[95% Conf. Interval]
.0027471
.0001611
17.06
0.000
.0024274
.0030669
.0058479
.1283621
feb I
.0244475
.0244491
-1.75
3.26
0.084
0.002
-.0426865
-.0912208
.031287
-.030058
-.0164521
-.0283678
mar
.0798245
apr I
.0184849
.0244517
0.76
0.452
.0670277
.0320981
.0201918
.0244554
.0806483
.0687515
1.31
0.193
may
jun I
jul
.0244602
0.83
0.411
0.128
0.030
-.0109886
.0053981
-.0062397
.0334949
.02264
.0375826
.024466
1.54
.0861538
.053983
.0244729
2.21
.1025679
aug I
sep I
oct |
nov |
.042361
.0821135
.0244809
.0244899
0.087
0.001
.0909617
.130732
1.73
3.35
.0712785
.0244999
2.91
0.005
.1199171
dec I
cons I
.0245111
.0190028
.0961572
3.92
0.000
.0474966
.1448178
10.46857
550.89
0.000
10.43084
10.50629
The team meeting will be held in 3 days from the date of the assignment and because of the
limitation of time the Chief economist has given vou the following guidelines:
Transcribed Image Text:(c) Consider the following change in the time series model: Ye = P1Yt-1 + U;
where ut follows a white noise process. What is the condition we need to impose on øl in order for
the series yt to be weakly stationary? Why?
P.T.O
(d) Consider the following change in the time series model: y: = Bo + B1×1-1 + B2x1-2 + Uz
where y, is some outcome variable of interest, and x,-1 and Xx-2 are strictly exogenous explanatory
variables. How would you test for the presence of serial correlation in the residual u;?
(e) Briefly explain how you would carry out econometric analysis of the model in (d) if u̟ is found
to be stationary, but positively serially correlated.
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