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1answer
22 views

Correlated state-space models

I'm struggling with Reinsel's book "Elements of Multivariate Time Series Analysis," because I thought that it would be a good idea to switch from Vector ARMA to state-space representations; ...
1
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1answer
53 views

Constants in a DLM Model R

Good afternoon, I am attempting to fit a state space model of the form: $$ (S_t- \mu) = G*(S_{t-1} - \mu) + E_t $$ $$ Y = F*S_t + v_t $$ Where $Y$ is nx1, $G$ is 3x3, $S_t$ is 3x1, $\mu$ is 3x1, and ...
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1answer
29 views

Kalman filter and Box-Cox

I'm interested in wind forecasting, which I have analyzed over some time by means of ARMA methods. Now I've being reading about Kalman filtering. Kalman filter is optimal when Gaussian assumption can ...
2
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1answer
34 views

probability definition of time and place event help in definition representation

I have a probability question: How can I represent the probability of an event occurring at a specific {time,place}? How are the time and place represented? Example: I want to represent the ...
2
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1answer
80 views

Random walk with drift in dynamic linear model

Suppose I have a dynamic linear model as defined in the dlm-package for R, see Petris 2009. $y_t = F_t θ_t + ν_t, ν_t$~$N(0,V_t)$ $θ_t = G_t ...
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0answers
27 views

Gibbs sampler for local linear trend model

Question: Consider the local linear trend model given by: \begin{align*} y_t = \mu_t + \tau \varepsilon_t \ \cdots \ \text{Observation equation} \\ \mu_{t+1} = \phi \mu_t + \eta_t \ \cdots \ ...
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0answers
44 views

A question on making prediciton from state space model

I am trying to use JAGS to make prediction from a state space model. I use JAGS to estimate parameters in the model and make prediction. I plot the hist graph for a.new and r.new, the graph for r.new ...
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0answers
89 views

How does JAGS deal with state space models?

I am trying to use JAGS to deal with the following multivariate state space model. $Y_t=X_t\theta_t+\epsilon_t$ $\theta_t=\theta_{t-1}+\nu_t$ JAGS code is neat but JAGS is running too slow when I ...
4
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1answer
185 views

Explaining Kalman filters in state space models

What are the steps involved in the use of Kalman filters in state space models? I have seen a couple of different formulations, but I'm not sure about the details. For example, Cowpertwait starts ...
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0answers
40 views

Weighting data sources in a bayesian model (BUGS)

I use a state space model to fit observations to a population dynamic model (using the BUGS language). In the "state" part, the dynamic model create a new "state" of the population (i.e. size and ...
1
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2answers
85 views

How do I perform a multi-state decomposition with interaction effects?

I am trying to perform a decomposition with interaction effects. This paper provides a solution for n-factors where each factor has a binary state (see section 2). I have a problem with 2 factors, ...
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0answers
73 views

Sensible Transformations of Economic Indices like CFNAI and ADSBCI in Time Series Analysis

I am trying to fit an unobserved components model for revenue and transactions for a firm where I also use some exogenous variables that capture economic conditions. The UCM decomposes a time series ...
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0answers
36 views

Introducing observation errors in jags code

I want to introduce observations errors around my data in Jags, but I face some trouble coding it without having a double definition error on node Y3 So far I have : ...
5
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1answer
407 views

Dynamic factor analysis vs state space model

The MARSS package in R offers function for dynamic factor analysis. In this package, the dynamic factor model is written as a special form of state space model and they assume the common trends follow ...
2
votes
0answers
59 views

Rao-Blackwellising state space for a (marginalised) particle filter

I am starting to look at particle filtering for a problem that I have. In particular, I would like to reduce the dimensionality of the particles. The model that I have is able to be partitioned. ...
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0answers
213 views

State space representation using KFAS package

I am using KFAS package for R. You can run install.packages("KFAS") library(KFAS) ?regSSM ...
1
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0answers
99 views

Poisson State Space with AR(1) latent process

I have been trying to use sspir R package to estimate the following Poisson model: $Y_{t}\sim Po(\exp(\lambda_{t}));$ such that $\lambda_{t}=X_{t}\beta +\gamma_{t}$ and ...
2
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2answers
222 views

Exogenous variables in dlm package

I have been trying to estimate state space models using dlm package in R. The problem is that the model I am estimating requires inclusion of a few exogenous variables. I still can't figure out how to ...
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0answers
103 views

Criticism, please: simplifying state space model

As a little experiment, I am extending a nice, interpretable AR/MA relationship between a security $r$ that is variably influenced by the previous $k$ time points over another security $f$. These ...
1
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0answers
62 views

Building artificial state space model from noise-less data

I have a discrete time stochastic process, where at each time the state of the system $X_t$ is given by: $$ X_t = f_\theta(X_{t-1},\epsilon_t), \; \; \text{for} \; t = 1,\dots,T $$ and, for example, ...
3
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2answers
471 views

Confidence intervals for exponential smoothing

I'm using exponential smoothing (Brown's method) for forecasting. The forecast can be calculated for one or more steps (time intervals). Is there any way to calculate confidence intervals for such ...
0
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1answer
208 views

Assumption of Gaussian distribution of acceleration

I have a data set consisting of noisy position values of a trajectory of a human hand. I want to estimate a generative model of these trajectories, and the obvious choice is a Kalman Filter/linear ...
2
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1answer
595 views

Estimating State Space Model in R with MARSS package and shared parameters between Q and R

I am trying to estimate the following unobserved components model using the MARSS package $y_t = \mu_t + \varepsilon_t $ $\mu_t = \mu_{t-1} + \beta_{t-1}$ $\beta_t = \beta_{t-1} + \zeta_{t}$ with ...
6
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1answer
809 views

Kalman filter vs. smoothing splines

Q: For which data is it appropriate to use state-space modeling and Kalman filtering instead of smoothing splines and vice versa? Is there some equivalence relationship between the two? I'm trying ...
13
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1answer
356 views

How to check which model is better in state space time series analysis?

I am doing time series data analysis by state space methods. With my data the stochastic local level model totally outperformed the deterministic one. But the deterministic level and slope model gives ...