Abstract
Gaussian models of time series, ARMA, have been widely used in the literature. Benjamin et al. (J Am Stat Assoc 98:214–223, 2003) extended these models to the exponential family distributions. Also in that direction, Rocha and Cribari-Neto (Test 18:529–545, 2009) proposed a time series model for the class of beta distributions. In this paper, we develop an autoregressive and moving average symmetric model, named SYMARMA, which is a dynamic model for random variables belonging to the class of symmetric distributions including also a set of regressors. We discuss methods for parameter estimation, hypothesis testing and forecasting. In particular, we provide closed-form expressions for the score function and Fisher information matrix. Robust study is presented based on influence function. We conduct simulation studies to evaluate the consistency and asymptotic normality of the conditional maximum likelihood estimator for the model parameters. An application with real data is presented and discussed.
Similar content being viewed by others
Notes
The return is defined as \(r_t = (p_t - p_{t-1})/p_{t-1}\) where \(p_t\) is the price of an asset at time t.
The T-bill rates were divided by 100 to convert from a percentage and then by 253 to convert to a daily rate.
References
Benjamin MA, Rigby RA, Stasinopoulos M (2003) Generalized autoregressive moving avarege models. J Am Stat Assoc 98:214–223
Cao CZ, Lin JG, Zhu LX (2010) Heteroscedasticity and/or autocorrelation diagnostics in nonlinear models with AR(1) and symmetrical errors. Stat Pap 51:813–836
Chen C, Liu LM (1993) Joint Estimation of model parameters and outlier effects in time series. J Am Stat Assoc 88:284–297
Cox DR (1981) Statistical analysis of time series: some recent developments. Scand J Stat 8:93–115
Cox DR, Hinkley DV (1974) Theoretical statistics. Chapman and Hall, London
Creal D, Koopman SJ, Lucas A (2013) Generalized autoregressive score models with applications. J Appl Econom 28:777–795
Cysneiros FJA, Paula GA (2005) Restricted methods in symmetrical linear regression models. Comput Stat Data Anal 49:689–708
Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman and Hall, New York
Fang KT, Kotz S, Ng KW (1990) Symmetric multivariate and relates distributions. Chapman and Hall, London
Galea M, Paula GA, Uribe-Opazo M (2003) On influence diagnostic in univariate elliptical linear regression models. Stat Pap 44:23–45
Heyde CC, Feigin PD (1975) On efficiency and exponential families in stochastic process estimation. Stat Distrib Sci Work 1:227–240
Li WK (1994) Time series model based on generalized linear models: some further results. Biometrics 50:506–511
Ljung GM, Box GEP (1978) On a measure of a lack of fit in time series models. Biometrika 65:297–303
Lucas A (1997) Robustness of the Student-t based M-estimator. Commun Stat, Theory Methods 26:1165–1182
Paula GA, Cysneiros FJA (2009) Systematic risk estimation in symmetric models. Appl Econ 16:217–221
Paula GA, Leiva V, Barros M, Liu S (2012) Robust statistical modeling using Birnbaum-Saunders-\(t\) distribution applied to insurance. Appl Stoch Model Bus Ind 28:16–34
Peña D (1990) Influential observations in time series. J Bus Econ Stat 8:235–241
Ruppert D (2004) Statistics and finance. Springer, New York
R Core Team. R (2012) A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, ISBN: 3-900051-07-0, http://www.R-project.org
Rocha AV, Cribari-Neto F (2009) Beta autoregressive moving avarege models. Test 18:529–545
Zeger SL (1988) A regression model for time series of counts. Biometrics 75:621–629
Zeger SL, Qaqish B (1988) Markov regression models for time series: a quasi-likelihood approach. Biometrics 44:1019–1031
Acknowledgments
The authors thank the Editor, Dr. Victor Leiva, an anonymous Associate Editor and referees for their constructive comments on an earlier version of this manuscript, which resulted in this improved version. This research work was partially supported by a CNPq, CAPES and FACEPE agency from Brazil.
Author information
Authors and Affiliations
Corresponding author
Appendices
Appendix 1: Proof of Theorem 1
Proof
Let \(\Phi (B) = 1 - \phi _1B - \cdots - \phi _pB^p\) the autoregressive polynomial, \(\Theta (B) = 1 + \theta _1B + \cdots + \theta _qB^q\) the moving averages polynomial and \(B^ky_t=y_{t-k}\) the lag operator and \(\Psi (B) =\sum \nolimits _{i=0}^{\infty }\psi _iB^i=\Theta (B)\Phi (B)^{-1}\), \(\psi _0=1\) and assuming that \(\Phi (B)\) is invertible. The SYMARMA model can be rewritten as
and, since \(\Phi (B)\) is invertible,
Therefore, assuming that \(\Phi (B)\) is invertible, the marginal mean of \(y_t\) of the SYMARMA model is given by
\(\square \)
Appendix 2: Proof of Theorem 2
Proof
Let \(Y_t = \mu _t + r_t\) where \(r_t's\) are uncorrelated residuals with mean zero. We have
Note that \(\mu _t\) given in (2) is \(\mathcal{F}_{t-1}\)-measurable. Therefore, the marginal variance of \(Y_t\), \(\mathrm {Var}(Y_t)\), is given by
\(\square \)
Appendix 3: Proof of Theorem 3
Proof
By Theorem 1 and 2 we have
where \(\Psi _0=1\).
\(\square \)
Expected conditional Fisher information matrix
The elements of the expected conditional Fisher information matrix, \(\mathbf{K}\), are obtained from expression
where \(\omega _r\) and \(\omega _s\) are model parameters and \(\ell \) is the logarithm of the conditional likelihood function.
Under suitable regularity conditions
From some algebraic manipulations, we also obtain that
with \(z_t = \sqrt{u_t} = (y_t-\mu _t)/\sqrt{\varphi }\). Therefore, using the results in (10), we have
Furthermore, the expressions
are measurable with respect to \(\mathcal{F}_{t-1}\).
1.1 The \(\mathbf{K}_{{\varvec{\delta }}{\varvec{\delta }}}\) matrix elements
with \(d_{g} = \mathrm {E}\left[ W^2_g(u_t)z_t^2|\mathcal{F}_{t-1}\right] \). So, \(d_{g} = \mathrm {E}\left[ W^2_g(U^2)U^2|\mathcal{F}_{t-1}\right] \) with \(U \sim S(0,1,g)\).
From the results presented in (12) one can easily find the expressions for the elements of \(\mathbf{K}_{{\varvec{\delta }}{\varvec{\delta }}}\).
1.2 The \(\mathbf{K}_{\varphi \varphi }\) matrix elements
with \(f_{g} = \mathrm {E}\left[ W^2_g(u_t)u_t^2|\mathcal{F}_{t-1}\right] \). So, \(f_{g} = \mathrm {E}\left[ W^2_g(U^2)U^4|\mathcal{F}_{t-1}\right] \) with \(U \sim S(0,1,g)\). From Fang et al. (1990) (p. 94). we have \(\mathrm {E}\left[ W_g(u_t)u_t|\mathcal{F}_{t-1}\right] = -1/2\). Therefore,
1.3 \(\mathbf{K}_{{\varvec{\delta }}\varphi }\) matrix
From Fang et al. (1990) (p. 94) we have \(\mathrm {E}\left[ W^2_g(u_t)z_tu_t|\mathcal{F}_{t-1}\right] = 0\) and in addition, \(\mathrm {E}\left[ W_g(u_t)z_t|\mathcal{F}_{t-1}\right] =0\) because (11).
Rights and permissions
About this article
Cite this article
Maior, V.Q.S., Cysneiros, F.J.A. SYMARMA: a new dynamic model for temporal data on conditional symmetric distribution. Stat Papers 59, 75–97 (2018). https://doi.org/10.1007/s00362-016-0753-z
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00362-016-0753-z