Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/149102
Authors: 
Hualde, Javier
Nielsen, Morten Ørregaard
Year of Publication: 
2017
Series/Report no.: 
Queen's Economics Department Working Paper 1376
Abstract: 
We consider truncated (or conditional) sum of squares estimation of a parametric model composed of a fractional time series and an additive generalized polynomial trend. Both the memory parameter, which characterizes the behaviour of the stochastic component of the model, and the exponent parameter, which drives the shape of the deterministic component, are considered not only unknown real numbers, but also lying in arbitrarily large (but finite) intervals. Thus, our model captures different forms of nonstationarity and noninvertibility. As in related settings, the proof of consistency (which is a prerequisite for proving asymptotic normality) is challenging due to non-uniform convergence of the objective function over a large admissible parameter space, but, in addition, our framework is substantially more involved due to the competition between stochastic and deterministic components. We establish consistency and asymptotic normality under quite general circumstances, finding that results differ crucially depending on the relative strength of the deterministic and stochastic components.
Subjects: 
Asymptotic normality
consistency
deterministic trend
fractional process
generalized polynomial trend
noninvertibility
nonstationarity
truncated sum of squares estimation
JEL: 
C22
Document Type: 
Working Paper

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