NON-GAUSSIAN LOG-PERIODOGRAM REGRESSION

By Carlos Velasco. Econometric Theory, 2000

102 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/759a94aafc83e639f3bd813fcb93969cc74acf7c

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Citations in the corpus

A BIAS-REDUCED LOG-PERIODOGRAM REGRESSION ESTIMATOR FOR THE LONG-MEMORY PARAMETER (2003, methodology): “…estimators of Robinson (1995b), the pooled and/or tapered log-periodogram regression estimators for stationary non-Gaussian series analyzed by Velasco (2000), the modified log-periodogram estimator of Kim and Phillips (1999b) for nonstationary time series, the tapered log-periodogram…”
Estimators of Long-Memory: Fourier versus Wavelets (2008, background): “ator dbGPH(m n) should be slightly modiﬁed to avoid a numberof Fourier frequencies near 0. In addition, tapering and pooling are then required, even if the process Xis stationary and invertible; see (Velasco, 2000, Theorem 3) and Fay¨ et al. (2004). WAVELET ESTIMATORS OF LONG-MEMORY 29 8.2. Asymptot”
Log Periodogram Regression: The Nonstationary Case (2006, unknown): “s=1 ln s 1 sd−1 → 0, (27) for all d > 1.”
Nonlinear Log-Periodogram Regression for Perturbed Fractional Processes (2003, background): “Nevertheless, Gaussianity is restrictive in some empirical applications and could be relaxed following the lines of recent work by Velasco (2000) and Deo and Hurvich (2001), although we have not done so here.”
MEMORY PARAMETER ESTIMATION IN THE PRESENCE OF LEVEL SHIFTS AND DETERMINISTIC TRENDS (2013, unknown): “We conducted numerous simulation experiments to assess the bias and RMSE of the robust estimators under a variety of distributional assumptions on the {vt } process following the design of Velasco (2000).”
Level Shifts and the Illusion of Long Memory in Economic Time Series (2004, background): “For fully non-Gaussian processes, such as the RLS process in (3), existing theoretical results require that the periodogram ordinates be pooled across frequencies before the log periodogram regression is run (Velasco 2000).”
Finite Sample Comparison of Parametric, Semiparametric, and Wavelet Estimators of Fractional Integration (2005, methodology): “Under suitable regularity conditions, including yt being Gaussian (later relaxed by Velasco (2000)) and a restriction on the bandwidth, Robinson (1995b) derived the asymptotically normal limit distribution for the LPR estimator when d is in the stationary and invertible range (−1/2, 1/2) .”
Residual Log-Periodogram Inference for Long-Run-Relationships (2002, unknown): “13) in Velasco (2000). Set MT ‘ 2ðd dÞ log(3) T oð1Þ as T !1 by Assumption A.”
Unit root log periodogram regression (2007, background): “Velasco (2000) showed consistency of an LP regression estimator under non-Gaussian ut when d 2 ð0; 12Þ and periodogram ordinates are pooled and frequencies are trimmed from the origin.”