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ON ASYMPTOTIC INFERENCE IN COINTEGRATED TIME SERIES WITH FRACTIONALLY INTEGRATED ERRORS
Vector valued autoregressive models with fractionally integrated errors are considered. The possibility of the coefficient matrix of the model having eigenvalues with absolute values equal or close to unity is included. Quadratic approximation to the log-likelihood ratios in the vicinity of auxiliar...
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| Published in: | Econometric theory 1999-08, Vol.15 (4), p.583-621 |
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| Format: | Article |
| Language: | English |
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| container_end_page | 621 |
| container_issue | 4 |
| container_start_page | 583 |
| container_title | Econometric theory |
| container_volume | 15 |
| creator | Jeganathan, P. |
| description | Vector valued autoregressive models with fractionally
integrated errors are considered. The possibility of the
coefficient matrix of the model having eigenvalues with
absolute values equal or close to unity is included. Quadratic
approximation to the log-likelihood ratios in the vicinity
of auxiliary estimators of the parameters is obtained and
used to make a rough identification of the approximate
unit eigenvalues, including complex ones, together with
their multiplicities. Using the identification thus obtained,
the stationary linear combinations (cointegrating relationships)
and the trends that induce the nonstationarity are identified,
and Wald-type inference procedures for the parameters associated
with them are constructed. As in the situation in which
the errors are independent and identically distributed
(i.i.d.), the limiting behaviors are nonstandard in the
sense that they are neither normal nor mixed normal. In
addition, the ordinary least squares procedure, which works
reasonably well in the i.i.d. errors case, becomes severely
handicapped to adapt itself approximately to the underlying
model structure, and hence its behavior is significantly
inferior in many ways to the procedures obtained here. |
| doi_str_mv | 10.1017/S0266466699154057 |
| format | article |
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integrated errors are considered. The possibility of the
coefficient matrix of the model having eigenvalues with
absolute values equal or close to unity is included. Quadratic
approximation to the log-likelihood ratios in the vicinity
of auxiliary estimators of the parameters is obtained and
used to make a rough identification of the approximate
unit eigenvalues, including complex ones, together with
their multiplicities. Using the identification thus obtained,
the stationary linear combinations (cointegrating relationships)
and the trends that induce the nonstationarity are identified,
and Wald-type inference procedures for the parameters associated
with them are constructed. As in the situation in which
the errors are independent and identically distributed
(i.i.d.), the limiting behaviors are nonstandard in the
sense that they are neither normal nor mixed normal. In
addition, the ordinary least squares procedure, which works
reasonably well in the i.i.d. errors case, becomes severely
handicapped to adapt itself approximately to the underlying
model structure, and hence its behavior is significantly
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integrated errors are considered. The possibility of the
coefficient matrix of the model having eigenvalues with
absolute values equal or close to unity is included. Quadratic
approximation to the log-likelihood ratios in the vicinity
of auxiliary estimators of the parameters is obtained and
used to make a rough identification of the approximate
unit eigenvalues, including complex ones, together with
their multiplicities. Using the identification thus obtained,
the stationary linear combinations (cointegrating relationships)
and the trends that induce the nonstationarity are identified,
and Wald-type inference procedures for the parameters associated
with them are constructed. As in the situation in which
the errors are independent and identically distributed
(i.i.d.), the limiting behaviors are nonstandard in the
sense that they are neither normal nor mixed normal. In
addition, the ordinary least squares procedure, which works
reasonably well in the i.i.d. errors case, becomes severely
handicapped to adapt itself approximately to the underlying
model structure, and hence its behavior is significantly
inferior in many ways to the procedures obtained here.</description><subject>Approximation</subject><subject>Brownian motion</subject><subject>Covariance matrices</subject><subject>Econometrics</subject><subject>Eigenvalues</subject><subject>Estimators</subject><subject>Inference</subject><subject>Jordan matrices</subject><subject>Mathematical economics</subject><subject>Mathematical vectors</subject><subject>Perceptron convergence procedure</subject><subject>Quadratic approximation</subject><subject>Time series</subject><issn>0266-4666</issn><issn>1469-4360</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><sourceid>8BJ</sourceid><recordid>eNp1kF1r2zAYhcVYYVm6HzDYhaGwO7d69RldGldJDKnd2h6lV8JR5JI0iTMpgfXf1yGllI5e6YXnHHHOQegn4EvAIK8qTIRgQgilgDPM5Rc0ACZUzKjAX9HgiOMj_4a-h7DCGIiSdIDuizxKqoeb27qoszTK8rEudZ7q_orSIstrPSmTWl9HdXajo0qXma6i-6yeRuMySeusyJPZ7CF6J9RlWZTVOTprm3VwP17fIfoz1nU6jWfFJEuTWWw58H1sgbKFmnOKMeegGseogsYpy4hoW-rIwlqHCZNkLoDyhlrRWtv3EvORJYTQIfp9-nfnu78HF_ZmswzWrdfN1nWHYOhICsX6qkN08UG46g5-22czwPvFpFQAvQpOKuu7ELxrzc4vN41_NoDNcWjz39C959fJswr7zr8ZKKcUMO5xfMLLsHf_3nDjn4yQVHIjJneG55Uq2YSaYyP6GqHZzP1y8ejeJf00xAuV_I7G</recordid><startdate>19990801</startdate><enddate>19990801</enddate><creator>Jeganathan, P.</creator><general>Cambridge University Press</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>HAGHG</scope><scope>IOIBA</scope><scope>K30</scope><scope>PAAUG</scope><scope>PAWHS</scope><scope>PAWZZ</scope><scope>PAXOH</scope><scope>PBHAV</scope><scope>PBQSW</scope><scope>PBYQZ</scope><scope>PCIWU</scope><scope>PCMID</scope><scope>PCZJX</scope><scope>PDGRG</scope><scope>PDWWI</scope><scope>PETMR</scope><scope>PFVGT</scope><scope>PGXDX</scope><scope>PIHIL</scope><scope>PISVA</scope><scope>PJCTQ</scope><scope>PJTMS</scope><scope>PLCHJ</scope><scope>PMHAD</scope><scope>PNQDJ</scope><scope>POUND</scope><scope>PPLAD</scope><scope>PQAPC</scope><scope>PQCAN</scope><scope>PQCMW</scope><scope>PQEME</scope><scope>PQHKH</scope><scope>PQMID</scope><scope>PQNCT</scope><scope>PQNET</scope><scope>PQSCT</scope><scope>PQSET</scope><scope>PSVJG</scope><scope>PVMQY</scope><scope>PZGFC</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope></search><sort><creationdate>19990801</creationdate><title>ON ASYMPTOTIC INFERENCE IN COINTEGRATED TIME SERIES WITH FRACTIONALLY INTEGRATED ERRORS</title><author>Jeganathan, P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c515t-c134d9b53005519ae4391ae9c426ff3e2dcce02472b6135a3c6fcc4696b8c2223</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Approximation</topic><topic>Brownian motion</topic><topic>Covariance matrices</topic><topic>Econometrics</topic><topic>Eigenvalues</topic><topic>Estimators</topic><topic>Inference</topic><topic>Jordan matrices</topic><topic>Mathematical economics</topic><topic>Mathematical vectors</topic><topic>Perceptron convergence procedure</topic><topic>Quadratic approximation</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jeganathan, P.</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Periodicals Index Online Segment 12</collection><collection>Periodicals Index Online Segment 29</collection><collection>Periodicals Index Online</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - 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integrated errors are considered. The possibility of the
coefficient matrix of the model having eigenvalues with
absolute values equal or close to unity is included. Quadratic
approximation to the log-likelihood ratios in the vicinity
of auxiliary estimators of the parameters is obtained and
used to make a rough identification of the approximate
unit eigenvalues, including complex ones, together with
their multiplicities. Using the identification thus obtained,
the stationary linear combinations (cointegrating relationships)
and the trends that induce the nonstationarity are identified,
and Wald-type inference procedures for the parameters associated
with them are constructed. As in the situation in which
the errors are independent and identically distributed
(i.i.d.), the limiting behaviors are nonstandard in the
sense that they are neither normal nor mixed normal. In
addition, the ordinary least squares procedure, which works
reasonably well in the i.i.d. errors case, becomes severely
handicapped to adapt itself approximately to the underlying
model structure, and hence its behavior is significantly
inferior in many ways to the procedures obtained here.</abstract><cop>New York</cop><pub>Cambridge University Press</pub><doi>10.1017/S0266466699154057</doi><tpages>39</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0266-4666 |
| ispartof | Econometric theory, 1999-08, Vol.15 (4), p.583-621 |
| issn | 0266-4666 1469-4360 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_38769497 |
| source | International Bibliography of the Social Sciences (IBSS); JSTOR Archival Journals and Primary Sources Collection; Cambridge University Press |
| subjects | Approximation Brownian motion Covariance matrices Econometrics Eigenvalues Estimators Inference Jordan matrices Mathematical economics Mathematical vectors Perceptron convergence procedure Quadratic approximation Time series |
| title | ON ASYMPTOTIC INFERENCE IN COINTEGRATED TIME SERIES WITH FRACTIONALLY INTEGRATED ERRORS |
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