ASYMPTOTIC NORMALITY FOR WEIGHTED SUMS OF LINEAR PROCESSES
We establish asymptotic normality of weighted sums of linear processes with general triangular array weights and when the innovations in the linear process are martingale differences. The results are obtained under minimal conditions on the weights and innovations. We also obtain weak convergence of...
Gespeichert in:
| Veröffentlicht in: | Econometric theory 2014-02, Vol.30 (1), p.252-284 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 284 |
|---|---|
| container_issue | 1 |
| container_start_page | 252 |
| container_title | Econometric theory |
| container_volume | 30 |
| creator | Abadir, Karim M. Distaso, Walter Giraitis, Liudas Koul, Hira L. |
| description | We establish asymptotic normality of weighted sums of linear processes with general triangular array weights and when the innovations in the linear process are martingale differences. The results are obtained under minimal conditions on the weights and innovations. We also obtain weak convergence of weighted partial sum processes. The results are applicable to linear processes that have short or long memory or exhibit seasonal long memory behavior. In particular, they are applicable to GARCH and ARCH(∞) models and to their squares. They are also useful in deriving asymptotic normality of kernel-type estimators of a nonparametric regression function with short or long memory moving average errors. |
| doi_str_mv | 10.1017/S0266466613000182 |
| format | Article |
| fullrecord | <record><control><sourceid>jstor_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_1508438120</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><cupid>10_1017_S0266466613000182</cupid><jstor_id>24534510</jstor_id><sourcerecordid>24534510</sourcerecordid><originalsourceid>FETCH-LOGICAL-c527t-52d13972b2d9da9a35fd2043cbff89a95b51a382a726f5539dd17fb9269bf1873</originalsourceid><addsrcrecordid>eNp1kE1Lw0AYhBdRsFZ_gAch4MVLdL836y3EtA2kTUlSpKew-VhpaRvdbQ_-exNaRBRP72GemXkZAG4RfEQQiacMYs4p5xwRCCHy8BkYIMqlSwmH52DQy26vX4Ira9cdgqUgA_DsZ8vpPE_yKHBmSTr14yhfOqMkdV7DaDzJwxcnW0wzJxk5cTQL_dSZp0kQZlmYXYMLrTa2uTndIViMwjyYuHEyjgI_diuGxd5luEZEClziWtZKKsJ0jSElVam1J5VkJUOKeFgJzDVjRNY1ErqUmMtSI0-QIXg45r6b9uPQ2H2xXdmq2WzUrmkPtkAMepR4CMMOvf-FrtuD2XXf9ZQQnBNGOwodqcq01ppGF-9mtVXms0Cw6Ncs_qzZee6OnrXdt-bbgCkjlKG-mZwy1bY0q_qt-VH9b-oXhRJ4cg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1507766354</pqid></control><display><type>article</type><title>ASYMPTOTIC NORMALITY FOR WEIGHTED SUMS OF LINEAR PROCESSES</title><source>Cambridge Journals</source><source>JSTOR Archive Collection A-Z Listing</source><creator>Abadir, Karim M. ; Distaso, Walter ; Giraitis, Liudas ; Koul, Hira L.</creator><creatorcontrib>Abadir, Karim M. ; Distaso, Walter ; Giraitis, Liudas ; Koul, Hira L.</creatorcontrib><description>We establish asymptotic normality of weighted sums of linear processes with general triangular array weights and when the innovations in the linear process are martingale differences. The results are obtained under minimal conditions on the weights and innovations. We also obtain weak convergence of weighted partial sum processes. The results are applicable to linear processes that have short or long memory or exhibit seasonal long memory behavior. In particular, they are applicable to GARCH and ARCH(∞) models and to their squares. They are also useful in deriving asymptotic normality of kernel-type estimators of a nonparametric regression function with short or long memory moving average errors.</description><identifier>ISSN: 0266-4666</identifier><identifier>EISSN: 1469-4360</identifier><identifier>DOI: 10.1017/S0266466613000182</identifier><language>eng</language><publisher>New York, USA: Cambridge University Press</publisher><subject>Approximation ; Brownian motion ; Cointegration ; Covariance ; Decomposition ; Density estimation ; Econometric models ; Econometrics ; Economic models ; Economic theory ; Ergodic theory ; Estimators ; Fourier transforms ; GARCH models ; Linear models ; Martingales ; Mathematics ; Memory ; Normal distribution ; Partial sums ; Random variables ; Regression analysis ; Stationary processes ; Studies ; Time series ; Unit root ; Vector-autoregressive models</subject><ispartof>Econometric theory, 2014-02, Vol.30 (1), p.252-284</ispartof><rights>Copyright © Cambridge University Press 2013</rights><rights>Cambridge University Press 2014</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c527t-52d13972b2d9da9a35fd2043cbff89a95b51a382a726f5539dd17fb9269bf1873</citedby><cites>FETCH-LOGICAL-c527t-52d13972b2d9da9a35fd2043cbff89a95b51a382a726f5539dd17fb9269bf1873</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/24534510$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.cambridge.org/core/product/identifier/S0266466613000182/type/journal_article$$EHTML$$P50$$Gcambridge$$H</linktohtml><link.rule.ids>164,314,780,784,803,27923,27924,55627,58016,58249</link.rule.ids></links><search><creatorcontrib>Abadir, Karim M.</creatorcontrib><creatorcontrib>Distaso, Walter</creatorcontrib><creatorcontrib>Giraitis, Liudas</creatorcontrib><creatorcontrib>Koul, Hira L.</creatorcontrib><title>ASYMPTOTIC NORMALITY FOR WEIGHTED SUMS OF LINEAR PROCESSES</title><title>Econometric theory</title><addtitle>Econom. Theory</addtitle><description>We establish asymptotic normality of weighted sums of linear processes with general triangular array weights and when the innovations in the linear process are martingale differences. The results are obtained under minimal conditions on the weights and innovations. We also obtain weak convergence of weighted partial sum processes. The results are applicable to linear processes that have short or long memory or exhibit seasonal long memory behavior. In particular, they are applicable to GARCH and ARCH(∞) models and to their squares. They are also useful in deriving asymptotic normality of kernel-type estimators of a nonparametric regression function with short or long memory moving average errors.</description><subject>Approximation</subject><subject>Brownian motion</subject><subject>Cointegration</subject><subject>Covariance</subject><subject>Decomposition</subject><subject>Density estimation</subject><subject>Econometric models</subject><subject>Econometrics</subject><subject>Economic models</subject><subject>Economic theory</subject><subject>Ergodic theory</subject><subject>Estimators</subject><subject>Fourier transforms</subject><subject>GARCH models</subject><subject>Linear models</subject><subject>Martingales</subject><subject>Mathematics</subject><subject>Memory</subject><subject>Normal distribution</subject><subject>Partial sums</subject><subject>Random variables</subject><subject>Regression analysis</subject><subject>Stationary processes</subject><subject>Studies</subject><subject>Time series</subject><subject>Unit root</subject><subject>Vector-autoregressive models</subject><issn>0266-4666</issn><issn>1469-4360</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp1kE1Lw0AYhBdRsFZ_gAch4MVLdL836y3EtA2kTUlSpKew-VhpaRvdbQ_-exNaRBRP72GemXkZAG4RfEQQiacMYs4p5xwRCCHy8BkYIMqlSwmH52DQy26vX4Ira9cdgqUgA_DsZ8vpPE_yKHBmSTr14yhfOqMkdV7DaDzJwxcnW0wzJxk5cTQL_dSZp0kQZlmYXYMLrTa2uTndIViMwjyYuHEyjgI_diuGxd5luEZEClziWtZKKsJ0jSElVam1J5VkJUOKeFgJzDVjRNY1ErqUmMtSI0-QIXg45r6b9uPQ2H2xXdmq2WzUrmkPtkAMepR4CMMOvf-FrtuD2XXf9ZQQnBNGOwodqcq01ppGF-9mtVXms0Cw6Ncs_qzZee6OnrXdt-bbgCkjlKG-mZwy1bY0q_qt-VH9b-oXhRJ4cg</recordid><startdate>20140201</startdate><enddate>20140201</enddate><creator>Abadir, Karim M.</creator><creator>Distaso, Walter</creator><creator>Giraitis, Liudas</creator><creator>Koul, Hira L.</creator><general>Cambridge University Press</general><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8BJ</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FQK</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>JBE</scope><scope>K60</scope><scope>K6~</scope><scope>L.-</scope><scope>L.0</scope><scope>M0C</scope><scope>M2O</scope><scope>MBDVC</scope><scope>PADUT</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYYUZ</scope><scope>Q9U</scope></search><sort><creationdate>20140201</creationdate><title>ASYMPTOTIC NORMALITY FOR WEIGHTED SUMS OF LINEAR PROCESSES</title><author>Abadir, Karim M. ; Distaso, Walter ; Giraitis, Liudas ; Koul, Hira L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c527t-52d13972b2d9da9a35fd2043cbff89a95b51a382a726f5539dd17fb9269bf1873</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Approximation</topic><topic>Brownian motion</topic><topic>Cointegration</topic><topic>Covariance</topic><topic>Decomposition</topic><topic>Density estimation</topic><topic>Econometric models</topic><topic>Econometrics</topic><topic>Economic models</topic><topic>Economic theory</topic><topic>Ergodic theory</topic><topic>Estimators</topic><topic>Fourier transforms</topic><topic>GARCH models</topic><topic>Linear models</topic><topic>Martingales</topic><topic>Mathematics</topic><topic>Memory</topic><topic>Normal distribution</topic><topic>Partial sums</topic><topic>Random variables</topic><topic>Regression analysis</topic><topic>Stationary processes</topic><topic>Studies</topic><topic>Time series</topic><topic>Unit root</topic><topic>Vector-autoregressive models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Abadir, Karim M.</creatorcontrib><creatorcontrib>Distaso, Walter</creatorcontrib><creatorcontrib>Giraitis, Liudas</creatorcontrib><creatorcontrib>Koul, Hira L.</creatorcontrib><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>International Bibliography of the Social Sciences</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>ABI/INFORM Global</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Research Library China</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><jtitle>Econometric theory</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Abadir, Karim M.</au><au>Distaso, Walter</au><au>Giraitis, Liudas</au><au>Koul, Hira L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>ASYMPTOTIC NORMALITY FOR WEIGHTED SUMS OF LINEAR PROCESSES</atitle><jtitle>Econometric theory</jtitle><addtitle>Econom. Theory</addtitle><date>2014-02-01</date><risdate>2014</risdate><volume>30</volume><issue>1</issue><spage>252</spage><epage>284</epage><pages>252-284</pages><issn>0266-4666</issn><eissn>1469-4360</eissn><abstract>We establish asymptotic normality of weighted sums of linear processes with general triangular array weights and when the innovations in the linear process are martingale differences. The results are obtained under minimal conditions on the weights and innovations. We also obtain weak convergence of weighted partial sum processes. The results are applicable to linear processes that have short or long memory or exhibit seasonal long memory behavior. In particular, they are applicable to GARCH and ARCH(∞) models and to their squares. They are also useful in deriving asymptotic normality of kernel-type estimators of a nonparametric regression function with short or long memory moving average errors.</abstract><cop>New York, USA</cop><pub>Cambridge University Press</pub><doi>10.1017/S0266466613000182</doi><tpages>33</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0266-4666 |
| ispartof | Econometric theory, 2014-02, Vol.30 (1), p.252-284 |
| issn | 0266-4666 1469-4360 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1508438120 |
| source | Cambridge Journals; JSTOR Archive Collection A-Z Listing |
| subjects | Approximation Brownian motion Cointegration Covariance Decomposition Density estimation Econometric models Econometrics Economic models Economic theory Ergodic theory Estimators Fourier transforms GARCH models Linear models Martingales Mathematics Memory Normal distribution Partial sums Random variables Regression analysis Stationary processes Studies Time series Unit root Vector-autoregressive models |
| title | ASYMPTOTIC NORMALITY FOR WEIGHTED SUMS OF LINEAR PROCESSES |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T12%3A31%3A41IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=ASYMPTOTIC%20NORMALITY%20FOR%20WEIGHTED%20SUMS%20OF%20LINEAR%20PROCESSES&rft.jtitle=Econometric%20theory&rft.au=Abadir,%20Karim%20M.&rft.date=2014-02-01&rft.volume=30&rft.issue=1&rft.spage=252&rft.epage=284&rft.pages=252-284&rft.issn=0266-4666&rft.eissn=1469-4360&rft_id=info:doi/10.1017/S0266466613000182&rft_dat=%3Cjstor_proqu%3E24534510%3C/jstor_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1507766354&rft_id=info:pmid/&rft_cupid=10_1017_S0266466613000182&rft_jstor_id=24534510&rfr_iscdi=true |