A modified conditional sum-of-squares estimator removes the leading bias term induced by constant-term estimation in stationary or non-stationary ARFIMA(p1,d,p2) models.
Since ∑t−1 r=0e4 r≤(∑∞ r=0e2 r)2 and ∑t−1 r=0 ∑t−1 s=0,s̸=re2 re2 s≤(∑∞ r=0e2 r)2, it follows that E(y4 t )≤c (∞∑ r=0 e2 r )2 (A.58) forc<∞
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The modified conditional sum-of-squares estimator for fractionally integrated models
A modified conditional sum-of-squares estimator removes the leading bias term induced by constant-term estimation in stationary or non-stationary ARFIMA(p1,d,p2) models.