Package com.imsl.test.example.stat

Class ARAutoUnivariateEx2

java.lang.Object

com.imsl.test.example.stat.ARAutoUnivariateEx2

public class ARAutoUnivariateEx2
extends Object

Finds the minimum AIC autoregressive model for the Canadian lynx data.

Using the Canadian Lynx data included in TIMSAC-78, ARAutoUnivariate is used to find the minimum AIC autoregressive model using a maximum number of lags of maxlag=20.

This example compares the three different methods for estimating the autoregressive coefficients, and it illustrates the relationship between these estimates and those calculated within the TIMSAC UNIMAR code. As illustrated, the UNIMAR code estimates the coefficients and innovation variance using only the last \(N\)-maxlag values in the time series. The other estimation methods use all \(N-k\) values, where \(k\) is the number of lags with minimum AIC selected by ARAutoUnivariate.

This example also illustrates how to generate forecasts for the observed series values and beyond by setting the backward origin for the forecasts.

See Also:

• Code
• Output

Constructor Summary

Constructors

Constructor	Description
ARAutoUnivariateEx2()

Method Summary

Modifier and Type	Method	Description
static void	main(String[] args)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

ARAutoUnivariateEx2

public ARAutoUnivariateEx2()

Method Details

main

public static void main(String[] args)
                 throws Exception

Throws:

Exception