Asymptotics for the conditional-sum-of-squares estimator in multivariate fractional time series models

This paper proves consistency and asymptotic normality for the conditional-sum-of-squares estima- tor, which is equivalent to the conditional maximum likelihood estimator, in multivariate fractional time series models. The model is parametric and quite general, and, in particular, encompasses the multivariate non-cointegrated fractional ARIMA model. The novelty of the consistency result, in par- ticular, is that it applies to a multivariate model and to an arbitrarily large set of admissible parameter values, for which the objective function does not converge uniformly in probablity, thus making the proof much more challenging than usual. The neighborhood around the critical point where uniform convergence fails is handled using a truncation argument.


Issue Date:
2011-01
Publication Type:
Working or Discussion Paper
DOI and Other Identifiers:
Record Identifier:
https://ageconsearch.umn.edu/record/273758
Language:
English
Total Pages:
1259
JEL Codes:
C22; C32
Series Statement:
Working Paper No. 1259




 Record created 2018-06-20, last modified 2020-10-28

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