Financial econometrics assignment STATA data file containing computer generated

Financial econometrics assignment
STATA data file containing computer generated data on four
time series Xyz and w and a series newt which is a quarterly time trend running from
(l950:Ql to 1999:Q4). Assume that the series Xyz and W are already in logs.
1. Investigate the order of integration of the series Xyz and W paying particular
attention to the form of test regression that you use. (5 marks)
2. Produce dynamic forecasts of Xyz and w using an estimated VECM model for all 4
series using in-sample forecasting for the period 199oQl to 1999Q4. Now compare
the l-step ahead forecast performance of a univariate AR(2) model for AX With
forecasts of the same variable using an estimated VECM model for all 4 series using
in-sample forecasting for the period 199oQl to 1999Q4. What are the gains (if any) to
using the VECM compared to the simple time-series model for the series? Are these
gains What youd expect? (15 marks)
-14.50275993 -1.530043006 -8.122175217 -1.956660032 21916
-14.51187515 -2.24020648 -7.872457981 -2.225475073 21916
-14.39873505 -2.170288563 -8.007760048 -2.106750011 21916
-14.21742535 -2.33504653 -8.081435204 -2.103800058 21916
-13.92595482 -2.223700047 -8.117899895 -2.268074989 21916
-14.31774998 -2.319171429 -8.468085289 -2.166409969 21916
-14.37531471 -1.919851542 -8.609794617 -2.25970006 21916
-14.40717983 -1.881685972 -8.651804924 -2.263674974 21916
-14.96535969 -1.828351974 -8.532645226 -2.208344936 21916
-14.97742462 -1.900382519 -8.539195061 -2.400465012 21916
-14.68150043 -1.804103494 -8.550365448 -2.360929966 21916
-14.52728462 -1.635601044 -8.492400169 -2.484014988 21916
-14.36007023 -1.581733942 -8.43859005 -2.460340023 21916
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-13.48674488 -1.61788547 -8.391875267 -2.03847003 21916
-13.43309021 -1.036982536 -8.683679581 -1.696380019 21916
-13.98347473 -0.804214478 -8.661114693 -1.908929944 21916
-14.28096008 -1.27927351 -8.64124012 -2.148185015 21916
-13.74643517 -1.530099988 -8.713479996 -2.043070078 21916
-13.62243462 -1.153501511 -8.777874947 -1.863710046 21916
-13.30130005 -0.747189522 -8.697400093 -1.976979971 21916
-12.34585953 -0.934365511 -8.728010178 -1.950445056 21916
-12.10986519 -0.077505998 -8.694725037 -2.133305073 21916
-11.98903465 0.118291996 -8.517065048 -2.052289963 21916
-11.95732498 0.613966525 -8.461574554 -2.061115026 21916
-11.56301975 0.0049335 -8.508390427 -2.291244984 21916
-11.54570484 0.505329013 -8.517415047 -2.581259966 21916
-11.83208466 0.925090015 -8.481245041 -2.6026299 21916
-11.61533546 0.982654512 -8.419184685 -2.820110083 21916
-11.21407032 0.860624492 -8.746714592 -2.740225077 21916
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-11.34470463 1.193490982 -9.093870163 -2.629445076 21916
-11.66991997 1.486914992 -9.057519913 -2.770940065 21916
-12.1733799 1.402680516 -9.278835297 -2.715694904 21916
-12.7205801 1.122884989 -9.382684708 -2.776340008 21916
-13.21825027 0.892898023 -9.40694046 -2.859765053 21916
-12.32279491 1.005462527 -9.164500237 -3.144769907 21916
-11.53337955 0.977981985 -9.414520264 -3.215169907 21916
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-11.19622517 1.748541951 -9.245869637 -3.325944901 21916
-10.97433472 2.017317057 -9.508544922 -3.684220076 21916
-11.24654007 2.564915419 -9.541130066 -3.738549948 21916
-11.53732967 2.3910954 -9.531385422 -3.750794888 21916
-11.43966484 1.921355724 -9.542474747 -3.883285046 21916
-11.04923534 1.880594492 -9.715399742 -3.838745117 21916
-11.33345509 2.187336683 -9.769815445 -4.077040195 21916
-11.89272499 2.328834295 -9.914654732 -4.150519848 21916
-12.2502346 2.303031921 -10.21905994 -4.307720184 21916
-12.44336033 2.45124054 -10.33307457 -4.311620235 21916
-11.88719463 2.573405981 -10.29312992 -3.935719967 21916
-12.18930531 2.729248047 -10.39410496 -3.673295021 21916
-12.2712698 2.717103958 -10.1996851 -3.821799994 21916
-12.02319527 2.490706682 -10.13134956 -4.1208601 21916
-11.54183483 2.304426193 -10.12987995 -3.84572506 21916
-11.21432495 2.713939428 -10.16241455 -4.020370007 21916
-11.164505 2.732206583 -10.10571003 -4.054289818 21916
-11.04514027 2.665309429 -9.713299751 -4.463825226 21916
-10.87847042 2.917650461 -9.747989655 -4.435124874 21916
-10.40266991 2.560173035 -9.660059929 -4.598609924 21916
-9.819156647 2.83778739 -9.938830376 -4.526030064 21916
-9.412139893 3.123636961 -10.16888046 -4.714149952 21916
-10.04216003 3.838008404 -10.28798485 -4.781304836 21916
-10.87193489 4.079926491 -10.16073513 -4.87638998 21916
-10.96522522 3.435137033 -10.20765018 -4.776714802 21916
-10.99290466 3.435222387 -10.67338467 -4.872115135 21916
-11.28874016 3.264060974 -10.46401024 -4.896729946 21916
-11.79885483 3.068568468 -10.4486351 -4.998149872 21916
-11.98700047 2.744955063 -10.60925484 -4.603650093 21916
-11.75570488 2.5377841 -10.51073456 -4.509315014 21916
-11.72893524 2.456084251 -10.56671047 -5.127534866 21916
-11.86079979 2.994818926 -10.68785954 -5.340205193 21916
-12.15832996 2.569492102 -10.9416151 -5.255670071 21916
-12.73097992 2.688513994 -10.89244461 -5.272659779 21916
-13.033885 2.265777349 -10.83213043 -5.624314785 21916
-13.41753006 2.134600639 -10.72896957 -5.712214947 21916
-12.94369507 2.074098349 -10.72502995 -5.270094872 21916
-12.52295494 1.918574691 -10.71029472 -5.656485081 21916
-12.40931511 2.035866976 -10.70015526 -5.793324947 21916
-12.51047516 2.062902212 -10.50666523 -5.804944992 21916
-12.41860485 1.895998836 -10.50537968 -5.961909771 21916
-12.58934975 2.225399017 -10.37154961 -6.021170139 21916
-12.68080044 1.953127742 -10.61475468 -6.029365063 21916
-12.50860023 1.763038993 -10.47696495 -6.24766016 21916
-12.02228546 1.637994051 -10.59119034 -6.037284851 21916
-12.63133526 2.093744755 -10.61851501 -6.639699936 21916
-13.59306526 1.645311356 -10.63727474 -6.754930019 21916
-13.94768524 1.289034486 -10.36851501 -6.399525166 21916
-13.98962021 0.599886477 -10.27965546 -6.286439896 21916
-13.77252483 0.292036504 -10.53009033 -6.168990135 21916
-13.27772522 0.558386505 -10.68715 -6.316884995 21916
-13.09200001 0.845544994 -10.82756042 -6.296299934 21916
-13.3614254 1.467075944 -10.72666454 -6.299415112 21916
-13.81112957 1.662508368 -10.65903473 -6.494820118 21916
-13.79185963 1.150255442 -10.6905899 -6.713435173 21916
-13.61686039 1.015926003 -10.67636013 -6.532440186 21916
-12.93332958 0.988155007 -10.77767467 -6.749300003 21916
-12.63397503 1.616077065 -10.83244038 -7.159955025 21916
-12.71860504 1.300189495 -10.79197025 -6.955725193 21916
-13.28291512 1.600506783 -10.79763031 -7.072050095 21916
-13.97011471 1.748596311 -10.90295982 -7.014834881 21916
-13.8891449 1.479900479 -11.01177025 -6.885515213 21916
-13.52507496 1.272382498 -10.85929966 -6.550039768 21916
-13.41256523 1.489510059 -11.16617489 -6.543950081 21916
-13.39706039 1.988580346 -11.27548981 -6.436894894 21916
-13.25985527 2.157490492 -11.41565037 -6.448989868 21916
-13.45499516 1.999106765 -11.5374651 -6.311739922 21916
-13.59102535 2.209940195 -11.40021992 -6.490574837 21916
-13.60706043 2.031300545 -11.42119503 -6.431250095 21916
-13.29047966 2.120060444 -11.42660046 -6.331065178 21916
-12.58623505 2.06722784 -11.66902542 -6.134954929 21916
-12.5676651 2.757365942 -11.71399021 -6.242024899 21916
-12.54201508 2.839867115 -11.64962482 -6.423240185 21916
-12.70821953 3.049258471 -11.59998512 -6.640345097 21916
-13.32393456 2.828526974 -11.69707966 -6.801599979 21916
-13.66326046 2.629425049 -11.56006527 -6.784424782 21916
-13.44659519 2.183968306 -11.63582039 -6.726065159 21916
-12.88362026 2.194794416 -11.74656963 -7.029109955 21916
-12.61692524 2.575414419 -12.12300491 -7.249804974 21916
-12.85438538 3.073935986 -12.37464523 -7.523719788 21916
-13.63630009 2.891175985 -12.4198246 -7.399260044 21916
-14.4113102 2.62700057 -12.58207512 -7.812860012 21916
-14.68075466 2.245559692 -12.66868973 -8.012104988 21916
-14.2501297 1.732408643 -12.74992466 -8.071245193 21916
-13.86852455 2.201574802 -12.91090965 -7.967969894 21916
-13.59803486 2.684881926 -13.16371536 -8.320355415 21916
-13.94931984 3.291043997 -13.33870029 -8.292960167 21916
-14.65327454 3.473731995 -13.46380997 -8.254425049 21916
-14.70309544 2.746443033 -13.48496532 -8.325989723 21916
-14.88018513 3.061130047 -13.56507015 -8.365004539 21916
-14.77279472 2.875207424 -13.75070477 -8.357580185 21916
-14.47297478 2.734199047 -13.91025543 -8.547844887 21916
-14.8915596 3.108290911 -14.00408459 -8.571820259 21916
-15.09296513 3.739265919 -14.06859493 -8.648830414 21916
-14.81876469 3.13833189 -13.78799534 -8.722374916 21916
-14.59013462 2.739737511 -13.65747547 -8.884349823 21916
-14.61678982 3.120873928 -13.74734974 -9.048665047 21916
-14.63586998 2.924036503 -13.99579525 -9.067154884 21916
-14.88959026 3.352655411 -14.109025 -9.124239922 21916
-14.76233959 3.082838535 -14.09745026 -9.137610435 21916
-15.5117197 3.360997915 -13.98061466 -9.233869553 21916
-15.96217537 2.511845112 -14.17014503 -9.42501545 21916
-15.96761036 2.525684595 -14.13375473 -9.071805 21916
-15.90952015 2.25377059 -14.19648457 -9.245349884 21916
-16.21805954 2.255012989 -14.33847046 -8.997340202 21916
-16.14183044 2.567337513 -14.42492485 -8.975324631 21916
-16.17816925 2.691314936 -14.37465 -8.991595268 21916
-15.47197533 2.200330019 -14.55726528 -9.351984978 21916
-15.49816513 2.735842943 -14.48801517 -9.49971962 21916
-15.49243546 2.981419086 -14.66338539 -9.735549927 21916
-15.47832012 2.975399494 -14.71657467 -10.22439003 21916
-15.66546535 2.998600483 -14.74835014 -10.31649494 21916
-16.22135925 3.261654615 -14.83096504 -10.56303501 21916
-16.11400032 3.193254471 -14.8081646 -10.5499897 21916
-15.96037006 2.726021528 -15.14223957 -10.52075005 21916
-16.34767532 2.94787693 -15.18783474 -10.58094025 21916
-16.49354553 3.090965033 -15.3178854 -11.03339481 21916
-16.40084076 3.098115444 -15.41396999 -11.03685474 21916
-16.21368027 3.42512393 -15.51076508 -11.11798 21916
-15.82351017 3.378278494 -15.44626045 -11.27141953 21916
-16.45949936 3.639963388 -15.76350021 -11.49637032 21916
-17.37223053 3.410075426 -15.74147511 -10.92896461 21916
-17.38060951 2.812194586 -15.97737026 -10.96182537 21916
-17.06223488 2.881278515 -15.96596527 -11.26319504 21916
-17.05164528 3.2945714 -16.27394485 -11.46878529 21916
-16.81607056 3.281863928 -16.34477425 -11.61295033 21916
-16.89893913 3.508635044 -16.28767967 -11.55331516 21916
-16.74827576 3.188942909 -16.26112938 -11.37173462 21916
-16.97390938 3.319759607 -16.17493439 -11.3768549 21916
-16.91929054 3.177297115 -16.36886024 -11.33856964 21916
-17.04991913 3.250047445 -16.55858994 -11.22107029 21916
-17.39998436 3.798201561 -16.75901031 -11.51299953 21916
-17.45042419 3.430664539 -16.61301994 -11.47667027 21916
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-18.29009056 3.298737049 -17.08670044 -12.17086029 21916
-18.11500549 2.929902077 -17.2626152 -12.37281036 21916
-17.81833458 3.202302933 -17.46479416 -12.68584538 21916
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-17.98830986 3.543233395 -17.30346489 -13.13175011 21916
-17.61828423 3.470439911 -17.6169796 -13.25384045 21916
-17.40229034 4.007759094 -17.76651573 -13.177845 21916
-17.1594696 4.212805271 -17.70705986 -13.40844536 21916
-17.43223572 4.712456703 -17.70862579 -13.49577522 21916
-17.15363503 4.885770321 -17.92789459 -13.41543961 21916
-16.8275547 4.836814404 -18.0815506 -13.45600033 21916
-16.82766914 5.075069904 -18.2269249 -13.25494957 21916
-17.03525543 5.645431995 -18.01078987 -13.35538483 21916
-16.86375427 5.087794781 -17.97216988 -13.20510483 21916
-16.56526566 4.947042942 -18.27086067 -13.1584549 21916
-16.28609467 5.158928394 -18.29685974 -12.9480896 21916
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-17.28639984 5.225615501 -18.52788544 -12.83911037 21916
-17.61349487 5.171410084 -18.44223022 -12.81657982 21916
PLACE THIS ORDER OR A SIMILAR ORDER WITH US TODAY AND GET AN AMAZING DISCOUNT

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Financial econometrics assignment STATA data file containing computer generated

Financial econometrics assignment
STATA data file containing computer generated data on four
time series Xyz and w and a series newt which is a quarterly time trend running from
(l950:Ql to 1999:Q4). Assume that the series Xyz and W are already in logs.
1. Investigate the order of integration of the series Xyz and W paying particular
attention to the form of test regression that you use. (5 marks)
2. Produce dynamic forecasts of Xyz and w using an estimated VECM model for all 4
series using in-sample forecasting for the period 199oQl to 1999Q4. Now compare
the l-step ahead forecast performance of a univariate AR(2) model for AX With
forecasts of the same variable using an estimated VECM model for all 4 series using
in-sample forecasting for the period 199oQl to 1999Q4. What are the gains (if any) to
using the VECM compared to the simple time-series model for the series? Are these
gains What youd expect? (15 marks)
-14.50275993 -1.530043006 -8.122175217 -1.956660032 21916
-14.51187515 -2.24020648 -7.872457981 -2.225475073 21916
-14.39873505 -2.170288563 -8.007760048 -2.106750011 21916
-14.21742535 -2.33504653 -8.081435204 -2.103800058 21916
-13.92595482 -2.223700047 -8.117899895 -2.268074989 21916
-14.31774998 -2.319171429 -8.468085289 -2.166409969 21916
-14.37531471 -1.919851542 -8.609794617 -2.25970006 21916
-14.40717983 -1.881685972 -8.651804924 -2.263674974 21916
-14.96535969 -1.828351974 -8.532645226 -2.208344936 21916
-14.97742462 -1.900382519 -8.539195061 -2.400465012 21916
-14.68150043 -1.804103494 -8.550365448 -2.360929966 21916
-14.52728462 -1.635601044 -8.492400169 -2.484014988 21916
-14.36007023 -1.581733942 -8.43859005 -2.460340023 21916
-13.86470509 -1.80508399 -8.383595467 -2.51747489 21916
-13.85917473 -1.480389953 -8.277319908 -2.181479931 21916
-13.48674488 -1.61788547 -8.391875267 -2.03847003 21916
-13.43309021 -1.036982536 -8.683679581 -1.696380019 21916
-13.98347473 -0.804214478 -8.661114693 -1.908929944 21916
-14.28096008 -1.27927351 -8.64124012 -2.148185015 21916
-13.74643517 -1.530099988 -8.713479996 -2.043070078 21916
-13.62243462 -1.153501511 -8.777874947 -1.863710046 21916
-13.30130005 -0.747189522 -8.697400093 -1.976979971 21916
-12.34585953 -0.934365511 -8.728010178 -1.950445056 21916
-12.10986519 -0.077505998 -8.694725037 -2.133305073 21916
-11.98903465 0.118291996 -8.517065048 -2.052289963 21916
-11.95732498 0.613966525 -8.461574554 -2.061115026 21916
-11.56301975 0.0049335 -8.508390427 -2.291244984 21916
-11.54570484 0.505329013 -8.517415047 -2.581259966 21916
-11.83208466 0.925090015 -8.481245041 -2.6026299 21916
-11.61533546 0.982654512 -8.419184685 -2.820110083 21916
-11.21407032 0.860624492 -8.746714592 -2.740225077 21916
-11.20040035 1.4755795 -8.938360214 -2.781754971 21916
-11.34470463 1.193490982 -9.093870163 -2.629445076 21916
-11.66991997 1.486914992 -9.057519913 -2.770940065 21916
-12.1733799 1.402680516 -9.278835297 -2.715694904 21916
-12.7205801 1.122884989 -9.382684708 -2.776340008 21916
-13.21825027 0.892898023 -9.40694046 -2.859765053 21916
-12.32279491 1.005462527 -9.164500237 -3.144769907 21916
-11.53337955 0.977981985 -9.414520264 -3.215169907 21916
-11.09825516 1.236968517 -9.324515343 -3.191304922 21916
-11.19622517 1.748541951 -9.245869637 -3.325944901 21916
-10.97433472 2.017317057 -9.508544922 -3.684220076 21916
-11.24654007 2.564915419 -9.541130066 -3.738549948 21916
-11.53732967 2.3910954 -9.531385422 -3.750794888 21916
-11.43966484 1.921355724 -9.542474747 -3.883285046 21916
-11.04923534 1.880594492 -9.715399742 -3.838745117 21916
-11.33345509 2.187336683 -9.769815445 -4.077040195 21916
-11.89272499 2.328834295 -9.914654732 -4.150519848 21916
-12.2502346 2.303031921 -10.21905994 -4.307720184 21916
-12.44336033 2.45124054 -10.33307457 -4.311620235 21916
-11.88719463 2.573405981 -10.29312992 -3.935719967 21916
-12.18930531 2.729248047 -10.39410496 -3.673295021 21916
-12.2712698 2.717103958 -10.1996851 -3.821799994 21916
-12.02319527 2.490706682 -10.13134956 -4.1208601 21916
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