A non-linear forecast combination procedure for binary outcomes

We develop a non-linear forecast combination rule based on copulas that incorporate the dynamic interaction between individual predictors. This approach is optimal in the sense that the resulting combined forecast produces the highest discriminatory power as measured by the receiver operating charac...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Lahiri, Kajal 1947- (VerfasserIn), Liu, Yang (VerfasserIn)
Format: Elektronisch E-Book
Sprache:English
Veröffentlicht: München CESifo 2015
Schriftenreihe:CESifo working paper 5175 : Category 11, Empirical and theoretical methods
Online-Zugang:kostenfrei
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!

MARC

LEADER 00000nmm a2200000 cb4500
001 BV042900710
003 DE-604
005 20190709
007 cr|uuu---uuuuu
008 150930s2015 |||| o||u| ||||||eng d
035 |a (OCoLC)927108530 
035 |a (DE-599)GBV817738924 
040 |a DE-604  |b ger 
041 0 |a eng 
049 |a DE-521 
084 |a QB 910  |0 (DE-625)141231:  |2 rvk 
100 1 |a Lahiri, Kajal  |d 1947-  |e Verfasser  |0 (DE-588)13377080X  |4 aut 
245 1 0 |a A non-linear forecast combination procedure for binary outcomes  |c Kajal Lahiri ; Liu Yang 
264 1 |a München  |b CESifo  |c 2015 
300 |a 1 Online-Ressource (38 S.)  |b graph. Darst. 
336 |b txt  |2 rdacontent 
337 |b c  |2 rdamedia 
338 |b cr  |2 rdacarrier 
490 1 |a CESifo working paper  |v 5175 : Category 11, Empirical and theoretical methods 
520 1 |a We develop a non-linear forecast combination rule based on copulas that incorporate the dynamic interaction between individual predictors. This approach is optimal in the sense that the resulting combined forecast produces the highest discriminatory power as measured by the receiver operating characteristic (ROC) curve. Under additional assumptions, this rule is shown to be equivalent to the quintessential linear combination scheme. To illustrate its usefulness, we apply this methodology to optimally aggregate two currently used leading indicators-the ISM new order diffusion index and the yield curve spread-to predict economic recessions in the United States. We also examine the sources of forecasting gains using a counterfactual experimental set up. 
538 |a . - Acrobat Reader 
700 1 |a Liu, Yang  |e Verfasser  |0 (DE-588)1017346283  |4 aut 
830 0 |a CESifo working paper  |v 5175 : Category 11, Empirical and theoretical methods  |w (DE-604)BV014083264  |9 5175 
856 4 0 |u https://www.cesifo.org/DocDL/cesifo1_wp5175.pdf  |x Verlag  |z kostenfrei  |3 Volltext 
912 |a ebook 
999 |a oai:aleph.bib-bvb.de:BVB01-028328871 

Datensatz im Suchindex

_version_ 1804175193586794496
any_adam_object
author Lahiri, Kajal 1947-
Liu, Yang
author_GND (DE-588)13377080X
(DE-588)1017346283
author_facet Lahiri, Kajal 1947-
Liu, Yang
author_role aut
aut
author_sort Lahiri, Kajal 1947-
author_variant k l kl
y l yl
building Verbundindex
bvnumber BV042900710
classification_rvk QB 910
collection ebook
ctrlnum (OCoLC)927108530
(DE-599)GBV817738924
discipline Wirtschaftswissenschaften
format Electronic
eBook
fullrecord <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01987nmm a2200337 cb4500</leader><controlfield tag="001">BV042900710</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20190709 </controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">150930s2015 |||| o||u| ||||||eng d</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)927108530</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBV817738924</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-521</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QB 910</subfield><subfield code="0">(DE-625)141231:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lahiri, Kajal</subfield><subfield code="d">1947-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)13377080X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A non-linear forecast combination procedure for binary outcomes</subfield><subfield code="c">Kajal Lahiri ; Liu Yang</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">München</subfield><subfield code="b">CESifo</subfield><subfield code="c">2015</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (38 S.)</subfield><subfield code="b">graph. Darst.</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">CESifo working paper</subfield><subfield code="v">5175 : Category 11, Empirical and theoretical methods</subfield></datafield><datafield tag="520" ind1="1" ind2=" "><subfield code="a">We develop a non-linear forecast combination rule based on copulas that incorporate the dynamic interaction between individual predictors. This approach is optimal in the sense that the resulting combined forecast produces the highest discriminatory power as measured by the receiver operating characteristic (ROC) curve. Under additional assumptions, this rule is shown to be equivalent to the quintessential linear combination scheme. To illustrate its usefulness, we apply this methodology to optimally aggregate two currently used leading indicators-the ISM new order diffusion index and the yield curve spread-to predict economic recessions in the United States. We also examine the sources of forecasting gains using a counterfactual experimental set up.</subfield></datafield><datafield tag="538" ind1=" " ind2=" "><subfield code="a">. - Acrobat Reader</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Yang</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1017346283</subfield><subfield code="4">aut</subfield></datafield><datafield tag="830" ind1=" " ind2="0"><subfield code="a">CESifo working paper</subfield><subfield code="v">5175 : Category 11, Empirical and theoretical methods</subfield><subfield code="w">(DE-604)BV014083264</subfield><subfield code="9">5175</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.cesifo.org/DocDL/cesifo1_wp5175.pdf</subfield><subfield code="x">Verlag</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ebook</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-028328871</subfield></datafield></record></collection>
id DE-604.BV042900710
illustrated Not Illustrated
indexdate 2024-07-10T07:12:24Z
institution BVB
language English
oai_aleph_id oai:aleph.bib-bvb.de:BVB01-028328871
oclc_num 927108530
open_access_boolean 1
owner DE-521
owner_facet DE-521
physical 1 Online-Ressource (38 S.) graph. Darst.
psigel ebook
publishDate 2015
publishDateSearch 2015
publishDateSort 2015
publisher CESifo
record_format marc
series CESifo working paper
series2 CESifo working paper
spelling Lahiri, Kajal 1947- Verfasser (DE-588)13377080X aut
A non-linear forecast combination procedure for binary outcomes Kajal Lahiri ; Liu Yang
München CESifo 2015
1 Online-Ressource (38 S.) graph. Darst.
txt rdacontent
c rdamedia
cr rdacarrier
CESifo working paper 5175 : Category 11, Empirical and theoretical methods
We develop a non-linear forecast combination rule based on copulas that incorporate the dynamic interaction between individual predictors. This approach is optimal in the sense that the resulting combined forecast produces the highest discriminatory power as measured by the receiver operating characteristic (ROC) curve. Under additional assumptions, this rule is shown to be equivalent to the quintessential linear combination scheme. To illustrate its usefulness, we apply this methodology to optimally aggregate two currently used leading indicators-the ISM new order diffusion index and the yield curve spread-to predict economic recessions in the United States. We also examine the sources of forecasting gains using a counterfactual experimental set up.
. - Acrobat Reader
Liu, Yang Verfasser (DE-588)1017346283 aut
CESifo working paper 5175 : Category 11, Empirical and theoretical methods (DE-604)BV014083264 5175
https://www.cesifo.org/DocDL/cesifo1_wp5175.pdf Verlag kostenfrei Volltext
spellingShingle Lahiri, Kajal 1947-
Liu, Yang
A non-linear forecast combination procedure for binary outcomes
CESifo working paper
title A non-linear forecast combination procedure for binary outcomes
title_auth A non-linear forecast combination procedure for binary outcomes
title_exact_search A non-linear forecast combination procedure for binary outcomes
title_full A non-linear forecast combination procedure for binary outcomes Kajal Lahiri ; Liu Yang
title_fullStr A non-linear forecast combination procedure for binary outcomes Kajal Lahiri ; Liu Yang
title_full_unstemmed A non-linear forecast combination procedure for binary outcomes Kajal Lahiri ; Liu Yang
title_short A non-linear forecast combination procedure for binary outcomes
title_sort a non linear forecast combination procedure for binary outcomes
url https://www.cesifo.org/DocDL/cesifo1_wp5175.pdf
volume_link (DE-604)BV014083264
work_keys_str_mv AT lahirikajal anonlinearforecastcombinationprocedureforbinaryoutcomes
AT liuyang anonlinearforecastcombinationprocedureforbinaryoutcomes