dse-package {dse} R Documentation

## Dynamic Systems Estimation - Multivariate Time Series Package

### Description

Functions for time series modeling, including multi-variate state-space and ARMA (VAR, ARIMA, ARIMAX) models.

### Details

A Brief User's Guide is distributed with dse as a vignette. The package implements an R/S style object approach to time series modeling. This means that different model and data representations can be implemented with fairly simple extensions to the package.

The package includes methods for simulating, estimating, and converting among different model representations. These are mainly in dse. Package EvalEst has methods for studying estimation techniques and for examining the forecasting properties of models. There are also functions for forecasting and for evaluating the performance of forecasting models, as well as functions for evaluating model estimation techniques.

 Package: dse Depends: R, setRNG, tframe License: free, see LICENSE file for details. URL: http://tsanalysis.r-forge.r-project.org/

The main objects are:

TSdata

time series input and output data structure

TSmodel

a DSE model structure

TSestModel

model, data and some estimation information

The main general methods are:

TSdata

create, extract a DSE data structure

TSmodel

create, extract a DSE model structure

simulate

simulate a model to produce artifical data

toSS

convert to a state-space model

toARMA

convert to an ARMA model

ARMA

construct an ARMA model

SS

construct a state-space model

l

evaluate a model with data

smoother

calculate the smoothed state estimate

The main estimation methods are:

estVARXls

estimate an ARMA model with least squares

estVARXar

estimate an ARMA model with ar

estSSfromVARX

calculate a state-space model from an estimated VAR model

bft

a (usually) good “black-box” estimated model

estMaxLik

estimate a model using maximum likelihood

The main diagnositic methods are:

checkResiduals

autocorrelation diagnostics

informationTests

calculate several information tests for a model

McMillanDegree

calculate the McMillanDegree of a model

stability

calculate the stability of a model

roots

calculate the roots of a model

The methods for producing and evaluating forecasts are:

l

evaluate a model with data (and simple forecasts)

forecast

calculate forecasts

featherForecasts

calculate forecasts starting at different periods

horizonForecasts

calculate forecasts at different horizons

forecastCov

calculate the covariance of forecasts

MonteCarloSimulations

multiple simulations

The methods for evaluating estimation methods are:

EstEval

evaluate estimation methods

The functions described in the Brief User's Guide and examples in the help pages should work fairly reliably (since they are tested regularly), however, the code is distributed on an “as-is” basis. This is a compromise which allows me to make the software available with minimum effort. This software is not a commercial product. It is the by-product of ongoing research. Error reports, constructive suggestions, and comments are welcomed.

### Usage

library("dse")

library("EvalEst")

### References

Anderson, B. D. O. and Moore, J. B. (1979) Optimal Filtering. Prentice-Hall.

Gilbert, P. D. (1993) State space and ARMA models: An overview of the equivalence. Working paper 93-4, Bank of Canada. Available at http://www.bankofcanada.ca/1993/03/publications/research/working-paper-199/

Gilbert, P. D. (1995) Combining VAR Estimation and State Space Model Reduction for Simple Good Predictions. J. of Forecasting: Special Issue on VAR Modelling. 14:229–250.

Gilbert, P.D. (2000) A note on the computation of time series model roots. Applied Economics Letters, 7, 423–424

Jazwinski, A. H. (1970) Stochastic Processes and Filtering Theory. Academic Press.

TSdata, TSmodel, TSestModel.object