Short run and alternative macroeconomic forecasts for Romania and strategies to improve their

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1 Mihel Bru, Eri Mri Shor ru d lerive mroeoomi foress for omi d sregies o improve heir, Jourl of Ieriol Sudies, Vol. 5, No, 0, pp Shor ru d lerive mroeoomi foress for omi d sregies o improve heir Jourl of Ieriol Sudies Foudio of Ieriol Sudies, 0 CS, 0 Sieifi Ppers Mihel Bru Eri Mri Ademy of Eoomi Sudies Buhres Fuly of Cyereis, Sisis d Eoomi Iformis 6, Pi om, s disri 00374,, omi Asr. For he sme mroeoomi vriles more prediios e mde, usig differe foresig foresig. The mos impor sep is he hoie of he prediio wih he highes degree of ury, his eig used i eslishig he govermel poliies or he moery poliy y he erl. We mde shor ru foress Jury 0-Mrh 0 for vriles s iflio re, uemployme re d ieres re for omi usig ehiques lie: eoomeri modelig, expoeil smoohig ehique d movig verge mehod. I order o improve he foress ury, we used wo empiril sregies: mig omied progosis d uildig he foress sed o hisoril ury idiors. The prediios sed o expoeil smoohig ehique hve he highes degree of ury, eig superior o hose go pplyig he sregies of improvig he ury. eeived: Mrh, 0 s evisio: Jue, 0 Aeped: Ooer, 0 Keywords: foress, ury, eoomeri models, smoohig expoeil ehiques. JEL Clssifiio: E, E7,C5, C53. INTODUCTION There re my quiive mehods used o uild foress, wo of he mos populr eig he eoomeri models d he expoeil smoohig d movig verge ehiques. These e used o develop lerive prediios for he sme vrile. We hose he es prediio usig he ury idiors. Some empiril sregies ould e used o improve he ury, heir effeiveess eig i relio o he priulr d. Mig empiril reserhes for USA, Bru 0 showed h he es sregy for he ury imporoveme is eepig os he hisoril errors. This sregy geered he es resuls lso for omi, u i does o exeed he performe of expoeil smoohig ehiques. 30

2 Mihel Bru, Eri Mri Shor ru d lerive mroeoomi foress for omi d sregies o improve heir FOECASTS ACCUACY IN LITEATUE Fores ury is lrge hper i he lierure reled o he evluio of foress ueriy. There re wo mehods used i omprig he prediio quliy : veril mehods eg, me squred error d horizol mehods suh s dise i ime. A exhusive preseio of he prolem ig io ou ll he hievemes i lierure is o possile, u will oulie some impor olusios. I order o evlue he fores performe, d lso o order he prediios, sisiis hve developed severl mesures of ury. Fildes. d Seler 000 lyzed he prolem of ury usig sisis, idiig ldmrs i he lierure. For ompriso ewee he MSE idiors of he foress, Grger d Newold propose sisi. Aoher sisi is preseed y Dieold d Mrio i order o ompre oher quiive mesures of errors. Dieold d Mrio were proposed i 995 ompriso es of wo fores s ury uder he ull hypohesis h ses he l of differee. The es proposed y hem ws ler improved y Hrvey d Ashley, who developed ew sisi sed o oosrp iferee. Ler, Chrisofferse d Dieold hve developed ew wy of mesurig he ury h eeps he oiegrio relioship ewee vriles. Armsrog d Fildes 995 shows h he purpose of mesurig fores error is he provisio of iformio ou he shpe of errors disriuio d proposed loss fuio for mesurig he fores error. Armsrog d Fildes show h i is o suffiie o use sigle mesure of ury. Mrio.S. 000 preses he mos sigifi ess of foress ury, iludig he hges of his es- Dieold Mrio DM. Sie he orml disriuio is poor pproximio of he disriuio of low volume d series, Hrvey, Leyoure, d Newold improve he properies of fiie d ses, pplyig some orreios: he hge of DM sisis i order o elimie he is d o me ompriso o o orml disriuio, u o he -Sude. Clr evlues he power of some ess of equl fores ury, suh s modified versios of DM es or hose of Newey d Wes, whih re sed o he Brle erel d fixed legh of d series. Meese d ogoff i heir sudy from 983, The empiril exhge re models of he seveies ompred he MSE d he is of exhge re foress, h were sed o sruurl models d hey mde olusio h ws ler used o improve mroeoomi foress performe. They hve hus demosred h rdom wl proess geeres eer foress h sruurl models. I lierure, here re severl rdiiol wys of mesureme, whih e red ordig o he depedee or idepedee of mesureme sle. A omplee lssifiio is mde y J Hydm d AB Koehler 005 i heir referee sudy i he field, Aoher Loo Mesures of Fores Aury. I prie, he mos used mesures of fores error re: oo Me Squred Error MSE Me error ME MSE = e T + j, X j = 0 ME = e T + j, X j = The sig of idior vlue provides impor iformio: if i hs posiive vlue, he he urre vlue of he vrile ws uderesimed, whih mes expeed verge vlues oo smll. A egive vlue of he idior shows expeed vlues oo high o verge. 0 3

3 Jourl of Ieriol Sudies Vol. 5, No., 0 Me solue error MAE MAE = e T + j, j = ee sudies rge ury lysis usig s ompriso rierio differe models used i mig prediios or he lysis of foresed vlues for he sme mroeoomi idiors regisered i severl ouries. T. Teräsvir, v Dij D., Medeiros MC 005 exmie he ury of foress sed o lier uoregressive models, uoregressive wih smooh rsiio STA d eurl ewors eurl ewor- NN ime series for 47 mohs of he mroeoomi vriles of G7 eoomies. For eh model is used dymi speifiio d i is showed h STA models geere eer foress h lier uoregressive oes. Neurl ewors over log horizo fores geere eer prediios h he models usig pproh from prive o geerl. U. Heilem d Seler H. 007 expli why mroeoomi fores ury i he ls 50 yers i G7 hs o improved. The firs explio refers o he rii rough o mroeoomeris models d o foresig models, d he seod oe is reled o he urelisi expeios of fores ury. Prolems reled o he foress is, d quliy, he fores proess, predied idiors, he relioship ewee fores ury d fores horizo re lyzed. uh K. 008, usig he empiril sudies, oied foress wih higher degree of ury for Europe mroeoomi vriles y omiig speifi su-groups prediios i ompriso wih foress sed o sigle model for he whole Uio. Gorr WL 009 showed h he uivrie mehod of prediio is suile for orml odiios of foresig while usig oveiol mesures for ury, u mulivrie models re reommeded for prediig exepiol odiios whe OC urve is used o mesure ury. Dover J. d J. Weisser 0 used rod se of idividul foress o lyze four mroeoomi vriles i G7 ouries. Alyzig ury, is d foress effiiey, resuled lrge disrepies ewee ouries d lso i he sme oury for differe vriles. I geerl, he foress re ised d oly frio of GDP foress re loser o he resuls regisered i reliy. X 0 THE ACCUACY EVALUATION OF THE MACOECONOMIC FOECASTS BASED ON ECONOMETIC MODELS The vriles used i models re: he iflio re luled srig from he hrmoized idex of osumer pries, uemployme re i BIM pproh d ieres re o shor erm. The ls idior is luled s verge of dily vlues of ieres res o he mre. The d series for he omi eoomy re mohly oes d hey re e from Euros wesie for he period from ferury 999 o deemer 0. The idiors re expressed i omprle pries, he referee se eig he vlues from jury 999. Afer pplyig he ADF es Augmeed Diey-Fuller es for, d 4 lgs, we go h ieres re series is siory, while he iflio re deoed ri d he uemployme re deoed rs series hve oe sigle ui roo eh of hem. I order o siorize he d we differeed he series, rezulig siory d series: ri = ri ri rs = rs rs 3

4 Mihel Bru, Eri Mri Shor ru d lerive mroeoomi foress for omi d sregies o improve heir Tig io ou h our ojeive is he hieveme of oe-moh-hed foress for Jury, Ferury d Mrh 0, we osidered eessry o upde he models. We used wo ypes of models: VA model, AMA oe d model i whih iflio d ieres re re explied usig vriles wih lg. The models for eh lyzed period re show i he Aex. We developed oe-moh-hed foress srig from hese models, he we evlued heir ury. U Theil s sisi is luled i wo vris y he Ausrli Tresorery i order o evlue he foress ury. The followig oios re used: - he regisered resuls p- he predied resuls - referee ime e- he error e=-p - umer of ime periods U = = = p + = The more loser of oe is U, he foress ury is higher. f U = = = f If U ==> here re o differees i erms of ury ewee he wo foress o ompre If U <=> he fores o ompre hs higher degree of ury h he ive oe If U >=> he fores o ompre hs lower degree of ury h he ive oe Idiors of iflio foress ury for Jury 0- Mrh 0 Tle Iflio re Models used o uild he foress Idiors of ury VA AMA Models wih lgged vriles MSM 0, ,934 0,67367 ME -0,997-0,97-0,090 MAE 0,660 0,873 0,893 MPE -0,007-0,0030-0,007 U 0, , ,00567 U 0,7587,6447,76748 Soure: ow lulios usig Exel. 33

5 Jourl of Ieriol Sudies Vol. 5, No., 0 All hese models ed o overesime he predied vlues of he iflio re o he foress horizo. The prediios of iflio sed o models wih lgged vriles hve he eer ury, he vlue lose o zero for U sisi vlidig his olusio, lso lie oher ury idiors wih smll vlues suh s ME d MPE. How U sisi of Theil is more h for ll oe-sep-hed foress, exepig hose sed o VA model, he ïve prediios re more ure h hose sed o AMA models or hose wih lgs for iflio re. Tle Idiors of foress ury for he uemployme re for Jury 0- Mrh 0 Uemployme re Models used o uild he foress Idiors of ury VA AMA MSE 0, ,387 ME 0,0-0,05667 MAE 0, , MPE 0, ,00803 U 0, ,00809 U, , Soure: ow lulios usig Exel. For he uemployme re, he VA models uderesimed he predied vlues. The vlues regisered y he ury idiors re ordiory, euse some of hem show higher ury for foress sed o VA models ME,MPE,U d ohers for prediios usig AMA proedure MSE, MAE, U. However, he uemployme re foress sed o AMA models re eer h hose go usig he ïve model. Idiors of foress ury for he ieres re for Jury 0- Mrh 0 Tle 3 Ieres re Models used o uild he foress Idiors of ury VA AMA Models wih lgged vriles MSE 0, , , ME -0,667-0,47-0,57833 MAE 0,6667 0,47 0, MPE -0,003-0,705-0,08 U, ,87779, U 0,0565 0, ,0978 Soure: ow lulios usig Exel. The es foress for he ieres re re geered y he AMA models, ll he ury mesures hvig low vlues. For ll he meioed eoomeri vlues we see edey of overesimig he predied vlues. Oly he AMA models provided good ury, he vlue lose o zero for U sisi 0,877 emphsizig his olusio, ulie VA models or hose wih lgs where U regisered vlues 34

6 Mihel Bru, Eri Mri Shor ru d lerive mroeoomi foress for omi d sregies o improve heir greer h. All he foress sed o he proposed eoomeri models re eer h he prediios usig he rdom wl model. THE ACCUACY EVALUATION OF MACOECONOMIC FOECASTS BASED ON EXPONENTIAL SMOOTHING AND MOVING AVEAGE TECHNIQUES Expoeil smoohig is ehique used o me foress s he eoomeri modelig. I is simple mehod h es io ou he more ree d. I oher words, ree oservios i he d series re give more weigh i prediig h he older vlues. Expoeil smoohig osiders expoeilly deresig weighs over ime. 4. Simple expoeil smoohig mehod M The ehique e pplied for siory d o me shor ru foress. Srig from = + u, where is os d u resid, s- sesol frequey, he prediio for he ex period is: ˆ ˆ, =,,..., = + is smoohig for, wih vlues ewee 0 d, eig deermied y miimizig he sum of squred prediio errors. + ˆ + = mi mi e+ 6 i= 0 i= 0 Eh fuure smoohed vlue is luled s weighed verge of he ps oservios, resulig: ˆ i + = ˆ + s. 7 i= 5. Hol-Wiers Simple expoeil smoohig mehod M The mehod is reommeded for d series wih lier red d wihou sesol vriios, he fores eig deermied s: Filly, he prediio vlue o horizo is: + = = + ˆ ˆ ˆ 0 6. Hol-Wiers mulipliive expoeil smoohig mehod M3 This ehique is used whe he red is lier d he sesol vriio follows mulipliive model. The smoohed d series is: 35

7 Jourl of Ieriol Sudies Vol. 5, No., 0 36 ˆ, where -ierep, - red, - mulipliive sesol for s s The prediio is: ˆ ˆ ˆ ˆ Hol-Wiers ddiive expoeil smoohig mehod M4 This ehique is used whe he red is lier d he sesol vriio follows ddiive model. The smoohed d series is: ˆ 4 - ierep, - red, - ddiive sesol for s s 5 The prediio is: ˆ ˆ ˆ ˆ 6 8. Doule expoeil smoohig mehod M5 This ehique is reommeded whe he red is lier, wo reursive equios eig used: S S 7 D S D where S d D re simple, respeively doule smoohed series.

8 Mihel Bru, Eri Mri Shor ru d lerive mroeoomi foress for omi d sregies o improve heir 9. Movig verge mehod M6 The fores sed o movig verge mehod srs from he hypohesis of model wih os: X 4 The prmeer ime T is he verge of he ls oservios, whe is he legh of he iervl: The predied vlue for X vrile is: T X T ˆ T 5 X ˆ ˆ,,,... 6 T T I Aex we preseed he foress sed o expoeil smoohig d movig verge ehiques. All he expoeil d movig verge mehods overesimed he iflio d uemployme re, euse of he egive vlues of ME idior. For iflio d ieres re he Hol-Wiers ddiive expoeil smoohig mehod geered he es prediios o progosis horizo of 3 mohs. For uemployme re he Hol-Wiers ddiive d mulipliive expoeil smoohig mehod re he es o e used. The prediios sed o movig verge hve higher degree of ury h my foress sed o expoeil smoohig ehiques, u hese re o eer h simple progoses h use he ive model. Alyzig he U idiors, we me omprisos ewee he foresig mehods. For he iflio re he VA model geered eer prediios h he expoeil smoohig or movig verge ehiques. For he uemployme re AMA proedure is reommeded euse of he highes ury of foress. The Hol-Wiers mulipliive expoeil smoohig is he es hoie whe we predi he ieres re, euse he d series hs ree hges differe from he old vlues. Tle 4 Idiors of ury for foress sed o eexpoeil smoohig d movig verge ehiques Iflio re M M M3 M4 M5 M MSE 0, , , , , , ME -0,5348-0,383-0,330-0,368-0,4733-0,653 MAE 0,4938 0,873 0,70 0,758 0,433 0,603 MPE -0,054-0,0097-0,0093-0,0094-0,047-0,075 U 0,0097 0, , , , ,035 U,4966 0,3396 0, , ,83338, Uemployme re M M M3 M4 M5 M6 MSE 0, , ,3873 0, ,5536 0,59444 ME -0, ,045-0,0-0, , -0,

9 Jourl of Ieriol Sudies Vol. 5, No., MAE 0, , , 0, 0, 0,03333 MPE -0, ,0060-0,003-0, ,0304-0,00756 U 0, ,0047 0, , ,066 0,06389 U 0,849 0,8350 0, ,6674,6796,96064 Ieres re M M M3 M4 M5 M6 MSE,937948,04768, ,500938,4383, ME,93333, , ,46667,4,46667 MAE,93333, ,78 3,46667,4,46667 MPE 0, , ,64576,3735 0, ,00756 U 0, ,7976 0,7035 0, ,3079 0,04445 U,7804,3536 0, , ,0748, Soure: ow lulios usig Exel. 5. STATEGIES OF POSSIBLE IMPOVEMENT OF FOECASTS ACCUACY Bru 0 ses some impor sregies o e used i prie i order o improve he foress ury. Oe of hese sregies is he uildig of omied foress i differe vris: prediios sed o lier omiios whose oeffiies re deermied usig he previous foress d prediios sed o orrelio mrix, he use of regressio models for lrge d ses of predied d effeive vlues. O he oher hd, we pply he hisoril errors mehod, whih supposes h he sme vlue of ury idior luled for previous period. The omied foress d hose sed o hisoril errors for iflio re d ieres re re show i Aex 3 Tle 5 Idiors of omied foress ury for he iflio re for Jury 0- Mrh 0 Iflio re Comied foress Aury idiors VA d AMA VA d models wih lgs models wih lgs d AMA MSE 0, , ,93878 ME -0,6370 -,6397-3,6737 MAE 0,5960,5987 3,637 MPE -0,079-0,088-0,4 U 0,0336 0, , U, , ,557 Soure: ow lulios usig Exel. 38

10 Mihel Bru, Eri Mri Shor ru d lerive mroeoomi foress for omi d sregies o improve heir Tle 6 Idiors of omied foress ury for he ires re for Jury 0- Mrh 0 Ieres re Comied foress Aury idiors VA d AMA VA d models wih lgs models wih lgs d AMA MSE,097845, , ME,54333,034 0, MAE,54333, 0,683 MPE 0, , ,43553 U 0,0759 0,064 0,4956 U,89578,74036,989 Soure: ow lulios usig Exel. We improved he foress ury y usig omied foress oly for he ieres re. For he iflio re we hd lower ury if we omied he prediios sed o eoomeri models. Aoher sregy o uild ew foress implies o mii os he hisoril idiors of ury. For exmple, we used MPE, ME, MAE d MSE idiors of prediios sed o eoomeri models for Novemer-Deemer 0 o uild ew foress for Jury-Mrh 0. MPE X X X + + = = MPE X + X X = MPE + X ME = X X X = ME + X + + MAE = X MSE + MAE = X = X X X X X + X X = MAE + X + + = MAE + X = MSE + X Tle 7 Idiors of ury for he iflio re d ires re foress sed o hisoril mesures of prediios for Jury 0- Mrh 0 Idiors of foress ury for iflio re Jury 0-Mrh Prediios sed o MPE idior 0 VA AMA Model wih lgged vriles MSE 0, , , ME -0,303-0,6589-0,46 MAE 0,4587 0,679 0,

11 Jourl of Ieriol Sudies Vol. 5, No., 0 MPE -0,0-0,00-0,043 U 0, ,059 0, U,6088 0,6530 0,9586 Idiors of foress ury for ieres re Jury 0- Prediios sed o MPE idior Mrh 0 VA AMA Model wih lgged vriles MSE 0,7776 0,970798,0644 ME -0,7664 0,387,00967 MAE 0, ,705,3503 MPE -0,759 0,348 0, U 0, , ,9543 U, ,86369,70469 Idiors of foress ury for iflio re Jury 0- Prediios sed o ME idior Mrh 0 VA AMA Model wih lgged vriles MSE 0, , , ME -0,3073-0,6607-0,4607 MAE 0,4603 0,697 0,4403 MPE -0,03-0,0-0,04 U 0, ,067 0,00909 U, , , Idiors of foress ury Prediios sed o ME idior for ieres re Jury 0- Mrh 0 VA AMA Model wih lgged vriles MSE 0,474,463707, ME -0,94,08, MAE 0,485333,86333,63333 MPE -0,0708 0,3845 0,66355 U 0, ,65 0,34496 U 0,6498,756,6869 Idiors of foress ury for iflio re Jury 0- Prediios sed o MAE idior Mrh 0 VA AMA Model wih lgged vriles MSE 0, , , ME -0,73-0,4533-0,3497 MAE 0,353 0,497 0,393 MPE -0,0077-0,040-0,005 U 0, , ,00685 U 0, ,8503 0,08883 Idiors of foress ury for ieres re Jury 0- Mrh 0 Prediios sed o MAE idior VA AMA Model wih lgged vriles 40

12 Mihel Bru, Eri Mri Shor ru d lerive mroeoomi foress for omi d sregies o improve heir MSE, , , ME, ,5-0,48667 MAE,93333, , MPE 0,5874-0,73-0,7485 U 0, , , U 3,30497, ,564 Idiors of foress ury for iflio re Prediios sed o MAE idior Jury 0-Mrh 0 VA AMA Model wih lgged vriles MSE 0, , , ME -0,80-0,5400-0,6437 MAE 0,7800 0,4990 0,607 MPE -0,005-0,070-0,005 U 0, , ,0355 U, ,4769 0,63938 Idiors of foress ury for ieres re Prediios sed o MAE idior Jury 0-Mrh 0 VA AMA Model wih lgged vriles MSE, , ,88856 ME,75 0, , MAE,75 0,75 0,6 MPE 0, ,0777 0,0030 U 0, ,5083 0, U 3,3953,0004 0,70369 Idiors of foress ury for iflio re Jury 0- Prediios sed o MSE idior Mrh 0 VA AMA Model wih lgged vriles MSE 0, , ,9457 ME -0,3353-0,447-0,488 MAE 0,400 0,3837 0,3778 MPE -0,09-0,030-0,08 U 0, , , U, , ,65379 Idiors of foress ury for ieres re Prediios sed o MSE idior Jury 0-Mrh 0 VA AMA Model wih lgged vriles MSE 3,373457, , ME 3,479, ,33005 MAE 3,479, ,33005 MPE,5377 0, ,909 U 0, ,3376 0, U 4,776603, ,4769 Soure: ow lulios usig Exel. 4

13 Jourl of Ieriol Sudies Vol. 5, No., 0 The iflio prediios o shor ru Jury 0-Mrh 0 sed o hisoril ury idiors lie MAE hve he highes degree of ury. I his se, VA models deermied he es foress for he followig idiors: MAE, ME, MPE, U. All he prediios sed o MAE re superior, i erms of ury, o hose sed o he ïve model. For he res of hisoril ury idiors, he foress usig VA models re iferior o hose uil usig he ïve model, ulie AMA models d models wih lg. The es prediios of he ieres re sed o hisoril ury idiors re hose h use he MSE for models wih lgs. Good resuls pper whe MAE is used for VA models d MAE for models wih lgged vriles. The ury for iflio foress sed o hisoril errors is superior o hose evlued whe he simple models re used, u he expoeil smoohig ehiques provide eer resuls. 6. CONCLUSION The hose of he es fores from my lerive oes for he sme vrile, u elored usig differe mehods is riol sep h is preeded efore he eslishme of goveremel or moery poliies or efore y deisiol proess sed o he previous owledge of some mroeoomi vriles. For d series of he omi eoomy, for shor ru foress o 3 mohs Jury 0-Mrh 0, he eoomeri models geered prediios wih rher good degree of ury, u hese ould e improved for he ieres re y omiig he foress sed o hese eoomeri models. The progoses for iflio d ieres re re loser of rel vlues whe he foress re sed o hisoril idior of ury, more ofe he MAE d he MSE orrespodig o he previous wo mohs from he fores origi. However, he expoeil smoohig mehods deermied he es prediios i erms of ury, euse hese ehiques e io ou oly he ree vlues i he d series used u uild foress ANNEX Eoomeri models used o uild oe-sep-hed foress o horizo Jury 0- Mrh 0 eferee period for he d series Ferury 999- Deemer 0 VA I = *I *I *S *S *D *D S = *I *I *S *S e-06*D e-05*D D = *I *I *S *S *D *D

14 Mihel Bru, Eri Mri Shor ru d lerive mroeoomi foress for omi d sregies o improve heir Ferury 999- Jury 0 I = *I *I *S *S *D *D S = e-05*I *I *S *S *D *D D = *I *I *S *S *D *D Ferury 999- Ferury 0 I = *I *I *S *S *D *D S = e-05*I *I *S *S *D *D D = *I *I *S *S *D *D eferee period for he d series AMA Ferury 999-Deemer 0 Ferury 999-Jury 0 Ferury 999- Ferury 0 eferee period for he d series Ferury 999-Deemer 0 Ferury 999-Jury 0 Ferury 999- Ferury 0 ri 0,436 ri rs 0,78 rs rd ri rs 0,8 0,84 rd 0,53 0,7 ri rd ri rs 0,76 0, 75 rs 0, 0,94 rd 0,53 0,7 ri rd 0,76 0, 75 rs 0, 0,94 rd Model wih lgged vriles ri 0,06 0,6 rd rd 0,055 0,3 ri 0,303 ri 0, 35 ri rd 0,095 0,49 ri 0, 57 ri ri 0,0 0,6 rd rd 0,094 0,5 ri 0, 58 ri ri 0, 0,6 rd Soure: ow ompuios usig EViews. 43

15 Jourl of Ieriol Sudies Vol. 5, No., 0 ANNEX Oe-sep-hed foress sed o eoomeri models d he ehiques of expoeil smoohig or movig verge ehiques Oe-moh-hed foress sed o VA models Jury Ferury Mrh Iflio re ri 999=00 9,06 % 9, % 9, 7 % Ieres re rd,56 %,63 %,76 % Uemployme re rs 7,00 % 7, % 7,5 % Iflio re % VA AMA Model wih lgs Effeive vlues Novemer 9,783 % 8,55084 % 8,83468 % 8,7 % Deemer 9,888 % 8,6848 % 8,95068 % 8,78 % Ieres re % VA AMA Model wih lgs Effeive vlues Novemer,055 % 3,896 % 5,59 % 5,47 % Deemer,38 % 4,58 % 6,5 % 4,97 % Oe-moh-hed foress sed o AMA models Jury Ferury Mrh Iflio re ri 999=00 8,83 % 9,07 % 9,7047 % Ieres re rd,66 %,48 %,46 % Uemployme re rs 7,053 % 7,8 % 6,787 % Oe-moh-hed foress of iflio d ieres re sed o iflio re from he previous period Jury Ferury Mrh Iflio re ri 999=00 9,0 % 9,06% 9,64 % Ieres re rd,085 %,4%,09 % Oe-moh-hed foress sed o he ehiques of expoeil smoohig or movig verge ehiques Iflio re % M M M3 M4 M5 M6 =0 Jury Ferury Mrh

16 Mihel Bru, Eri Mri Shor ru d lerive mroeoomi foress for omi d sregies o improve heir Uemployme re M M M3 M4 M5 M6 =0 Jury Ferury Mrh Ieres re M M M3 M4 M5 M6 =0 Jury Ferury Mrh Soure: ow lulios usig Exel. ANNEX 3 Comied foress d prediios sed o hisoril ury idiors for ifl io d ieres re Comied foress Iflio re % Comied foress VA d AMA Comied foress VA d models wih lgs Comied foress models wih lgs d AMA Effeive vlues Jury 0 8,690 8,59 4,783 8,899 Ferury 0 8,688 3,340 3,494 9,55 Mrh 0 8,660 8,7 8,65 9,40 Ieres re % Comied foress VA d AMA Comied foress VA d models wih lgs Comied foress models wih lgs d AMA Effeive vlues Jury 0 4,44 4,087 3,448,83 Ferury 0,876,663,76,78 Mrh 0 4,773 4,68 4,3,7 Hisoril idior of ury Mohly iflio foress Jury 0- Mrh 0 sed o ury idiors of prediios mde wo mohs go Jury 0 VA AMA Model wih lgs MPE 9,9383 8,6537 8,9783 ME 9,9 8,65 8,93 MAE 9,9 8,9 8,93 MAE 8,37 8,65 8,63 MSE 9,0748 8,8646 8,8569 Ferury 0 MPE 9, , ,9559 ME 9,06 8,70 8,93 MAE 9,06 8,78 8,95 MAE 8,50 8,78 8,6 MSE 8,8765 8, ,8089 Mrh 0 MPE 8,789 8,65 8,735 ME 8,777 8,67 8,707 MAE 9,8 8,899 9,0 MAE 8,66 8,899 8,778 MSE 8, ,0389 9,

17 Jourl of Ieriol Sudies Vol. 5, No., 0 Hisoril idior of ury Mohly ieres foress Jury 0- Mrh 0 sed o ury idiors of prediios mde wo mohs go Jury 0 VA AMA Model wih lgs MPE, , ,3796 ME,35 4,49 6,30 MAE 5,8 5,47 5,59 MAE 5,76 5,47 5,35 MSE 5,3 6, ,64765 Ferury 0 MPE,686706,66964,89304 ME 0,00,44 4,36 MAE,75,74 3,97 MAE 3,9,9,69 MSE 7,0675, ,77963 Mrh 0 MPE,40454,36380,3408 ME,8,4,8 MAE,75,99,59 MAE,8,57,97 MSE 3, ,0005 3,33 Soure: ow lulios usig Exel. EFEENCES Armsrog, J. S., Collopy F. 000, Aoher Error Mesure for Seleio of he Bes Foresig Mehod: The Uised Asolue Perege Error, Ieriol Jourl of Foresig, 8, p Armsrog, J. S., Fildes. 995, O he seleio of Error Mesures for Comprisos Amog Foresig Mehods, Jourl of Foresig, 4, p Bohri, SM. H., Feridu M. 005, Foresig Ifl io hrough Eoomeris models: A Empiril Sudy o Pisi D, The Iformio Tehologis Vol., p. 5-. Bru, M. 0, Sregies o Improve he Aury of Mroeoomi Foress i USA, LAP LAMBET Ademi Pulishig, ISBN-0: , ISBN-3: Dieold, F.X., Mrio,. 995, Comprig Prediive Aury, Jourl of Busiess, Eoomi Sisis, 3, p Fildes., Seler H. 000, The Se of Mroeoomi Foresig, Lser Uiversiy EC3/99, George Wshigo Uiversiy, Ceer for Eoomi eserh, Disussio Pper No Hydm,. J., Koehler A.B. 005, Aoher Loo Mesures of Fores Aury, Worig Pper 3/05, Aville hp:// EUOSTAT, 0. D se. [olie] Aville : hp://epp.euros.e.europ.eu/porl/pge/porl/sisis/ hemes [Aessed o April 0]. 46