ARMA Model Identification

Choi, ByoungSeon

doi:10.1007/978-1-4613-9745-8

Cited by 210 publications

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“…A model with the smallest BIC is used as a MINIC recommendation. For a detailed treatment of the Hannan and Rissanen method, see Choi (1992), Hannan andRissanen (1982), andSAS (1999).…”

Section: Automated Methodsmentioning

confidence: 99%

“…The first recommendation is a model with a maximum triangular pattern. For a detailed description of the ESACF method, see Choi (1992), Tiao (1984,1990), and SAS Institute (1999).…”

Section: Automated Methodsmentioning

confidence: 99%

“…There are two major approaches for model identification: the traditional Box-Jenkins procedure and time series diagnostics by automated methods (Choi, 1992). The former consists in detailed inspection of empirical autocorrelation and partial autocorrelation functions (ACF and PACF) and in comparing their shape and value with theoretical ARIMA patterns.…”

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confidence: 99%

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Comparison of automated procedures for ARMA model identification

2008

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This article evaluates the performance of three automated procedures for ARMA model identification commonly available in current versions of SAS for Windows: MINIC, SCAN, and ESACF. Monte Carlo experiments with different model structures, parameter values, and sample sizes were used to compare the methods. On average, the procedures either correctly identified the simulated structures or selected parsimonious nearly equivalent mathematical representations in at least 60% of the trials conducted. For autoregressive models, MINIC achieved the best results. SCAN was superior to the other two procedures for mixed structures. For moving-average processes, ESACF obtained the most correct selections. For all three methods, model identification was less accurate for low dependency than for medium or high dependency processes. The effect of sample size was more pronounced for MIMIC than for SCAN and ESACF. SCAN and ESACF tended to select higherorder mixed structures in larger samples. These fmdings are confined to stationary nonseasonal time series.Time series analysis deals with repeated and equally spaced observations on a single unit. Classical statistical techniques are no longer appropriate here because data points cannot be assumed independent and uncorrelated. One of the most widely employed procedures for time series data is the autoregressive integrated moving-average (ARIMA) approach proposed by Box and Jenkins (1970). Since Glass, Willson, and Gottman (1975) introduced the ARIMA technique to the social and behavioral sciences, this methodology has been increasingly employed in different research fields (Delcor,

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“…A model with the smallest BIC is used as a MINIC recommendation. For a detailed treatment of the Hannan and Rissanen method, see Choi (1992), Hannan andRissanen (1982), andSAS (1999).…”

Section: Automated Methodsmentioning

confidence: 99%

“…The first recommendation is a model with a maximum triangular pattern. For a detailed description of the ESACF method, see Choi (1992), Tiao (1984,1990), and SAS Institute (1999).…”

Section: Automated Methodsmentioning

confidence: 99%

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Comparison of automated procedures for ARMA model identification

2008

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“…The problem of ARMA model specification has a long history, and we omit a literature review, though note the book by Choi (1992), dedicated to the topic. Less well documented is the use of small-sample distributional approximations using saddlepoint techniques.…”

Section: Introductionmentioning

confidence: 99%

Autoregressive Lag—Order Selection Using Conditional Saddlepoint Approximations

Butler

Paolella

2017

Econometrics

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Abstract:A new method for determining the lag order of the autoregressive polynomial in regression models with autocorrelated normal disturbances is proposed. It is based on a sequential testing procedure using conditional saddlepoint approximations and permits the desire for parsimony to be explicitly incorporated, unlike penalty-based model selection methods. Extensive simulation results indicate that the new method is usually competitive with, and often better than, common model selection methods.

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“…Subsequently, Schwarz [11] proposed the information criterion BIC. Since then, these criteria have been applied in such contexts as selecting models in Autoregressive (AR) and Autoregressive Moving Average (ARMA) process [12,13,14,15,16,17], estimating dependency order in multiple Markov chains [18,19,20], detecting change-points in non-homogeneous Markov chains [21], estimating the length of the hidden state space of a hidden Markov model [22], estimating order in Autoregressive Conditional Heteroskedasticity process (ARCH) [23] and on estimating dependency order in specific situations [24].…”

Section: Introductionmentioning

confidence: 99%

Model identification using the Efficient Determination Criterion

Resende

Dorea

2016

Journal of Multivariate Analysis

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In the realm of the model selection context, Akaike's and Schwarz's information criteria, AIC and BIC, have been applied successfully for decades for model order identification. The Efficient Determination Criterion (EDC) is a generalization of these criteria, proposed originally to define a strongly consistent class of estimators for the dependency order of a multiple Markov chain. In this work, the EDC is generalized to partially nested models, which encompass many other order identification problems. Based on some assumptions, a class of strongly consistent estimators is established in this general environment. This framework is applied to BEKK multivariate GARCH models and, in particular, the strong consistency of the order estimator based on BIC is established for these models.

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ARMA Model Identification

Cited by 210 publications

References 0 publications

Comparison of automated procedures for ARMA model identification

Comparison of automated procedures for ARMA model identification

Autoregressive Lag—Order Selection Using Conditional Saddlepoint Approximations

Model identification using the Efficient Determination Criterion

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