dse-package: Dynamic Systems Estimation - Multivariate Time Series Package

Description

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

Arguments

Usage

library("dse")

library("EvalEst")

concept

DSE

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.

ll{ 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: [object Object],[object Object],[object Object]

The main general methods are: [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

The main estimation methods are: [object Object],[object Object],[object Object],[object Object],[object Object]

The main diagnositic methods are: [object Object],[object Object],[object Object],[object Object],[object Object]

The methods for producing and evaluating forecasts are: [object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

The methods for evaluating estimation methods are: [object Object]

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.

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.