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, + B3mar,.. + B12dec, + µ; where totacc is the total number of accidents, t is time (measured in months), and feb,, mar;, dec̟
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, + B3mar,.. + B12dec, + µ; where totacc is the total number of accidents, t is time (measured in months), and feb,, mar;, dec̟
Chapter6: Exponential And Logarithmic Functions
Section6.8: Fitting Exponential Models To Data
Problem 2TI: Sales of a video game released in the year 2000 took off at first, but then steadily slowed as time...
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![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,.. +ß12dec, + 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:
Number of obs
F( 12,
Source
SS
df
MS
108
95)
31.06
Model
1.00244071
12
.083536726
Prob > F
0.0000
Residual
.255496765
95
.00268944
R-squared
Adj R-squared
0.7969
--+--
0.7712
Total
1.25793748
107
.011756425
Root MSE
%3D
.05186
ltotacc
Coef.
Std.
Err.
t
P>|t]
[95% Conf.
Interval]
.0027471
.0001611
17.06
0.000
.0024274
.0030669
.0244475
.0244491
feb
-.0426865
-1.75
0.084
-.0912208
.0058479
mar
.0798245
3.26
0.002
.031287
.1283621
аpr
.0184849
.0244517
0.76
0.452
-.030058
.0670277
.0320981
-.0164521
.0806483
may |
jun |
jul |
.0244554
1.31
0.193
.0201918
.0375826
.053983
.0244602
0.83
0.411
-.0283678
.0687515
.024466
1.54
0.128
-.0109886
.0861538
aug
.0244729
2.21
0.030
.0053981
.1025679
.042361
.0244809
1.73
0.087
-.0062397
.0909617
sep
oct
.0821135
.0244899
3.35
0.001
.0334949
.130732
.0244999
.0245111
nov
.0712785
2.91
0.005
.02264
.1199171
dec |
.0961572
3.92
0.000
.0474966
.1448178
cons
10.46857
.0190028
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 you the following guidelines:
(a) Is there a trend in total accidents?
(b) Is there seasonality in total accidents?
(c) Consider the following change in the time series model: yt = P1Yt-1 + Uz
where ut follows a white noise process. What is the condition we need to impose on p1 in order for
the series yt to be weakly stationary? Why?
Р.Т.О
Bo + B1xt-1 + B2Xt-2 + Ut
(d) Consider the following change in the time series model: y;
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.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fa7729c56-a4ea-400e-be4f-de25e10fb2f8%2F2cd035a1-47f9-4680-8e91-0034d66a9419%2Flbbx55e_processed.png&w=3840&q=75)
Transcribed Image Text: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,.. +ß12dec, + 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:
Number of obs
F( 12,
Source
SS
df
MS
108
95)
31.06
Model
1.00244071
12
.083536726
Prob > F
0.0000
Residual
.255496765
95
.00268944
R-squared
Adj R-squared
0.7969
--+--
0.7712
Total
1.25793748
107
.011756425
Root MSE
%3D
.05186
ltotacc
Coef.
Std.
Err.
t
P>|t]
[95% Conf.
Interval]
.0027471
.0001611
17.06
0.000
.0024274
.0030669
.0244475
.0244491
feb
-.0426865
-1.75
0.084
-.0912208
.0058479
mar
.0798245
3.26
0.002
.031287
.1283621
аpr
.0184849
.0244517
0.76
0.452
-.030058
.0670277
.0320981
-.0164521
.0806483
may |
jun |
jul |
.0244554
1.31
0.193
.0201918
.0375826
.053983
.0244602
0.83
0.411
-.0283678
.0687515
.024466
1.54
0.128
-.0109886
.0861538
aug
.0244729
2.21
0.030
.0053981
.1025679
.042361
.0244809
1.73
0.087
-.0062397
.0909617
sep
oct
.0821135
.0244899
3.35
0.001
.0334949
.130732
.0244999
.0245111
nov
.0712785
2.91
0.005
.02264
.1199171
dec |
.0961572
3.92
0.000
.0474966
.1448178
cons
10.46857
.0190028
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 you the following guidelines:
(a) Is there a trend in total accidents?
(b) Is there seasonality in total accidents?
(c) Consider the following change in the time series model: yt = P1Yt-1 + Uz
where ut follows a white noise process. What is the condition we need to impose on p1 in order for
the series yt to be weakly stationary? Why?
Р.Т.О
Bo + B1xt-1 + B2Xt-2 + Ut
(d) Consider the following change in the time series model: y;
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.
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