dse-package: Dynamic Systems Estimation - Multivariate Time Series Package
In dse: Dynamic Systems Estimation (Time Series Package)

Description Details Usage References See Also

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

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.

library("dse")

library("EvalEst")

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

dse documentation built on March 26, 2020, 7:12 p.m.