Learn R Programming

portes (version 6.0)

Portmanteau Tests for Time Series Models

Description

Contains common univariate and multivariate portmanteau test statistics for time series models. These tests are based on using asymptotic distributions such as chi-square distribution and based on using the Monte Carlo significance tests. Also, it can be used to simulate from univariate and multivariate seasonal time series models.

Copy Link

Version

Install

install.packages('portes')

Monthly Downloads

609

Version

6.0

License

GPL (>= 2)

Maintainer

Last Published

June 18th, 2023

Functions in portes (6.0)

Simulate Data From Seasonal/Nonseasonal ARIMA(p,d,q)*(ps,ds,qs)_s or VARIMA(p,d,q)*(ps,ds,qs)_s Models

The Monthly Log Stock Returns of Intel Corporation from January 1973 to December 2003

Toeplitz Block Matrix of Hosking (1980) Auto and Cross Correlation Matrices

Quarterly, West German Investment, Income, and Consumption: 1960Q1-1982Q4

Compute The Vector of Moving Average Model (VMA)

Generalized Variance Portmanteau Test

Ljung and Box Portmanteau Test

Portmanteau Tests for Time Series Models

The Modified Multivariate Portmanteau Test, Li-McLeod (1981)

Portmanteau Test Statistics

The Univariate-Multivariate Box and Pierce Portmanteau Test

The Modified Multivariate Portmanteau Test, Hosking (1980)

Monthly Returns of IBM and S&P 500 Index

Canada/US Foreign Exchanges Rates, Daily, Jan. 4, 1971 to Sept. 5, 1996.

Quarterly U.K. economic time series from 1957 Q3 to 1967 Q4

Monthly simple returns of the CRSP value-weighted index, 1926 to 1997

The Impulse Response Function in the Infinite MA or VMA Representation

Check Stationary and Invertibility of ARMA or VARMA Models

GNP Deflator for U.S. Inflation Data from January 01, 1947 to April 01, 2010.

Extract Residuals from ARIMA, VAR, or any Simulated Fitted Time Series Model