Absorption of shocks in nonlinear autoregressive models
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Signal extraction and filtering by linear semiparametric methods
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Jackknife Estimator for Tracking Error Variance of Optimal Portfolios
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The forecasting ability of Internet-based virtual futures market
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On asymptotic theory for multivariate GARCH models
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Multi-Optimisation Consensus Clustering
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Posterior probability profiles for the automated assessment of the recovery of stroke patients
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Multivariate discount weighted regression and local level models
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Modeling non-Gaussian time-varying vector autoregressive processes by particle filtering
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Robust estimation of periodic autoregressive processes in the presence of additive outliers
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The multiple hybrid bootstrap - Resampling multivariate linear processes
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Models of the minimum wage impact upon employment, wages and prices: the Romanian case
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The effect of cooling functions on ensemble clustering using simulated annealing
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Estimating structural VARMA models with uncorrelated but non-independent error terms
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Different Approaches to Forecast Interval Time Series: A Comparison in Finance
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Estimating m-regimes STAR-GARCH model using QMLE with parameter transformation
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PC3: Principal Component-based Context Compression
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MMES'11/DEEE'11/COMATIA'11 Proceedings of the 2nd international conference on Mathematical Models for Engineering Science, and proceedings of the 2nd international conference on Development, Energy, Environment, Economics, and proceedings of the 2nd international conference on Communication and Management in Technological Innovation and Academic Globalization
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Information Transmission and the Bullwhip Effect: An Empirical Investigation
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Detecting effective connectivity in networks of coupled neuronal oscillators
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RainMon: an integrated approach to mining bursty timeseries monitoring data
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Dual time-frequency domain system identification
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Test of independence for functional data
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Two-step adaptive model selection for vector autoregressive processes
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Corrected portmanteau tests for VAR models with time-varying variance
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Exploring an optimal vector autoregressive model for multi-channel pulmonary sound data
Computer Methods and Programs in Biomedicine
Asymptotic distributions for quasi-efficient estimators in echelon VARMA models
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Simultaneous confidence bands for sequential autoregressive fitting
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Stationary-sparse causality network learning
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This reference work and graduate level textbook considers a wide range of models and methods for analyzing and forecasting multiple time series. The models covered include vector autoregressive, cointegrated, vector autoregressive moving average, multivariate ARCH and periodic processes as well as dynamic simultaneous equations and state space models. Least squares, maximum likelihood, and Bayesian methods are considered for estimating these models. Different procedures for model selection and model specification are treated and a wide range of tests and criteria for model checking are introduced. Causality analysis, impulse response analysis and innovation accounting are presented as tools for structural analysis. The book is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their tasks. It bridges the gap to the difficult technical literature on the topic.