Results 11 to 20 of about 1,472,346 (301)
Formulating State Space Models in R with Focus on Longitudinal Regression Models
Journal of Statistical Software, 2006 We provide a language for formulating a range of state space models with response densities within the exponential family. The described methodology is implemented in the R-package sspir. A state space model is specified similarly to a generalized linear
Claus Dethlefsen +1 more
doaj +4 more sources
Journal of Statistical Software, 2011 We give an overview of some of the software tools available in R, either as built- in functions or contributed packages, for the analysis of state space models.
Giovanni Petris, Sonia Petrone
doaj +1 more source
Fitting State Space Models with EViews
Journal of Statistical Software, 2011 This paper demonstrates how state space models can be fitted in EViews. We first briefly introduce EViews as an econometric software package. Next we fit a local level model to the Nile data.
Filip A. M. Van den Bossche
doaj +1 more source
The STAMP Software for State Space Models
Journal of Statistical Software, 2011 This paper reviews the use of STAMP (Structural Time Series Analyser, Modeler and Predictor) for modeling time series data using state-space methods with unobserved components.
Roy Mendelssohn
doaj +1 more source
Bayesian State Space Models in Macroeconometrics [PDF]
SSRN Electronic Journal, 2020 AbstractState space models play an important role in macroeconometric analysis and the Bayesian approach has been shown to have many advantages. This paper outlines recent developments in state space modelling applied to macroeconomics using Bayesian methods.
Joshua C.C. Chan, Rodney W. Strachan
openaire +4 more sources
CoRR, 2023 This paper studies sequence modeling for prediction tasks with long range dependencies. We propose a new formulation for state space models (SSMs) based on learning linear dynamical systems with the spectral filtering algorithm (Hazan et al. (2017)).
Naman Agarwal +3 more
openaire +2 more sources
Graphical State Space Model [PDF]
2021 IEEE International Conference on Unmanned Systems (ICUS), 2021 In this paper, a new framework, named as graphical state space model, is proposed for the real time optimal estimation of a class of nonlinear state space model. By discretizing this kind of system model as an equation which can not be solved by Extended Kalman filter, factor graph optimization can outperform Extended Kalman filter in some cases.
openaire +2 more sources
Standard State Space Models of Unawareness (Extended Abstract) [PDF]
Electronic Proceedings in Theoretical Computer Science, 2016 The impossibility theorem of Dekel, Lipman and Rustichini has been thought to demonstrate that standard state-space models cannot be used to represent unawareness. We first show that Dekel, Lipman and Rustichini do not establish this claim.
Peter Fritz, Harvey Lederman
doaj +1 more source
Modeling Volatility Using State Space Models [PDF]
International Journal of Neural Systems, 1997 In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, we show that empirical volatilities (the squared relative returns of prices) exhibit a significant amount of
Jens Timmer, Andreas S. Weigend
openaire +2 more sources
State-space models in estimating Lithuanian Business Cycle
Lietuvos Matematikos Rinkinys, 2005 There is not abstract.
Audronė Jakaitienė
doaj +3 more sources