You are an econometrician working in the Ministry of Finance in Trinidad and Tobago and usinga database consisting of 108 monthly observations on automobile accidents for Trinidad andTobago between January 2011 and December 2019, you estimate the following model:log( totacc,) = Bo + Bit+ B2feb, + Bzmar. + B12 dec, + 4,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:10831.060.0000Source IdfMSNumber of obsF( 12,Prob > FR-squaredAdj R-squared = 0.771295)Model IResidual I1.0024407112.083536726.25549676595.002689440.7969Total |1.25793748107.011756425Root MSE.05186ltotacc ICoef.Std. Err.P>It|[95% Conf. Interval]t.0027471.000161117.06-1.753.260.000.0024274.00306690.0840.002.0058479.1283621-.0912208.031287-.030058-.0164521-.0283678-.0109886.0053981-.0062397feb |-.0426865.0798245mar |apr |.0244475.0244491.0244517.0244554.0184849.0320981.02019180.760.452.06702771.310.193.0806483may Ijun Ijul Iaug |sep Ioct |nov |dec I_cons I.02446020.830.411.0687515.0861538.1025679.0909617.0375826.0244661.540.1280.0300.0870.0010.005.053983.02447292.211.733.352.91.042361.02448090821135.0712785.096157210.46857.0244899.0244999.0245111.0190028.0334949.02264.047496610.43084.130732.1199171.144817810.506293.920.000550.890.000The team meeting will be held in 3 days from the date of the assignment and because of thelimitation 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 + Ugwhere ut follows a white noise process. What is the condition we need to impose on øl in order forthe series yt to be weakly stationary? Why?P.T.O(d) Consider the following change in the time series model: y, = Bo + B1x1-1 + B2x-2 + u,where y, is some outcome variable of interest, and x,-1 and x1-2 are strictly exogenous explanatoryvariables. 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 u, is foundto be stationary, but positively serially correlated.

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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, + B3mar. + B12 dec, + 4. 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 ss df MS Number of obs - L08 F( 12, Prob > F R-squared Adj R-squared - 0.7712 Root MSE 95) - 31.06 0.0000 Model I 1.00244071 Residual I .255496765 12 .083536726 .00268944 95 0.7969 Total I 1.25793748 107 .011756425 .05186 ltotacc | Coef. Std. Err. t P>It| [958 Conf. Interval] .0027471 -.0426865 .0798245 .0001611 .0244475 .0244491 17.06 0.000 .0024274 -.0912208 .0030669 .0058479 .1283621 .0670277 .0806483 .0687515 .0861538 .1025679 .0909617 feb | -1.75 0.084 0.002 0.452 3.26 .031287 -.030058 mar | 0.76 1.31 0.83 apr | .0184849 .0244517 .0320981 .0201918 .0375826 .053983 .0244554 .0244602 .024466 .0244729 .0244809 -.0164521 -.0283678 -.0109886 .0053981 -.0062397 0.193 may I jun I jul I aug I sep I oct I nov | dec I 0.411 1.54 0.128 0.030 0.087 2.21 .042361 1.73 3.35 2.91 3.92 550.89 .0821135 .0244899 0.001 .0334949 .130732 .0712785 .0244999 0.005 .02264 .1199171 .0961572 .0245111 0.000 .0474966 .1448178 _cons | 10.46857 .0190028 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: ye = P1Yt-1+ Ug where ut follows a white noise process. What is the condition we need to impose on pl 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 + B1xt-1+ B2x1-2 + U where y, is some outcome variable of interest, and x-1 and x4-2 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 u, is found to be stationary, but positively serially correlated.