2008
DOI: 10.1016/j.jspi.2007.01.007
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Prediction in moving average processes

Abstract: Abstract. For the stationary invertible moving average process of order one with unknown innovation distribution F , we construct root-n consistent plug-in estimators of conditional expectations E(h(Xn+1)|X1, . . . , Xn). More specifically, we give weak conditions under which such estimators admit Bahadur type representations, assuming some smoothness of h or of F . For fixed h it suffices that h is locally of bounded variation and locally Lipschitz in L2(F ), and that the convolution of h and F is continuousl… Show more

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Cited by 5 publications
(3 citation statements)
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“…Moving averages are commonly used for time series to smooth out short‐term fluctuations and highlight longer term trends or cycles (Schick and Wefelmeyer, 2008). The signals can be actually regarded as being composed of a large number of frequencies, short‐term fluctuations are smoothed out and long‐term trends are reserved with the size of the subset being averaged growing.…”
Section: Resultsmentioning
confidence: 99%
“…Moving averages are commonly used for time series to smooth out short‐term fluctuations and highlight longer term trends or cycles (Schick and Wefelmeyer, 2008). The signals can be actually regarded as being composed of a large number of frequencies, short‐term fluctuations are smoothed out and long‐term trends are reserved with the size of the subset being averaged growing.…”
Section: Resultsmentioning
confidence: 99%
“…Correlation analysis. Moving averages are commonly used for time series to smooth out short-term fluctuations and highlight longer term trends or cycles (Schick and Wefelmeyer, 2008). The signals can be actually regarded as being composed of a large number of frequencies, short-term fluctuations are smoothed out and long-term trends are reserved with the size of the subset being averaged growing.…”
Section: Time-varying Response Characteristicsmentioning
confidence: 99%
“…For the MA(1) model X t = ε t − ϑε t−1 with |ϑ| < 1 and innovations ε t , t ∈ Z, that are i.i.d. with finite variance, Schick and Wefelmeyer (2006b) construct root-n consistent estimators for the random variable q(h) when m = 1. We generalize their result to arbitrary invertible linear processes and to arbitrary m.…”
Section: Introductionmentioning
confidence: 99%