Time series AR(1) model for short-tailed distributions

Akkaya, Ayşen D.; Tiku, M. L.

doi:10.1080/02331880512331344036

Cited by 16 publications

(5 citation statements)

References 42 publications

(59 reference statements)

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“…Time series models based on generalized linear models (GLM) [11] are considered in [12,13,14,15]. Other important and recent works on non-Gaussian time series modeling are [16,17,3,18,19,20,4]. A comprehensive reference on general models for time series analysis is [21].…”

Section: Introductionmentioning

confidence: 99%

Beta seasonal autoregressive moving average models

Bayer

Cintra

Cribari‐Neto

2018

Journal of Statistical Computation and Simulation

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In this paper we introduce the class of beta seasonal autoregressive moving average (β SARMA) models for modeling and forecasting time series data that assume values in the standard unit interval. It generalizes the class of beta autoregressive moving average models [Rocha and Cribari-Neto, Test, 2009] by incorporating seasonal dynamics to the model dynamic structure. Besides introducing the new class of models, we develop parameter estimation, hypothesis testing inference, and diagnostic analysis tools. We also discuss out-of-sample forecasting. In particular, we provide closed-form expressions for the conditional score vector and for the conditional Fisher information matrix.We also evaluate the finite sample performances of conditional maximum likelihood estimators and white noise tests using Monte Carlo simulations. An empirical application is presented and discussed.Univariate time series modeling is commonly used in many fields. Most conventional time series models are based on the Gaussianity assumption [1]. A well-known class of this linear models is the class of autoregressive integrated moving average models (ARIMA) [2]. However, it has been recognized that the Gaussian assumption is too restrictive for many applications [3]. As a consequence, there has been increased interest in non-Gaussian time series models [4]. Some models for discrete variate time series are considered in [5,6,7]. In [8] is proposed a quasi-likelihood approach to regression models for discrete and continuous time series. In [9] is focused on time series modeling under non-Gaussian innovations. Non-Gaussian time series models are considered as instantaneous transformations of Gaussian time series in [10,1]. Time series models based on generalized linear models (GLM) [11] are considered in [12,13,14,15]. Other important and recent works on non-Gaussian time series modeling are [16,17,3,18,19,20,4]. A comprehensive reference on general models for time series analysis is [21]. * F. M. Bayer is with the

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Section: Introductionmentioning

confidence: 99%

Beta seasonal autoregressive moving average models

Bayer

Cintra

Cribari‐Neto

2018

Journal of Statistical Computation and Simulation

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“…Moreover, for sample models like Equation (9), MMLEs and other estimators based on censored samples have lower efficiencies than MMLEs based on complete samples as expected [25]; see also [31] who enunciates five very desirable properties of MMLEs (based on complete samples) including their efficiency. See also [13,18,32,33], for the efficiency and robustness properties of MMLEs based on censored samples.…”

Section: Commentmentioning

confidence: 99%

Mahalanobis distance under non-normality

2010

Self Cite

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“…Differentiating Equation (3) with respect to , and˛and setting the derivatives equal to zero gives the following equations (Akkaya and Tiku, 2005):…”

Section: Parameter Estimation Procedures With Modified Maximum Likelihoodmentioning

confidence: 99%

Annual streamflow modelling with asymmetric distribution function

Şarlak¹

2008

Hydrological Processes

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Abstract:Classical autoregressive models (AR) have been used for forecasting streamflow data in spite of restrictive assumptions, such as the normality assumption for innovations. The main reason for making this assumption is the difficulties faced in finding model parameters for non-normal distribution functions. However, the modified maximum likelihood (MML) procedure used for estimating autoregressive model parameters assumes a non-normally distributed residual series. The aim in this study is to compare the performance of the AR(1) model with asymmetric innovations with that of the classical autoregressive model for hydrological annual data. The models considered are applied to annual streamflow data obtained from two streamflow gauging stations in Kızılırmak Basin, Turkey.

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Time series AR(1) model for short-tailed distributions

Cited by 16 publications

References 42 publications

Beta seasonal autoregressive moving average models

Beta seasonal autoregressive moving average models

Mahalanobis distance under non-normality

Annual streamflow modelling with asymmetric distribution function

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