|Statement||Richard J. Smith and A.M. Robert Taylor.|
|Series||Discussion paper / University of Bristol, Department of Economics -- no.98/444, Discussion paper (University of Bristol, Department of Economics) -- no.98/444.|
|Contributions||Taylor, A.M. Robert.|
tests for unit roots at the seasonal frequencies in quarterly time series. We de-velop likelihood ratio tests for seasonal unit roots and show that these tests are “nearly efﬁcient” in the sense of Elliott, Rothenberg & Stock (), i.e. that their asymptotic local power functions are indistinguishable from the Gaussian power envelope. Abstract: In an important generalization of zero frequency autoregressive unit root tests, Hylleberg, Engle, Granger, and Yoo () developed regression-based tests for unit roots at the seasonal frequencies in quarterly time series. We develop. a likelihood-ratio test for seasonal unit roots. By Robert M. Kunst. Download PDF (1 MB) Abstract. summary: a new test for the presence of seasonal unit roots in a quarterly time series, i.e. for seasonal integratedness, is constructed. a seasonally integrated process is characterized by a factor 1-l4 in its autoregressive representation. the Author: Robert M. Kunst. Nearly Efficient Likelihood Ratio Tests for Seasonal Unit Roots. By Michael Jansson, Morten Ørregaard Nielsen and Margit Sommer. Get PDF ( KB) Cite. BibTex; Full citation Publisher: Elsevier BV. Year: DOI identifier: /ssrn OAI identifier: Provided by: MUCC (Crossref).
Unconditional maximum likelihood estimation is considered for an autoregressive moving average that may contain an autoregressive unit root. The limiting distribution of the normalized maximum like. 2 Likelihood Ratio Tests for Seasonal Unit Roots No Deterministic Component Suppose fy t: 1 t Tgis an observed univariate quarterly time series generated by the zero-mean Gaussian AR(4) model r(L)yt =e t; (1) where r(L) is a lag polynomial of order four, et ˘i:i:d: N (0;1); and the initial conditions are y 3 = = y 0 = Following RT we assume that r(L) admits the. The seasonal unit root tests make it possible to determine the nature of the deterministic and stochastic seasonal fluctuations. In Section 2, we define the main seasonal time series models and the seasonal integration notion. Section 3 describes the HEGY test procedure. Perron ()/Rappoport-Reichlin () unit-root test with a trend shift at date k, computed sequentially for k = ko,, T - k0, where ko allows for trimming the initial and final parts of the sample. Another se- quential statistic that will prove useful in our empirical analysis is Quandt's () likelihood-ratio .
• Why are unit roots important? (1) Interesting to know if shocks have permanent or transitory eﬀects. (2) It is important for forecasting to know if the process has an attractor. and perform a likelihood ratio test. The 5% critical value for this test is 22 of Likelihood ratio tests for seasonal unit roots. Journal of Time Series Analysis, 20(4), [ bib ] 7: Robert AM Taylor. Robust stationarity tests in seasonal time series processes. Journal of Business & Economic Statistics, 21(1), [ bib ] 8: AM Robert Taylor. Variance ratio tests of the seasonal unit root hypothesis. Nearly Efficient Likelihood Ratio Tests for Seasonal Unit. unit roots in ARMA representations. Unobserved-components models assume all unit-root components to be there and may test for their “signiﬁcance”. For example (ns=non-seasonal, s=seasonal part): y t= yns +ys, ∆yns t = ε ns t, 1+B +B2+ +Bs−1 ys t = ε s t Here, a crucial parameter is the variance ratio .