HAC Corrections for Strongly Autocorrelated Time Series | PERPUSTAKAAN UNIVERSITAS KATOLIK PARAHYANGAN

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HAC Corrections for Strongly Autocorrelated Time Series

Muller, Ulrich K. - Nama Orang;

Applied work routinely relies on heteroscedasticity and autocorrelation consistent (HAC) standard errors when conducting inference in a time series setting. As is well known, however, these corrections perform poorly in small samples under pronounced autocorrelations. In this article, I first provide a review of popular methods to clarify the reasons for this failure. I then derive inference that remains valid under a specific form of strong dependence. In particular, I assume that the long-run properties can be approximated by a stationary Gaussian AR(1) model, with coefficient arbitrarily close to one. In this setting, I derive tests that come close to maximizing a weighted average power criterion. Small sample simulations show these tests to perform well, also in a regression context.

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Barcode		Tipe Koleksi	Nomor Panggil	Lokasi	Status
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Informasi Detail

Judul Seri: JOURNAL OF BUSINESS & ECONOMIC STATISTICS; Vol.32 No.3 July 2014
No. Panggil: -
Penerbit: : .,
Deskripsi Fisik: p. 311-340
Bahasa: English
ISBN/ISSN: -
Klasifikasi: -
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: -
Subjek: AR(1)
LOCAL-TO-UNITY
LONG-RUN VARIANCE
Info Detail Spesifik: https://doi.org/10.1080/07350015.2014.931238
Pernyataan Tanggungjawab: Ulrich K. Muller

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