MARSS Package Manual
Preface
Installation
Authors
Citation
Acknowledgments
Part 1. Overview
1
Overview
1.1
MARSS model form
1.2
Example model
1.3
Notes
2
Quick Start
2.1
Model specification
2.2
Linear constraints
2.3
Time-varying parameters
2.4
Inputs
c
and
d
2.5
Tips and Tricks
3
Data format
3.1
ts
objects
3.2
tsibble
objects
4
Model specification
4.1
General matrix specification
4.2
Linear constraints
4.3
Time-varying parameters
4.4
Examples for each parameter
5
Covariates format
Part 2. Short Examples
6
Common output for fits
7
Univariate Models
7.1
Random walk with drift
7.2
Random walk with time-varying parameters
7.3
AR(1) observed with error
7.4
Linear regression (LR)
7.5
LR with AR(1) errors
7.6
LR with AR(1) errors and independent errors
7.7
LR with AR(1) errors driven by covariate
7.8
Flat level model
7.9
Linear trend model
7.10
Stochastic level model
7.11
Stochastic slope model
8
Multivariate Models
8.1
RW observed with multiple time series
8.2
Multiple time series each observing a RW
8.3
Different time series observing a RW
8.4
Trend observed with AR(1) error
8.5
Dynamic Factor Model with 3 trends
8.6
Linear constraints
Part 3. Outputs
9
Parameter estimates
10
States and smoothed estimates
11
Model fits
12
Residuals
13
Confidence Intervals
14
Predictions and forecasts
Part 4. Tips and Tricks
15
Troubleshooting
16
Algorithm notes and cautions
16.1
Properly constrained models
16.2
Notes on the Kalman filter
16.3
Notes on the EM algorithm
16.4
Bias in variance estimates
16.5
Careful if specifying a prior on initial conditions
16.6
State-space form of ARMA(p,q) models
17
Other related packages
References
Published with bookdown
MARSS R Package
Chapter 14
Predictions and forecasts