Consider the stationary linear process \documentclass[12pt]{minimal}
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\begin{document}$X_t=\sum_{u=-\infty}^\infty a(t-u)\xi_u$\end{document}, \documentclass[12pt]{minimal}
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\begin{document}$t\in {\bf Z}$\end{document}, where \documentclass[12pt]{minimal}
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\begin{document}$\{ \xi_u\}$\end{document} is an i.i.d. finite variance sequence. The spectral density of \documentclass[12pt]{minimal}
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\begin{document}$\{ X_t\}$\end{document} may diverge at the origin (long-range dependence) or at any other frequency. Consider now the quadratic form \documentclass[12pt]{minimal}
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\begin{document}$Q_N=\sum_{t,s=1}^N b(t-s)P_{m,n} (X_t,X_s)$\end{document}, where \documentclass[12pt]{minimal}
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\begin{document}$P_{m,n}(X_t,X_s)$\end{document} denotes a non-linear function (Appell polynomial). We provide general conditions on the kernels \documentclass[12pt]{minimal}
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\begin{document}$b$\end{document} and \documentclass[12pt]{minimal}
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\begin{document}$a$\end{document} for \documentclass[12pt]{minimal}
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\begin{document}$N^{-1/2}Q_N$\end{document} to converge to a Gaussian distribution. We show that this convergence holds if \documentclass[12pt]{minimal}
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\begin{document}$b$\end{document} and \documentclass[12pt]{minimal}
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\begin{document}$a$\end{document} are not too badly behaved. However, the good behavior of one kernel may compensate for the bad behavior of the other. The conditions are formulated in the spectral domain.