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Res Ec 797A Lecture 23 Fall 2011

Res Ec 797A Lecture 23 Fall 2011 - Source BM BM BM BM...

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Unformatted text preview: Source BM BM BM BM Enders BM BM Pagegsl Res Ec 797A: Forecasting Order Of Topics And Readings For Lecture 23 (November 23, 2011) Topic Material Distributed Last Class But Not Covered And Amendments To The Way We Will Cover This Material Today handwritten and unnumbered l a new single page 264-272 92 93-100 Unaugmented restricted model vs. unaugmented unrestricted model SAS: Applying Elder and Kennedy’s Testing Strategy for Case 1 . Pay attention to the Proc Reg/Model statements that are “blocked off.” In particular, notice that I have included an RSS value for each model as a comment. . I will not pay attention to the %DFTEST model statements that are “blocked off” at the bottom of this page. F-test results for my unaugmented model and for my augmented model, using RSS information from SAS runs on previous page . I will not cover this page. I do not like this page. I really did not present or promote a testing strategy for the C series. Case 1 Example Using The Consumption Series (C) . This page replaces the previous page. We present a testing strategy that has two stages. . Stage 1: Test if C is difference-stationary or trend—stationary. No detenninistic regressors are included in the models representing each situation; i.e, no augmentations. .Stage 2: Take the “winner” from Stage 1. Augment this model with deterministic regressors. Test the “winner" from Stage 1 against the augmented model. VectorAutoregression (VAR) Introduction to Vector Autoregression (VAR) Transition from Enders’ coverage of VAR on pages 264-266 and 269-270. I have a complete example using two variables: . Y: Gross Domestic Product (GDP) . X: Investment Mechanics of VAR systems using Y and X above LOSE 1- EXQMFK U5M3 The, Cansu-MPjr-iovx Series (C) lSJYG-‘O‘ifi ill Bfitsm \Nfo tME‘vcj {Till-WV] (fl-.22) 3 N0 DEJFQ-rmim‘jjf'm Rejvsfifio‘fatUWGUfijmqefl'T94 HU' 9:1 (AM [310 I) C has a WW YodT WW de I LUV, C is chflevence S’Tofiomcwy') HA: Pii (and PJ*O 3? C ks STdHonmy (Avo‘md a dgferminld’jg TYQHOL (or C is Trend {fin-T WWW ) HD UMPOSES TWO ‘FCSinCTiO‘WS OY“ fl‘IC mock} This WI!” VCsv’H’ .m (A Rfisid‘vM Sum 01C Salumegjkesfhcjcd :7 RSSR HA \mwges W) YESTY‘icj‘i'm/xs 0m Th6 Model Th5 Wm “5”“ i“ 0‘ R2 5~\C\W~\l SUM 0" Stitit:l.re‘3, UI‘WCSJWWMIA :7 [690 F0? oo‘wxwaénfi‘aa Each is a R38 {or (m unavajmenked made], ‘F 5Jm+|sfici F :(KSSR-RESU)/(cth—<HU) nmgb—Msgfl/z 2902/2 257 293mm ’ HEM/Maw— 3) 395.03 ' Fad To Rejed‘ Ht, Hm’ YESJYHcTionS WM, (1 “NWT rejeofHa “0an C is dijf‘gevewoe s'tcfil'di) ' I do mo’r have evudemfi ThaT C ‘5 31—0130ny around a defermifiifi"; Wand, ' SO) Unqufimevfiea) resfric’fid maskd WMS 00* Ovef unauamenfcd) Unresi'vic’yed madtl ‘ 81338 2 ‘; 3W3 m WWW-r 9m 8m 5 i: AskaWikl m’fioo‘vc‘Tion «F daemmm ragvessavs make”? Jrhe “umT YooJT Conclusionnwtrom S+aje l ? bk: Resldtua‘ Sam of SanvCS Rom unquflmervkcok Yegfificlfe,‘ mm] above; RSSK Run a new mode.\ 6 Same argumevfis as reswdxea model above bu+ wH‘k ‘mdugion 0—; defiewrmm's’m. ve “25508, Le} aaeflicien’ss be chM 0’; (7.6+ Residual Sum 01: Blames :Svom Jrhis WSWW‘EA vefiric’reok mac) : KSSU H -. d‘: 62 = d}: 6+ = 0 A. Nady accoUVficd wcor 'mThe unaugwn’sed refividéd ma’rdaw O War WWW WC °\VC<10“/ have R851.) m, ‘ans i‘m “ms The vesfiided‘ made) above, buf HA: one or more (Ii-O a A a ‘ amah‘ffi \M h We dc’tevanisfic Yezsvessors. CM! F SW'xs’fici [3 F __ (Essa—Essa/(MK-Afi) W_ 3320/4 _ 83 _ ‘5 RssU/OHU " 7%9uU/(Bv—S~5) ’ mos ' 587.03 ' 'Fd’il To Rejegg Ho was; ma coegficienJVs en fin dfi’kvmmis’kic Yagvessore eiual mm. ‘1 d0 00+ \naxlfl avidemcc +ha‘s” The ddcwfims’hc rcojrbsvvs (3.67 AR Variabhas) below/w) i“ ‘HAC ”0:181. _ I‘M“: m evideme Gamma-3+ Jflnig behn a okéfievemc sjfaflwaw/ 13,0333 V \ C \UéO“ . - oveak W. \ mm“ m “gm due To ervodu’inon a {M A2 Vavaabmg, ...
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Res Ec 797A Lecture 23 Fall 2011 - Source BM BM BM BM...

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