Seasonal Nonstationarity and Near‐Nonstationarity

Ghysels, Éric; Osborn, Denise R.; Rodrigues, Paulo M. M.

doi:10.1002/9780470996249.ch32

Cited by 9 publications

(8 citation statements)

References 59 publications

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“…This has led over the years to the development of numerous unit root tests; see, for instance, Stock (1994), Maddala and Kim (1998) and Phillips and Xiao (1998) for extensive reviews. A similar phenomenon has also been observed in the context of seasonally unadjusted economic time series; see, for instance, Ghysels and Osborn (2001) and Ghysels et al (2000) for overviews.…”

Section: Introductionsupporting

confidence: 69%

On LM-type tests for seasonal unit roots in the presence of a break in trend

Nunes

Rodrigues

2010

Journal of Time Series Analysis

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This article proposes tests for seasonal unit roots allowing for the presence of a break in the trend slope occurring at an unknown date. In particular, new LM-type tests are derived based on the framework introduced by Hylleberg et al. (1990). Null asymptotic distributions are derived for the no break case as well as when a break is present in the data-generating process and a Monte Carlo investigation on the finite sample size and power performance of the new procedures is presented.

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Section: Introductionsupporting

confidence: 69%

On LM-type tests for seasonal unit roots in the presence of a break in trend

Nunes

Rodrigues

2010

Journal of Time Series Analysis

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“…Y t is a stationary and invertible ARMA process of the type described by equations (3) [13]. The simplest seasonal model for non-stationary variables is the seasonal random walk (SRW): Y t = Y t-S + ε t .…”

Section: Methodsmentioning

confidence: 99%

The Health Effects of Climate Change: A Survey of Recent Quantitative Research

et al. 2010

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Abstract:In recent years there has been a large scientific and public debate on climate change and its direct as well as indirect effects on human health. In particular, a large amount of research on the effects of climate changes on human health has addressed two fundamental questions. First, can historical data be of some help in revealing how short-run or long-run climate variations affect the occurrence of infectious diseases? Second, is it possible to build more accurate quantitative models which are capable of predicting the future effects of different climate conditions on the transmissibility of particularly dangerous infectious diseases? The primary goal of this paper is to review the most relevant contributions which have directly tackled those questions, both with respect to the effects of climate changes on the diffusion of non-infectious and infectious diseases, with malaria as a case study. Specific attention will be drawn on the methodological aspects of each study, which will be classified according to the type of quantitative model considered, namely time series models, panel data and spatial models, and non-statistical approaches. Since many different disciplines and approaches are involved, a broader view is necessary in order to provide a better understanding of the interactions between climate and health. In this respect, our paper also presents a critical summary of the recent literature related to

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“…(12) represents an ECM where 7 The HEGY test implies if one concludes a seasonal series is stationary by rejecting the three hypotheses it is equivalent to testing and rejecting the overall hypothesis π 1 = π 2 = π 3 = π 4 at the same significance level. Ghysels, Osborn and Rodrigues (2001) perceive a problem with this. Using the same significance level for each of the three mutually exclusive HEGY tests, does not imply the same level of significance for the overall test.…”

Section: Cointegration and Ecm Testingmentioning

confidence: 91%

Outbound Business Travel Depends on Business Returns: Australian Evidence

Collins

Tisdell

2004

Australian Economic Papers

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In an earlier note, Collins and Tisdell (2002b) explored the possibility of a long-run relationship between Australian business returns and international business travel. Using annual data they found that such a relationship exists. The purpose of this study is to further examine this relationship using quarterly data for the time frame 1974:1 to 1999:4. In addition, previous studies on international business travel have offered some but not strong evidence for the existence of a positive relationship between the level of international business travel and real GDP of the origin country. This study suggests that the aggregate return on business investments is a better predictor of international business travel than GDP. The Engle-Granger and Johansen's maximum-likelihood cointegration procedures are used to show a long-term relationship exists between Australian outbound business travel and Australian business returns, but not with Real Australian GDP. Reasons for this relationship are discussed. Copyright Blackwell Publishing Ltd/University of Adelaide and Flinders University of South Australia 2004.

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Seasonal Nonstationarity and Near‐Nonstationarity

Cited by 9 publications

References 59 publications

On LM-type tests for seasonal unit roots in the presence of a break in trend

On LM-type tests for seasonal unit roots in the presence of a break in trend

The Health Effects of Climate Change: A Survey of Recent Quantitative Research

Outbound Business Travel Depends on Business Returns: Australian Evidence

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