A necessary and sufficient condition for asymptotic independence of discrete Fourier transforms under short- and long-range dependence

By Soumendra Nath Lahiri. 2003

73 citations recorded by the source corpus.

Subfields: Applied Mathematics, Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/c444285867ea402ac40eba3464a493803d004938

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Citations in the corpus

TFT-bootstrap: Resampling time series in the frequency domain to obtain replicates in the time domain (2011, background): “Lahiri [35] gives necessary as well as sufficient conditions for the asymptotic independence and normality of tapered as well as nontapered Fourier coefficients for a much larger class of time series not limited to linear processes in the strict sense.”
Bootstrap Methods for Time Series (2003, background): “…transforms Y n (λ 1 ), . . . , Y n (λ k ) are asymptotically independent for any set of distinct ordinates −π < λ 1 < · · · < λ k ≤ π (cf. Brockwell and Davis (1991), Lahiri (2003b)); (ii) The original observations X n admit a representation in terms of the transformed values Y n = {Y n (w j ) :…”
Nonstationarity-Extended Local Whittle Estimation (2007, unknown): “Under some additional regularity assumptions such as in Lahiri (2003), they satisfy var(ηj) ≤ C for all 1 ≤ j ≤ m, and cov(ηj , ηk)→ 0 for j, k →∞ and j 6= k.”
Asymptotic spectral theory for nonlinear time series (2006, unknown): “ar X iv :m at h/ 06 11 02 9v 2 [ m at h. ST ] 2 4 O ct 2 00 7 The Annals of Statistics 2007, Vol. 35, No. 4, 1773–1801 DOI: 10.1214/009053606000001479 c© Institute of Mathematical Statistics, 2007”
A frequency domain empirical likelihood for short- and long-range dependence (2007, background): “This classification resembles that of [25] and encompasses the formulation of LRD described in [4, 40].”
Consistent estimation of the memory parameter for nonlinear time series (2006, background): “[See Lahiri, 2003, for sufficient and necessary conditions for the validity of eqn (5).]”
Estimators of Long-Memory: Fourier versus Wavelets (2008, methodology): “The consistency and asymptotic normality of the GPH have been established by Robinson (1995b) for stationary invertible Gaussian M(d) process −1/2 d 1/2 with no data taper (τ = 0) and no differencing (δ = 0).”