FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1588243567> ?p ?o ?g. }

• W1588243567 abstract "Time series modeling and analysis is central to most financial and econometric data modeling. With increased globalization in trade, commerce and finance, national variables like gross domestic productivity (GDP) and unemployment rate, market variables like indices and stock prices and global variables like commodity prices are more tightly coupled than ever before. This translates to the use of multivariate or vector time series models and algorithms in analyzing and understanding the relationships that these variables share with each other. Autocorrelation is one of the fundamental aspects of time series modeling. However, traditional linear models, that arise from a strong observed autocorrelation in many financial and econometric time series data, are at times unable to capture the rather nonlinear relationship that characterizes many time series data. This necessitates the study of nonlinear models in analyzing such time series. The class of bilinear models is one of the simplest nonlinear models. These models are able to capture temporary erratic fluctuations that are common in many financial returns series and thus, are of tremendous interest in financial time series analysis. Another aspect of time series analysis is homoscedasticity versus heteroscedasticity. Many time series data, even after differencing, exhibit heteroscedasticity. Thus, it becomes important to incorporate this feature in the associated models. The class of conditional heteroscedastic autoregressive (ARCH) models and its variants form the primary backbone of conditional heteroscedastic time series models. Robustness is a highly underrated feature of most time series applications and models that are presently in use in the industry. With an ever increasing amount of information available for modeling, it is not uncommon for the data to have some aberrations within itself in terms of level shifts and the occasional large fluctuations. Conventional methods like the maximum likelihood and least squares are well known to be highly sensitive to such contaminations. Hence, it becomes important to use robust methods, especially in this age with high amounts of computing power readily available, to take into account such aberrations. While robustness and time series modeling have been vastly researched individually in the past, application of robust methods to estimate time series models is still quite open. The central goal of this thesis is the study of robust parameter estimation of some simple vector and nonlinear time series models. More precisely, we will briefly study some prominent linear and nonlinear models in the time series literature and apply the robust S-estimator in estimating parameters of some simple models like the vector autoregressive (VAR) model, the (0, 0, 1, 1) bilinear model and a simple conditional heteroscedastic bilinear model. In each case, we will look at the important aspect of stationarity of the model and analyze the asymptotic behavior of the S-estimator." @default.
• W1588243567 created "2016-06-24" @default.
• W1588243567 creator A5011981597 @default.
• W1588243567 date "2010-01-01" @default.
• W1588243567 modified "2023-09-23" @default.
• W1588243567 title "Robust Multivariate and Nonlinear Time Series Models" @default.
• W1588243567 cites W1511796008 @default.
• W1588243567 cites W1535689967 @default.
• W1588243567 cites W1590093017 @default.
• W1588243567 cites W192975702 @default.
• W1588243567 cites W1930187923 @default.
• W1588243567 cites W1964524778 @default.
• W1588243567 cites W1975075722 @default.
• W1588243567 cites W1975285668 @default.
• W1588243567 cites W1976970516 @default.
• W1588243567 cites W1977569390 @default.
• W1588243567 cites W1979575715 @default.
• W1588243567 cites W1980262437 @default.
• W1588243567 cites W1981673657 @default.
• W1588243567 cites W1987333632 @default.
• W1588243567 cites W1997245915 @default.
• W1588243567 cites W1999814123 @default.
• W1588243567 cites W1999996900 @default.
• W1588243567 cites W2003036796 @default.
• W1588243567 cites W2005232262 @default.
• W1588243567 cites W2009721612 @default.
• W1588243567 cites W2015162326 @default.
• W1588243567 cites W2017887707 @default.
• W1588243567 cites W2020660743 @default.
• W1588243567 cites W2023229267 @default.
• W1588243567 cites W2024737188 @default.
• W1588243567 cites W2028725248 @default.
• W1588243567 cites W2029469239 @default.
• W1588243567 cites W2035753170 @default.
• W1588243567 cites W2041442087 @default.
• W1588243567 cites W2044503966 @default.
• W1588243567 cites W2044749499 @default.
• W1588243567 cites W2044752834 @default.
• W1588243567 cites W2054640351 @default.
• W1588243567 cites W2057824032 @default.
• W1588243567 cites W2057916813 @default.
• W1588243567 cites W2059384625 @default.
• W1588243567 cites W2062187186 @default.
• W1588243567 cites W2065296085 @default.
• W1588243567 cites W2069846606 @default.
• W1588243567 cites W2074812030 @default.
• W1588243567 cites W2078201173 @default.
• W1588243567 cites W2081169227 @default.
• W1588243567 cites W2084373150 @default.
• W1588243567 cites W2087544713 @default.
• W1588243567 cites W2090178007 @default.
• W1588243567 cites W2095701613 @default.
• W1588243567 cites W2107952665 @default.
• W1588243567 cites W2118234389 @default.
• W1588243567 cites W2124700543 @default.
• W1588243567 cites W2127476632 @default.
• W1588243567 cites W2134255835 @default.
• W1588243567 cites W2134752891 @default.
• W1588243567 cites W2146225536 @default.
• W1588243567 cites W2152701363 @default.
• W1588243567 cites W2155678210 @default.
• W1588243567 cites W2171618211 @default.
• W1588243567 cites W2325685092 @default.
• W1588243567 cites W2330761232 @default.
• W1588243567 cites W2408011753 @default.
• W1588243567 cites W2423293082 @default.
• W1588243567 cites W2903837271 @default.
• W1588243567 cites W2947626232 @default.
• W1588243567 cites W3100325525 @default.
• W1588243567 cites W3124084508 @default.
• W1588243567 cites W3146166473 @default.
• W1588243567 cites W36711357 @default.
• W1588243567 doi "https://doi.org/10.5075/epfl-thesis-4688" @default.
• W1588243567 hasPublicationYear "2010" @default.
• W1588243567 type Work @default.
• W1588243567 sameAs 1588243567 @default.
• W1588243567 citedByCount "0" @default.
• W1588243567 crossrefType "journal-article" @default.
• W1588243567 hasAuthorship W1588243567A5011981597 @default.
• W1588243567 hasConcept C101104100 @default.
• W1588243567 hasConcept C105795698 @default.
• W1588243567 hasConcept C121332964 @default.
• W1588243567 hasConcept C143724316 @default.
• W1588243567 hasConcept C149782125 @default.
• W1588243567 hasConcept C151406439 @default.
• W1588243567 hasConcept C151730666 @default.
• W1588243567 hasConcept C158622935 @default.
• W1588243567 hasConcept C159877910 @default.
• W1588243567 hasConcept C162324750 @default.
• W1588243567 hasConcept C24338571 @default.
• W1588243567 hasConcept C33923547 @default.
• W1588243567 hasConcept C42536954 @default.
• W1588243567 hasConcept C5297727 @default.
• W1588243567 hasConcept C62520636 @default.
• W1588243567 hasConcept C86803240 @default.
• W1588243567 hasConceptScore W1588243567C101104100 @default.
• W1588243567 hasConceptScore W1588243567C105795698 @default.
• W1588243567 hasConceptScore W1588243567C121332964 @default.
• W1588243567 hasConceptScore W1588243567C143724316 @default.
• W1588243567 hasConceptScore W1588243567C149782125 @default.

• next