An Exact Multivariate Model-Based Structural Decomposition

Casals, José Francisco Forniés; Jerez, Miguel; Sotoca, Sonia

doi:10.1198/016214502760047087

Cited by 28 publications

(18 citation statements)

References 17 publications

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“…In order to account for seasonality in the DMI, we employed a state-space structural decomposition approach using MATLAB software. By so doing, a time series of DMI is decomposed into seasonal, cyclical and irregular components (Casals et al 2002). As we were interested in inter-annual variability, we used only the trend-cycle component in the correlation, regression, and wavelet analyses.…”

Section: Longitudinal Gravitational Centre Of the Long-term Fishery Datamentioning

confidence: 99%

Effects of climate variability on the distribution and fishing conditions of yellowfin tuna (Thunnus albacares) in the western Indian Ocean

2012

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Variations in the abundance and distribution of pelagic tuna populations have been associated with large-scale climate indices such as the Southern Oscillation Index in the Pacific Ocean and the North Atlantic Oscillation in the Atlantic Ocean. Similarly to the Pacific and Atlantic, variability in the distribution and catch rates of tuna species have also been observed in association with the Indian Ocean Dipole (IOD), a basin-scale pattern of sea surface and subsurface temperatures that affect climate in the Indian Ocean. The environmental processes associated with the IOD that drive variability in tuna populations, however, are largely unexplored. To better understand these processes, we investigated longline catch rates of yellowfin tuna and their distributions in the western Indian Ocean in relation to IOD events, sea surface water temperatures (SST) and estimates of net primary productivity (NPP). Catch per unit effort (CPUE) was observed to be negatively correlated to the IOD with a periodicity centred around 4 years. During positive IOD events, SSTs were relatively higher, NPP was lower, CPUE decreased and catch distributions were restricted to the northern and western margins of the western Indian Ocean. During negative IOD events, lower SSTs and higher NPP were associated with increasing CPUE, particularly in the Arabian Sea and seas surrounding Madagascar, and catches expanded into central regions of the western Indian Ocean. These findings provide preliminary insights into some of the key environmental features driving the distribution of yellowfin tuna in the western Indian Ocean and associated variability in fisheries catches.

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Section: Longitudinal Gravitational Centre Of the Long-term Fishery Datamentioning

confidence: 99%

Effects of climate variability on the distribution and fishing conditions of yellowfin tuna (Thunnus albacares) in the western Indian Ocean

2012

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“…4). Seasonal adjustment is a common practice in time series analysis to avoid strong seasonality masking other important signals (Casals et al. , 2002).…”

Section: Methodsmentioning

confidence: 99%

Time series analyses reveal transient relationships between abundance of larval anchovy and environmental variables in the coastal waters southwest of Taiwan

Hsieh

Chen

Chiu

et al. 2009

Fisheries Oceanography

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We investigated environmental effects on larval anchovy fluctuations (based on CPUE from 1980 to 2000) in the waters off southwestern Taiwan using advanced time series analyses, including the statespace approach to remove seasonality, wavelet analysis to investigate transient relationships, and stationary bootstrap to test correlation between time series. For large-scale environmental effects, we used the Southern Oscillation Index (SOI) to represent the El Niñ o Southern Oscillation (ENSO); for local hydrographic conditions, we used sea surface temperature (SST), river runoff, and mixing conditions. Whereas the anchovy catch consisted of a northern species (Engraulis japonicus) and two southern species (Encrasicholina heteroloba and Encrasicholina punctifer), the magnitude of the anchovy catch appeared to be mainly determined by the strength of Eng. japonicus (Japanese anchovy). The main factor that caused the interannual variation of anchovy CPUE might change through time. The CPUE showed a negative correlation with combination of water temperature and river runoff before 1987 and a positive correlation with river runoff after 1988. Whereas a significant negative correlation between CPUE and ENSOs existed, this correlation was driven completely by the lowfrequency ENSO events and explained only 10% of the variance. Several previous studies on this population emphasized that the fluctuations of larval anchovy abundance were determined by local SST. Our analyses indicated that such a correlation was transient and simply reflected ENSO signals. Recent advances in physical oceanography around Taiwan showed that the ENSOs reduced the strength of the Asian monsoon and thus weakened the China Coastal Current toward Taiwan. The decline of larval anchovy during ENSO may be due to reduced China Coastal Current, which is important in facilitating the spawning migration of the Japanese anchovy.

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“…These formulations were discussed by Anderson and Moore (1979) and more recently by Durbin and Koopman (2001, sec. 4.2.4) and Casals, Jerez, and Sotoca (2002).…”

Section: Unobserved Components Models With Commonmentioning

confidence: 97%

State Space Models With a Common Stochastic Variance

Koopman

Bos

2004

Journal of Business & Economic Statistics

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This article considers a combination of the linear Gaussian state space model and the stochastic volatility model. The focus is on the simultaneous estimation of parameters related to the stochastic processes of both the mean and variance parts of the model. Kalman lter and Monte Carlo maximum likelihood methods lead to an elegant estimation procedure for which the simulation error can be made arbitrarily small. The standard asymptotic properties of maximum likelihood estimators apply as a result. A Monte Carlo simulation study is carried out to investigate the small-sample properties of the estimation procedure. The modeling and forecasting techniques within our new framework are illustrated for U.S. monthly in ation rates.

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An Exact Multivariate Model-Based Structural Decomposition

Cited by 28 publications

References 17 publications

Effects of climate variability on the distribution and fishing conditions of yellowfin tuna (Thunnus albacares) in the western Indian Ocean

Effects of climate variability on the distribution and fishing conditions of yellowfin tuna (Thunnus albacares) in the western Indian Ocean

Time series analyses reveal transient relationships between abundance of larval anchovy and environmental variables in the coastal waters southwest of Taiwan

State Space Models With a Common Stochastic Variance

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