A method for autoregressive-moving average estimation

Hannan, E. J.; Kavalieris, L.

doi:10.1093/biomet/71.2.273

Cited by 86 publications

(53 citation statements)

References 7 publications

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“…For the choice of n T data-dependent methods such as AIC or BIC (Hannan and Kavalieris, 1984) or deterministic rules such as 0.5 Koreisha et al, 1990) have been suggested. The estimated residuals are denoted byû…”

Section: Appendix C: Estimation Algorithms Two-stage Least Squaresmentioning

confidence: 99%

Business cycle analysis and VARMA models

Kascha¹,

Mertens

2009

Journal of Economic Dynamics and Control

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Section: Appendix C: Estimation Algorithms Two-stage Least Squaresmentioning

confidence: 99%

Business cycle analysis and VARMA models

Kascha¹,

Mertens

2009

Journal of Economic Dynamics and Control

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“…We adopt the Hannan-Kavalieris procedure as discussed in Hannan and Kavalieris (1984a), Hannan and Kavalieris (1984b), Lütkepohl and Poskitt (1996) and Lütkepohl (2007) to specify the optimal Kronecker indices. The Hannan-Kavalieris procedure consists of minimizing an information criterion denoted by C (p), given different alternative specifications of p. Because estimation of VARMA models is usually demanding in medium-and high-dimensional systems, we follow (Lütkepohl, 2007, pg.…”

Section: S 62 Hannan-kavalieris Proceduresmentioning

confidence: 99%

Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets

Dias

Kapetanios

2017

SSRN Journal

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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 strong and weak 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), BVAR and factor models, considering different model dimensions.JEL classification numbers: C13, C32, C53, C63, E0

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“…We use the three-step method proposed by Hannan and Rissanen (1982) and Hannan and Kavalieris (1984) for the purpose of identifying the ARMA orders and estimating the ARMA parameters. Since the datagenerating process (DGP) (2.2) involves the parameter λ, we need to modify the algorithm.…”

Section: Modifying the Box-cox Transformationmentioning

confidence: 99%

A Modified Box-Cox Transformation in the Multivariate ARMA Model

Terasaka

Hosoya

2007

JJSS

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The Box-Cox transformation has been used as a simple method of transforming dependent variable in ordinary-linear regression circumstances for improving the Gaussian-likelihood fit and making the disturbance terms of a model reasonably homoscedastic. The paper introduces a new version of the Box-Cox transformation and investigates how it works in terms of asymptotic performance and application, focusing in particular on inference on stationary multivariate ARMA models. The paper proposes a computational estimation procedure which extends the three-step Hannan and Rissanen method so as to accommodate the transformation and, for the purpose of parameter testing, the paper proposes a Monte-Carlo Wald test. The allied algorithm is applied to a bivariate series of the Tokyo stock-price index (Topix) and the call rate.

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A method for autoregressive-moving average estimation

Cited by 86 publications

References 7 publications

Business cycle analysis and VARMA models

Business cycle analysis and VARMA models

Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets

A Modified Box-Cox Transformation in the Multivariate ARMA Model

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