Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets PDF Download
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Author: Gustavo Fruet Dias
Publisher:
ISBN:
Category :
Languages : en
Pages : 40
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Book Description
We address the issue of modelling and forecasting macroeconomic variables using rich datasets by adopting the class of Vector Autoregressive Moving Average (VARMA) models. We overcome the estimation issue that arises with this class of models by implementing an iterative ordinary least squares (IOLS) estimator. We establish the consistency and asymptotic distribution of the estimator for weak and strong VARMA(p,q) models. Monte Carlo results show that IOLS is consistent and feasible for large systems, outperforming the MLE and other linear regression based efficient estimators under alternative scenarios. Our empirical application shows that VARMA models are feasible alternatives when forecasting with many predictors. We show that VARMA models outperform the AR(1), ARMA(1,1), Bayesian VAR, and factor models, considering different model dimensions.Supplement is available at: 'https://ssrn.com/abstract=2830838' https://ssrn.com/abstract=2830838.
Author: Gustavo Fruet Dias
Publisher:
ISBN:
Category :
Languages : en
Pages : 40
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Book Description
We address the issue of modelling and forecasting macroeconomic variables using rich datasets by adopting the class of Vector Autoregressive Moving Average (VARMA) models. We overcome the estimation issue that arises with this class of models by implementing an iterative ordinary least squares (IOLS) estimator. We establish the consistency and asymptotic distribution of the estimator for weak and strong VARMA(p,q) models. Monte Carlo results show that IOLS is consistent and feasible for large systems, outperforming the MLE and other linear regression based efficient estimators under alternative scenarios. Our empirical application shows that VARMA models are feasible alternatives when forecasting with many predictors. We show that VARMA models outperform the AR(1), ARMA(1,1), Bayesian VAR, and factor models, considering different model dimensions.Supplement is available at: 'https://ssrn.com/abstract=2830838' https://ssrn.com/abstract=2830838.
Author: Gustavo Fruet Dias
Publisher:
ISBN:
Category :
Languages : en
Pages : 64
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Book Description
The online Supplement presents the proof the auxiliary Lemmas 1-6, the entire set of tables with results from the Monte Carlo and the empirical studies, and further discussion on selected topics.Full paper is available at: 'https://ssrn.com/abstract=2707176' https://ssrn.com/abstract=2707176.
Author: Ken Hung
Publisher:
ISBN:
Category : Autoregression (Statistics)
Languages : en
Pages : 372
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Book Description
Author: Ho-Pun Lam
Publisher: Springer Nature
ISBN: 3030322424
Category : Computers
Languages : en
Pages : 190
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Book Description
This book constitutes revised selected papers from the Australasian Symposium on Service Research and Innovation, ASSRI 2018. The conference was held in two parts on September 6, 2018, in Sydney, Australia, and on December 14, 2018, in Wollongong, Australia. The 9 full and 2 short papers included in this volume were carefully reviewed and selected from a total of 26 submissions, covering a variety of topics related to service-oriented computing and service science. The book also includes 3 keynote papers.
Author: Christian Kascha
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: STANFORD UNIV CALIF DEPT OF STATISTICS.
Publisher:
ISBN:
Category :
Languages : en
Pages : 18
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Book Description
The vector autoregressive moving average model is a multivariate stationary stochastic process where the unobservable multivariate process consists of independently identically distributed random vectors. The coefficient matrices and the covariance matrix are to be estimated from an observed sequence. Under the assumption of normality the method of maximum likelihood is applied to likelihoods suitably modified for techniques in the frequency and time domains. Newton-Raphson and scoring iterative methods are presented.
Author: Fereydoon Ahrabi
Publisher:
ISBN:
Category : Autocorrelation (Statistics)
Languages : en
Pages : 192
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Book Description
The purpose of this paper is to derive asymptotically efficient estimates for the autoregressive matrix coefficients and moving average covariance matrices of the vector autoregressive moving average (VARMA) models in both time and frequency domains. To do this we shall apply the Newton-Raphson and scoring methods to the maximum likelihood equations derived from modified likelihood functions under the Gaussian Assumption.
Author: Greg Reinsel
Publisher:
ISBN:
Category :
Languages : en
Pages : 25
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Book Description
A method is presented for the estimation of the parameters in the vector autoregressive moving average time series model. The estimation procedure is derived from the maximum likelihood approach and is based on Newton-Raphson techniques applied to the likelihood equations. The resulting two-step Newton-Raphson procedure is computationally simple, involving only generalized least squares estimation in the second step. This Newton-Raphson estimator is shown to be asymptotically efficient and to possess a limiting multivariate normal distribution. (Author).
Author: George Athanasopoulos
Publisher:
ISBN:
Category : Autoregression (Statistics)
Languages : en
Pages : 470
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Book Description
Author: Efstathios Paparoditis
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
A new procedure for testing the fit of multivariate time series model is proposed. The method evaluates in a certain way the closeness of the sample spectral density matrix of the observed process to the spectral density matrix of the parametric model postulated under the null and uses for this purpose nonparametric estimation techniques. The asymptotic distribution of the test statistic is established and an alternative, bootstrap-based method is developed in order to estimate more accurately this distribution under the null hypothesis. Goodness-of-fit diagnostics useful in understanding the test results and identifying sources of model inadequacy are introduced. The applicability of the testing procedure and its capability to detect lacks of fit is demonstrated by means of some real data examples.
