MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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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:
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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:
(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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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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