Forecasting Large Datasets with Reduced Rank Multivariate Models

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Table 1. Summary of dataset Code Series Transformation A 0M 048 E M P L O Y E E H O U R S IN N O N AG . E STA B L ISH M E N T S (A R , B IL . H O U R S) M onthly G row th R ate A 0M 051 P E R SO N A L IN C O M E L E SS T R A N SF E R PAY M E N T S (A R , B IL . C H A IN 2000 U SD ) M onthly G row th R ate A 0M 052 P E R SO N A L IN C O M E (A R , B IL . C H A IN 2000 U SD ) M onthly G row th R ate A 0M 057 M A N U FAC T U R IN G A N D T R A D E SA L E S (M IL . C H A IN 1996 U SD ) M onthly G row th R ate A 0M 059 SA L E S O F R E TA IL ST O R E S (M IL . C H A IN 2000 U SD ) M onthly G row th R ate A 0M 082 C A PAC IT Y U T IL IZ AT IO N (M FG ) F irst D i¤erence A 0M 095 R AT IO , C O N SU M E R IN STA L L M E N T C R E D IT T O P E R SO N A L IN C O M E (P C T .) F irst D i¤erence A 0M 224R R E A L C O N SU M P T IO N (AC ) A 0M 224/G M D C M onthly G row th R ate C C IN RV C O N SU M E R C R E D IT O U T STA N D IN G - N O N R E VO LV IN G (G 19) C hange in Yearly G row th R ate C E S002 E M P L O Y E E S O N N O N FA R M PAY RO L L S - T O TA L P R IVAT E M onthly G row th R ate C P 90 C O M M E RC IA L PA P E R R AT E F irst D i¤erence E X RC A N FO R E IG N E XC H A N G E R AT E : C A N A DA (C A N A D IA N U SD P E R U .S.U SD ) M onthly G row th R ate E X R JA N FO R E IG N E XC H A N G E R AT E : JA PA N (Y E N P E R U .S.U SD ) M onthly G row th R ate E X R SW FO R E IG N E XC H A N G E R AT E : SW IT Z E R L A N D (SW ISS F R A N C P E R U .S.U SD ) M onthly G row th R ate E X RU K FO R E IG N E XC H A N G E R AT E : U N IT E D K IN G D O M (C E N T S P E R P O U N D ) M onthly G row th R ate E X RU S U N IT E D STAT E S;E F F E C T IV E E XC H A N G E R AT E (M E R M )(IN D E X N O .) M onthly G row th R ate FC L B M C W K LY R P L G C O M B A N K S:N E T C H A N G E C O M ’L A N D IN D U S L OA N S(B IL U SD ,SA A R ) N o Transf. FC L N Q C O M M E RC IA L A N D IN D U ST R IA L L OA N S O U STA N D IN G IN 1996 D O L L A R S (B C I) C hange in Yearly G row th R ate F M 1 M O N E Y ST O C K : M 1 (B IL U SD ,SA ) C hange in Yearly G row th R ate F M 2 M O N E Y ST O C K :M 2 (B IL U SD ,SA ) C hange in Yearly G row th R ate F M 2D Q M O N E Y SU P P LY - M 2 IN 1996 D O L L A R S (B C I) M onthly G row th R ate F M 3 M O N E Y ST O C K : M 3 (B IL U SD ,SA ) C hange in Yearly G row th R ate F M F B A M O N E TA RY B A SE , A D J FO R R E SE RV E R E Q U IR E M E N T C H A N G E S(M IL U SD ,SA ) C hange in Yearly G row th R ate F M R N B A D E P O SIT O RY IN ST R E SE RV E S:N O N B O R ROW E D ,A D J R E S R E Q C H G S (M IL U SD ,SA ) C hange in Yearly G row th R ate F M R R A D E P O SIT O RY IN ST R E SE RV E S:T O TA L ,A D J FO R R E SE RV E R E Q C H G S (M IL U SD ,SA ) C hange in Yearly G row th R ate F SDX P SA N D P ’S C O M P O SIT E C O M M O N ST O C K : D IV ID E N D Y IE L D (p erc P E R A N N U M ) F irst D i¤erence F SP C O M SA N D P ’S C O M M O N ST O C K P R IC E IN D E X : C O M P O SIT E (1941-43= 10) M onthly G row th R ate F SP IN SA N D P ’S C O M M O N ST O C K P R IC E IN D E X : IN D U ST R IA L S (1941-43= 10) M onthly G row th R ate F SP X E SA N D P ’S C O M P O SIT E C O M M O N ST O C K : P R IC E -E A R N IN G S R AT IO (p erc,N SA ) M onthly G row th R ate F YA A AC B O N D Y IE L D : M O O DY ’S A A A C O R P O R AT E (p erc P E R A N N U M ) F irst D i¤erence F Y B A AC B O N D Y IE L D : M O O DY ’S B A A C O R P O R AT E (p erc P E R A N N U M ) F irst D i¤erence F Y F F IN T E R E ST R AT E : F E D E R A L F U N D S (E F F E C T IV E ) (p erc P E R A N N U M ,N SA ) F irst D i¤erence F Y G M 3 IN T E R E ST R AT E : U .S.T R E A SU RY B IL L S,SE C M K T ,3-M O .(p erc P E R A N N ,N SA ) F irst D i¤erence F Y G M 6 IN T E R E ST R AT E : U .S.T R E A SU RY B IL L S,SE C M K T ,6-M O .(p erc P E R A N N ,N SA ) F irst D i¤erence F Y G T 1 IN T E R E ST R AT E : U .S.T R E A SU RY C O N ST M AT U R IT IE S,1-Y R .(p erc P E R A N N ,N SA ) F irst D i¤erence F Y G T 10 IN T E R E ST R AT E : U .S.T R E A SU RY C O N ST M AT U R IT IE S,10-Y R .(p erc P E R A N N ,N SA ) F irst D i¤erence F Y G T 5 IN T E R E ST R AT E : U .S.T R E A SU RY C O N ST M AT U R IT IE S,5-Y R .(p erc P E R A N N ,N SA ) F irst D i¤erence IP S10 IN D U ST R IA L P RO D U C T IO N IN D E X - T O TA L IN D E X M onthly G row th R ate L H E L IN D E X O F H E L P -WA N T E D A DV E RT ISIN G IN N E W SPA P E R S (1967= 100;SA ) F irst D i¤erence L H E L X E M P L O Y M E N T : R AT IO ; H E L P -WA N T E D A D S:N O . U N E M P L O Y E D C L F F irst D i¤erence L H E M C IV IL IA N L A B O R FO RC E : E M P L O Y E D , T O TA L (T H O U S.,SA ) M onthly G row th R ate L H U R U N E M P L O Y M E N T R AT E : A L L W O R K E R S, 16 Y E A R S A N D O V E R (p erc,SA ) F irst D i¤erence P M D E L N A P M V E N D O R D E L IV E R IE S IN D E X (P E RC E N T ) N o Transf. P M I P U RC H A SIN G M A N AG E R S’ IN D E X (SA ) N o Transf. P M N O N A P M N E W O R D E R S IN D E X (P E RC E N T ) N o Transf. P M N V N A P M IN V E N T O R IE S IN D E X (P E RC E N T ) N o Transf. P M P N A P M P RO D U C T IO N IN D E X (P E RC E N T ) N o Transf. P U N E W C P I-U : A L L IT E M S (82-84= 100,SA ) C hange in Yearly G row th R ate P W C M SA P RO D U C E R P R IC E IN D E X :C RU D E M AT E R IA L S (82= 100,SA ) C hange in Yearly G row th R ate P W FC SA P RO D U C E R P R IC E IN D E X :F IN ISH E D C O N SU M E R G O O D S (82= 100,SA ) C hange in Yearly G row th R ate P W F SA P RO D U C E R P R IC E IN D E X : F IN ISH E D G O O D S (82= 100,SA ) C hange in Yearly G row th R ate P W IM SA P RO D U C E R P R IC E IN D E X :IN T E R M E D M AT .SU P P L IE S A N D C O M P O N E N T S (SA ) C hange in Yearly G row th R ate
Page 1: DEPARTMENT OF ECONOMICS EUI Working
Page 4 and 5: This text may be downloaded for per
Page 6 and 7: 1 Introduction Forecasting future d
Page 8 and 9: sectoral data on industrial product
Page 10 and 11: i = 1; :::; p. An interesting speci
Page 12 and 13: In this paper we implement a Normal
Page 14 and 15: with and being respectively M r and
Page 16 and 17: 2.5 Multivariate Boosting (MB) The
Page 18 and 19: of boosting iterations m is a much
Page 20 and 21: and very likely to create problems
Page 22 and 23: multivariate benchmark is the basel
Page 24 and 25: ur with gains relatively smaller (b
Page 26 and 27: eduction produces additional gains
Page 28 and 29: and vec( ^ O 0 ) = gO vec( ^ A) (33
Page 32 and 33: Table 2: Relative WTMSFE vs AR(p*)
Page 34 and 35: Table 4: BVAR in Di¤erences and BV
Page 36 and 37: Table 6: Relative WTMSFE vs AR(p*)

Forecasting Large Datasets with Reduced Rank Multivariate Models

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