Testing causality using efficiently parametrized vector ARMA models
type: Item
instance of: scholarly article
title: Testing causality using efficiently parametrized vector ARMA models
zbMATH Open document ID: 0609.62125
DOI: 10.1016/0096-3003(86)90010-X
author: Paul Newbold
author: Steven M. Hotopp
published in: Applied Mathematics and Computation
publication date: 1986-01-01T00:00:00Z
review text: We outline a practical methodology for fitting to data vector autoregressive moving-average models, and employ these models to test for Granger causality. The procedures are applied to data on employment and hourly wages in manufacturing in the United States.
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 62P20
zbMATH DE Number: 3986478
zbMATH Keywords: model-building methodology
zbMATH Keywords: model selection
zbMATH Keywords: parameter estimation
zbMATH Keywords: model checking
zbMATH Keywords: economic time series
zbMATH Keywords: vector autoregressive moving-average models
zbMATH Keywords: test for Granger causality
MaRDI profile type: Publication
cites work: Fitting autoregressive models for prediction
cites work: Q4170056
cites work: Exact likelihood of vector autoregressive-moving average process with missing or aggregated data
cites work: Finite sample properties of estimators for autoregressive moving average models
cites work: Q5841817
cites work: Q3896388
cites work: Q3847819
cites work: Q4101268
cites work: The Fitting of Time-Series Models
cites work: Comparing alternative tests of causality in temporal systems. Analytic results and experimental evidence
cites work: Testing the adequacy of a time series model
cites work: Investigating Causal Relations by Econometric Models and Cross-spectral Methods
cites work: Q4113288
cites work: Q4166090
cites work: Q5612941
cites work: A method for autoregressive-moving average estimation
cites work: Recursive estimation of mixed autoregressive-moving average order
cites work: Likelihood Function of Stationary Multiple Autoregressive Moving Average Models
cites work: Q3868643
cites work: The Multivariate Portmanteau Statistic
cites work: Q3928869
cites work: Q3925035
cites work: SOME ASPECTS OF MODELLING AND FORECASTING MULTIVARIATE TIME SERIES
cites work: NECESSARY AND SUFFICIENT CONDITIONS FOR CAUSALITY TESTING IN MULTIVARIATE ARMA MODELS
cites work: Q4742198
cites work: The equivalence of two tests of time series model adequacy
cites work: The exact likelihood function of multivariate autoregressive-moving average models
cites work: Testing the specification of a fitted autoregressive-moving average model
cites work: Diagnostic tests for multiple time series models
cites work: The effect of transformations of variables upon their correlation coefficients
cites work: Q5791397
cites work: Modeling by shortest data description
cites work: Estimating the dimension of a model
cites work: The Lagrangian Multiplier Test
cites work: Modeling Multiple Times Series with Applications
cites work: Multiple Time Series Analysis and the Final Form of Econometric Models
cites work: Q5674824
cites work: On the Relationship Between the S Array and the Box-Jenkins Method of ARMA Model Identification
description: scientific article; zbMATH DE number 3986478