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dse (version 2009.12-1)

Dynamic Systems Estimation (time series package)

Description

Package dse provides tools for multivariate, linear, time-invariant, time series models. It includes ARMA and state-space representations, and methods for converting between them. It also includes simulation methods and several estimation functions. The package has functions for looking at model roots, stability, and forecasts at different horizons. The ARMA model representaion is general, so that VAR, VARX, ARIMA, ARMAX, ARIMAX can all be considered to be special cases. Kalman filter and smoother estimates can be obtained from the state space model, and state-space model reduction techniques are implemented. An introduction and User's Guide is available in a vignette.

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Version

Install

install.packages('dse')

Monthly Downloads

142

Version

2009.12-1

License

GPL-2 | file LICENSE

Maintainer

Paul Gilbert

Last Published

January 20th, 2011

Functions in dse (2009.12-1)

Estimate a TSmodel

Polynomial Utilities

Calculate Portmanteau statistic

Estimate a State Space Model

informationTestsCalculations

Calculate selection criteria

Calculate McMillan Degree

MittnikReducedModels

Reduced Models via Mittnik SVD balancing

egJofF.1dec93.data

Eleven Time Series used in Gilbert (1995)

Construct TSdata time series object

Estimate a state space TSmodel using VAR estimation

Estimate a VAR TSmodel

Four Time Series used in Gilbert (1993)

estimatorsHorizonForecastsWRTdata

Estimate models and forecast at given horizons

combine.forecastCov

Combine 2 Forecast Cov Objects

Estimate a TSmodel

minimumStartupLag

Starting Periods Required

Forecast covariance for different models

Estimate a VAR TSmodel

Estimated Time Series Model

Dynamic Systems Estimation - Multivariate Time Series Package

Set SS Model F Matrix to Constants

forecastCovWRTtrue

Compare Forecasts to True Model Output

Estimate Models

Riccati Equation

Extract or set Model Parameters

forecastCovEstimatorsWRTdata

Calculate Forecast Cov of Estimators WRT Data

horizonForecasts

Calculate forecasts at specified horizons

TSdata.forecastCov

TS Extractor Specific Methods

Calculate sum of squared prediction errors

Estimate a TSmodel

Simulate a TSmodel

informationTests

Tabulates selection criteria

roots.estimatedModels

Roots Specific Methods

Generate a random time series

Dynamic Systems Estimation - Multivariate Time Series Package

testEqual.forecast

Specific Methods for Testing Equality

Calculate the acf for an object

forecastCovCompiled

Forecast covariance for different models - internal

Print Version Information

nseries.featherForecasts

Number of Series

Check Balance of a TSmodel

seriesNamesInput

TSdata Series Names

Evaluate an ARMA TSmodel

Calculate Model Roots

Select Best Model

setTSmodelParameters

Set TSmodel Parameter Information

forecastCovEstimatorsWRTtrue

Compare Forecasts Cov Relative to True Model Output

residuals.TSestModel

Calculate the residuals for an object

Estimate a TSmodel

tfplot.forecast

Specific Methods for tfplot

Basis Transformation of a Model.

Time Series Models

seriesNamesInput.forecast

TS Input and Output Specific Methods

Maximum Likelihood Estimation

Combine series from two TSdata objects.

Set TSmodel Array Information

Calculate Model Observability Matrix

MittnikReduction

Balance and Reduce a Model

State Space Models

Weighted Estimation

Evaluate a TSmodel

time series data object

Combine two objects.

Calculate Phase Plots

Convert to State Space Innovations Model

horizonForecastsCompiled

Calculate forecasts at specified horizons

ARMA Model Constructor

featherForecasts

Multiple Horizon-Step Ahead Forecasts

print.TSestModel

Display TSmodel Arrays

is.forecastCovEstimatorsWRTdata.subsets

Check Inheritance

Specific Methods for tframed Data

Forecast Multiple Steps Ahead

Estimate a TSmodel

Minimum Forecast Cov Models

percentChange.TSdata

Calculate percent change

Specific Methods for Testing Equality

Number of Series in in Input or Output

excludeForecastCov

Filter Object to Remove Forecasts

Convert to Oform

Tfplot Specific Methods

totalForecastCov

Sum covariance of forecasts across all series

Specific Methods for tframed Data

Convert to Non-Innovation State Space Model

Scale Methods for TS objects

Calculate Model Reachability Matrix

Calculate Residuals Statistics and Likelihood

Markov Parameters

seriesNames.TSdata

Series Names Specific Methods

print.forecastCov

Print Specific Methods

summary.forecastCov

Summary Specific Methods

tfplot.forecastCov

Plots of Forecast Variance

extractforecastCov

Extract Forecast Covariance

Convert to State Space Model

selectForecastCov

Select Forecast Covariances Meeting Criteria

Evaluate a smoother with a TSmodel

Print Specific Methods

Flags to Indicate Use of Compiled Code

Calculate Stability of a TSmodel

outOfSample.forecastCovEstimatorsWRTdata

Calculate Out-of-Sample Forecasts

Specific Methods for Summary

checkBalanceMittnik

Check Balance of a TSmodel

checkConsistentDimensions

Check Consistent Dimensions

Fix TSmodel Coefficients (Parameters) to Constants

Evaluate a state space TSmodel

State Dimension of a State Space Model

Balance a state space model

Add Model Roots to a plot

forecastCovReductionsWRTtrue

Forecast covariance for different models

Extract Forecasts

Plot Model Roots

Select a Data Subset and Model

Autocorrelations Diagnostics

shockDecomposition

Shock Decomposition

Convert to an ARMA Model