skip to content
Testing Dependence among Serially Correlated Multi-category Variables Preview this item
ClosePreview this item
Checking...

Testing Dependence among Serially Correlated Multi-category Variables

Author: M Hashem Pesaran; Allan Timmermann
Publisher: München : CESifo, Center for Economic Studies & Ifo Institute for economic research, 2006.
Series: CESifo working paper series, 1770.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
The contingency table literature on tests for dependence among discrete multi-category variables is extensive. Existing tests assume, however, that draws are independent, and there are no tests that account for serial dependencies-a problem that is particularly important in economics and finance. This paper proposes a new test of independence based on the maximum canonical correlation between pairs of discrete  Read more...
Rating:

(not yet rated) 0 with reviews - Be the first.

Find a copy online

Links to this item

Find a copy in the library

&AllPage.SpinnerRetrieving; Finding libraries that hold this item...

Details

Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: M Hashem Pesaran; Allan Timmermann
OCLC Number: 778412266
Description: 1 online resource (Text.)
Series Title: CESifo working paper series, 1770.
Responsibility: M. Hashem Pesaran, Allan Timmermann.

Abstract:

The contingency table literature on tests for dependence among discrete multi-category variables is extensive. Existing tests assume, however, that draws are independent, and there are no tests that account for serial dependencies-a problem that is particularly important in economics and finance. This paper proposes a new test of independence based on the maximum canonical correlation between pairs of discrete variables. We also propose a trace canonical correlation test using dynamically augmented reduced rank regressions or an iterated weighting method in order to account for serial dependence. Such tests are useful, for example, when testing for predictability of one sequence of discrete random variables by means of another sequence of discrete random variables as in tests of market timing skills or business cycle analysis. The proposed tests allow for an arbitrary number of categories, are robust in the presence of serial dependencies and are simple to implement using multivariate regression methods. Monte Carlo experiments show that the proposed tests have good finite sample properties. An empirical application to survey data on forecasts of GDP growth demonstrates the importance of correcting for serial dependencies in predictability tests.

Reviews

User-contributed reviews
Retrieving GoodReads reviews...
Retrieving DOGObooks reviews...

Tags

Be the first.
Confirm this request

You may have already requested this item. Please select Ok if you would like to proceed with this request anyway.

Linked Data


Primary Entity

<http://www.worldcat.org/oclc/778412266> # Testing Dependence among Serially Correlated Multi-category Variables
    a schema:Book, schema:MediaObject, schema:CreativeWork ;
   library:oclcnum "778412266" ;
   library:placeOfPublication <http://experiment.worldcat.org/entity/work/data/57528628#Place/munchen> ; # München
   library:placeOfPublication <http://id.loc.gov/vocabulary/countries/gw> ;
   schema:about <http://dewey.info/class/330/> ;
   schema:bookFormat schema:EBook ;
   schema:contributor <http://viaf.org/viaf/84096885> ; # Allan Timmermann
   schema:creator <http://viaf.org/viaf/108731710> ; # M Hashem Pesaran
   schema:datePublished "2006" ;
   schema:description "The contingency table literature on tests for dependence among discrete multi-category variables is extensive. Existing tests assume, however, that draws are independent, and there are no tests that account for serial dependencies-a problem that is particularly important in economics and finance. This paper proposes a new test of independence based on the maximum canonical correlation between pairs of discrete variables. We also propose a trace canonical correlation test using dynamically augmented reduced rank regressions or an iterated weighting method in order to account for serial dependence. Such tests are useful, for example, when testing for predictability of one sequence of discrete random variables by means of another sequence of discrete random variables as in tests of market timing skills or business cycle analysis. The proposed tests allow for an arbitrary number of categories, are robust in the presence of serial dependencies and are simple to implement using multivariate regression methods. Monte Carlo experiments show that the proposed tests have good finite sample properties. An empirical application to survey data on forecasts of GDP growth demonstrates the importance of correcting for serial dependencies in predictability tests." ;
   schema:exampleOfWork <http://worldcat.org/entity/work/id/57528628> ;
   schema:inLanguage "en" ;
   schema:isPartOf <http://experiment.worldcat.org/entity/work/data/57528628#Series/cesifo_working_paper_series> ; # CESifo working paper series ;
   schema:isPartOf <http://experiment.worldcat.org/entity/work/data/57528628#Series/cesifo_working_paper> ; # CESifo working paper ;
   schema:name "Testing Dependence among Serially Correlated Multi-category Variables" ;
   schema:productID "778412266" ;
   schema:publication <http://www.worldcat.org/title/-/oclc/778412266#PublicationEvent/munchen_cesifo_center_for_economic_studies_&_ifo_institute_for_economic_research_2006> ;
   schema:publisher <http://experiment.worldcat.org/entity/work/data/57528628#Agent/cesifo_center_for_economic_studies_&_ifo_institute_for_economic_research> ; # CESifo, Center for Economic Studies & Ifo Institute for economic research
   schema:url <http://www.cesifo-group.de/ifoHome/publications/working-papers/CESifoWP.html> ;
   schema:url <http://www.cesifo-group.de/DocCIDL/cesifo1_wp1770.pdf> ;
   wdrs:describedby <http://www.worldcat.org/title/-/oclc/778412266> ;
    .


Related Entities

<http://experiment.worldcat.org/entity/work/data/57528628#Agent/cesifo_center_for_economic_studies_&_ifo_institute_for_economic_research> # CESifo, Center for Economic Studies & Ifo Institute for economic research
    a bgn:Agent ;
   schema:name "CESifo, Center for Economic Studies & Ifo Institute for economic research" ;
    .

<http://experiment.worldcat.org/entity/work/data/57528628#Series/cesifo_working_paper> # CESifo working paper ;
    a bgn:PublicationSeries ;
   schema:hasPart <http://www.worldcat.org/oclc/778412266> ; # Testing Dependence among Serially Correlated Multi-category Variables
   schema:name "CESifo working paper ;" ;
    .

<http://experiment.worldcat.org/entity/work/data/57528628#Series/cesifo_working_paper_series> # CESifo working paper series ;
    a bgn:PublicationSeries ;
   schema:hasPart <http://www.worldcat.org/oclc/778412266> ; # Testing Dependence among Serially Correlated Multi-category Variables
   schema:name "CESifo working paper series ;" ;
    .

<http://viaf.org/viaf/108731710> # M Hashem Pesaran
    a schema:Person ;
   schema:familyName "Pesaran" ;
   schema:givenName "M. Hashem" ;
   schema:name "M Hashem Pesaran" ;
    .

<http://viaf.org/viaf/84096885> # Allan Timmermann
    a schema:Person ;
   schema:familyName "Timmermann" ;
   schema:givenName "Allan" ;
   schema:name "Allan Timmermann" ;
    .


Content-negotiable representations

Close Window

Please sign in to WorldCat 

Don't have an account? You can easily create a free account.