XVI Escola de Séries Temporais e Econometria
http://www.ime.usp.br/~abe/este2015/
XVI Escola de Séries Temporais e Econometria
Conferências
Marcelo Fernandes (FGV-SP)
Dani Gamerman (UFRJ)
S.J. Koopman (VU Amsterdam)
Alexandra Schmidt (UFRJ)
Ruey Tsay (Booth School of Business - Chicago)
Brani Vidakovic (GeorgiaTech)
Mauricio Zevallos (UNICAMP)
Wilfredo Palma (UC Chile)
ST1: High Dimensional Time Series
Chairman: Flávio Ziegelmann
Siem J. Koopman
Marcelo Medeiros
Guilherme Moura
ST2: High Dimension Volatility Models
Chairman: Hedibert Lopes
Ruey Tsay
Hedibert Lopes
André Portela
ST3: Discrete-Valued Time Series
Chairman: Valdério Reisen
Wilfredo Palma
Glaura Franco
Klaus Vasconcelos
ST4: Regularized Regression and Classification
Chairman: Marcelo Fernandes
Brani Vidakovic
Eduardo Mendes
Aluisio Pinheiro
Minicurso
State Space Models: Theory, Methods and Applications
David Stoffer (U. Pittsburgh)
The state space model (SSM) or the hidden Markov Model (HMM) is a very general model that subsumes a whole class of special cases of interest in much the same way that regression does. For example, the linear Gaussian model includes such varied models ARMA models as well as smoothing splines. Nonlinear state space models are used in finance, in particular, stochastic volatility as well as computational ecology to study population dynamics and to track animals. While inference for linear state space models is fairly simple using numerical optimization based on derivatives, inference in the nonlinear case is difficult and relies on derivative free numerical optimization. I will introduce the basic model along with some theory and applications. I will then introduce a variety of nonlinear models, and discuss applications and inference for these models based on Monte Carlo methods including MCEM, Metropolis-Hastings, and particle methods.
Tutoriais
T1 - Time-varying Copulas - Flávio Ziegelmann
T2 - An Introduction to Singular Spectrum Analysis - Paulo Canas Rodrigues
T3 - Bayesian Regularization - Hedibert Lopes
T4 - Resampling Techniques for Nonstationary Time Series - Jacek Leskow
XVI Escola de Séries Temporais e Econometria
Conferências
Marcelo Fernandes (FGV-SP)
Dani Gamerman (UFRJ)
S.J. Koopman (VU Amsterdam)
Alexandra Schmidt (UFRJ)
Ruey Tsay (Booth School of Business - Chicago)
Brani Vidakovic (GeorgiaTech)
Mauricio Zevallos (UNICAMP)
Wilfredo Palma (UC Chile)
ST1: High Dimensional Time Series
Chairman: Flávio Ziegelmann
Siem J. Koopman
Marcelo Medeiros
Guilherme Moura
ST2: High Dimension Volatility Models
Chairman: Hedibert Lopes
Ruey Tsay
Hedibert Lopes
André Portela
ST3: Discrete-Valued Time Series
Chairman: Valdério Reisen
Wilfredo Palma
Glaura Franco
Klaus Vasconcelos
ST4: Regularized Regression and Classification
Chairman: Marcelo Fernandes
Brani Vidakovic
Eduardo Mendes
Aluisio Pinheiro
Minicurso
State Space Models: Theory, Methods and Applications
David Stoffer (U. Pittsburgh)
The state space model (SSM) or the hidden Markov Model (HMM) is a very general model that subsumes a whole class of special cases of interest in much the same way that regression does. For example, the linear Gaussian model includes such varied models ARMA models as well as smoothing splines. Nonlinear state space models are used in finance, in particular, stochastic volatility as well as computational ecology to study population dynamics and to track animals. While inference for linear state space models is fairly simple using numerical optimization based on derivatives, inference in the nonlinear case is difficult and relies on derivative free numerical optimization. I will introduce the basic model along with some theory and applications. I will then introduce a variety of nonlinear models, and discuss applications and inference for these models based on Monte Carlo methods including MCEM, Metropolis-Hastings, and particle methods.
Tutoriais
T1 - Time-varying Copulas - Flávio Ziegelmann
T2 - An Introduction to Singular Spectrum Analysis - Paulo Canas Rodrigues
T3 - Bayesian Regularization - Hedibert Lopes
T4 - Resampling Techniques for Nonstationary Time Series - Jacek Leskow
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