dse-package {dse1}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 the dse bundle in the directory ‘dse/dse1/inst/doc/dse-guide.pdf’. The package implements an R/S style object oriented 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 dse1. Package dse2 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.

Bundle: dse
Contains: tframe dse1 dse2
Depends: R, setRNG, tframe
License: free, see LICENSE file for details.
URL: http://www.bank-banque-canada.ca/pgilbert

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("dse1")

library("dse2")

Author(s)

Paul Gilbert <pgilbert@bank-banque-canada.ca>

Maintainer: Paul Gilbert <pgilbert@bank-banque-canada.ca>

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 www.bank-banque-canada.ca/pgilbert

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.

See Also

TSdata, TSmodel, TSestModel.object


[Package dse1 version 2008.10-1 Index]